Skills

Structures CNN reporting on June 30, 2026 that the US government lifted export controls on Anthropic's Claude Fable 5 and Mythos 5 into a policy, security, and release-governance checklist. The workflow separates verified facts—Anthropic said Commerce lifted export controls on Fable 5 and Mythos 5 and would begin restoring access; Commerce Secretary Howard Lutnick posted on X about lifting controls on Fable after two weeks working with Anthropic to analyze and approve Fable 5; Fable is Mythos with extra public guardrails; earlier export ban followed Amazon finding a jailbreak (Anthropic called jailbreaks simple and noted similar work-arounds on other public models); Anthropic implemented a new safeguard blocking reported vulnerabilities; Commerce had required suspending all foreign-national use including Anthropic employees; Mythos was later released to select government-approved entities; CNN notes experts say Mythos can exploit cyber vulnerabilities at unprecedented pace; White House also asked OpenAI to limit GPT 5.6 release to government-approved partners—from internal frontier-model access planning. Distinct from June ban-imposition reporting tracked by anthropic-mythos-export-control-due-diligence.

Structures AI News reporting on June 24, 2026 about Samsung Electronics expanding employee access to ChatGPT Enterprise and Codex into a security, procurement, and workforce-governance checklist. The workflow separates verified facts—OpenAI said deployment covers all Samsung Electronics employees in Korea and all Device eXperience employees worldwide; Samsung plans use across software development, marketing, product development, manufacturing, and other functions for search, drafting, idea development, data interpretation, and code work; rollout follows 2023 restrictions after sensitive internal information was uploaded to external AI; new access uses ChatGPT Enterprise with data protection, user access, and security controls; Codex supports code write/review/debug plus internal tools, websites, prototypes, and automated workflows; OpenAI said Codex has 5M+ weekly users and Korea Codex WAU grew nearly 800% since Feb 1, 2026; Harrison Kim (OpenAI Korea GM) called it one of OpenAI's largest enterprise deployments; October 2025 Samsung memory partnership for Stargate and Samsung SDS reseller/consulting links cited—from internal rollout decisions. AI News also cites Deloitte 66% productivity gains and 53% improved insights from enterprise AI adoption surveys.

Structures Reuters-via-Yahoo Tech reporting on June 24, 2026 about OpenAI and Broadcom's Jalapeño custom inference chip into an infrastructure, finance, and procurement checklist. The workflow separates verified facts—OpenAI showed its first custom AI chip designed with Broadcom for inference; Broadcom CEO Hock Tan told Reuters the chip is as good as Nvidia Blackwell or Google TPUs; hardware chief Richard Ho said Jalapeño is designed for LLM inference and future LLM iterations; deployment planned by end of 2026 as first step in multi-generation plan; Celestica builds server systems for OpenAI-only use; lab samples run at target power/performance with GPT-5.3-Codex-Spark; ~nine-month design cycle to TSMC manufacturing with AI assisting design; Tan noted custom AI chip margins pressured by HBM demand with SK Hynix and Samsung supplying memory—from internal capacity planning. Reuters notes OpenAI exploring chips since 2023 and Anthropic weighing its own chip per April reporting.

Turns CNBC reporting on June 24, 2026 about Anthropic's Senate Banking Committee letter into a security, legal, and policy checklist. The workflow separates verified facts—Anthropic accused Alibaba of brazenly and illicitly attempting to extract AI capabilities; the June 10 letter to Sens. Tim Scott and Elizabeth Warren called it the largest known distillation attack on Anthropic to date; operators affiliated with Alibaba and its AI lab carried out 28.8 million exchanges using roughly 25,000 fraudulent accounts between April 22 and June 5; distillation trains a smaller model on outputs of a stronger one; Anthropic wrote Alibaba ignored Trump Administration warnings after a White House OSTP memorandum on industrial-scale distillation; February blog cited DeepSeek, Moonshot, and MiniMax campaigns; recent weeks complicated by export-control directive on Fable 5 and Mythos 5—from internal response decisions. CNBC notes Alibaba did not immediately respond and Bloomberg was first to report the letter.

Structures CNN reporting on June 23, 2026 about a rare Five Eyes joint statement into a security, legal, and executive-readiness checklist. The workflow separates verified alliance facts—that the US, UK, Canada, Australia, and New Zealand intelligence grouping warned frontier AI models capable of major cyberattacks overwhelming government and business defenses are months not years away; the statement on Monday said frontier AI models are anticipated to exceed current industry expectations, fundamentally transforming offensive and defensive cyber capabilities with a timeline of months; leaders were urged to act now by investing in cyber defenses, upgrading old systems, patching faulty software, and limiting access to critical systems; organizations integrating AI into security operations can detect vulnerabilities earlier, improve software quality, monitor unusual behaviour, and respond faster—from internal control decisions. It references CNN context that the warning follows the Trump administration ordering Anthropic to suspend foreign-national use of its most advanced models and notes there is currently no transparent, consistent US AI regulation framework.

Turns AI News reporting on June 19, 2026 about SAP and Google Cloud deploying agentic commerce architecture into a data, retail-ops, and procurement checklist. The workflow separates verified partnership facts—SAP research cited that 78% of businesses consider AI essential for retaining customers in 2026 while fewer than two in five share customer data across CX (37%) or CRM (39%) platforms; SAP Commerce Cloud adopting Universal Commerce Protocol; SAP Business Data Cloud Connect for Google BigQuery with bidirectional zero-copy linking; SAP Engagement Cloud multi-agent framework; Google Gemini including Nano Banana 2 for localized messaging and Google Rich Communication Services; Shopping Assistant with live inventory checks—from internal rollout decisions your org must still make. It references AI News that the architecture lets agents execute full retail sequences via UCP, merchants retain customer relationships in third-party channels, and marketing teams set business goals rather than manual campaign execution.

Turns Reuters-via-Yahoo Tech reporting on OpenAI's June 18, 2026 ChatGPT Enterprise analytics and spend-control launch into a finance, IT, and procurement checklist. The workflow separates verified product facts—global admin console visibility for ChatGPT and Codex credits, per-user/product/model breakdowns, usage trends, top users, workspace default credit limits, group limits with individual overrides, employee self-service usage views and credit requests, availability starting Thursday—from internal policy decisions your org must still make. It references Yahoo Tech (Reuters) that growing enterprise adoption by power users has drawn attention to escalating AI consumption costs and that OpenAI framed the release as helping manage costs and track credit usage.

Structures BBC reporting on June 17, 2026 about British AI security startup Mindgard red-teaming ChatGPT image generation into a safety, legal, and release-governance checklist. The workflow separates verified facts—Mindgard altered a widely shared humorous prompt so the latest public ChatGPT (GPT-5.4) generated sexualised or graphically violent images; founder Peter Garraghan (Lancaster University professor) said outputs were gruesome and sometimes sexualised without the prompt specifying subjects; researcher Jim Nightingale reported being shaken by results; BBC saw examples including titles like Grim crime scene aftermath and abandoned in fear and restraint; Mindgard first alerted OpenAI in May and received only an automated response before a partial block that was circumvented; OpenAI told BBC after contact it added safeguards and has layered image protections, automated systems, human review, and policies banning sexual violence, non-consensual intimate content, CSAM, and bypass attempts; Mindgard said small prompt changes still produced concerning content and prior research showed deepfake swaps remained possible; expert Rumman Chowdhury (Humane Intelligence) noted models lack human intent understanding; UK AI Security Institute previously found jailbreaks across tested systems; DSIT said safeguards are improving but more work remains—from internal image-model release decisions.

Structures verification of frontier-model export-control headlines into a legal, security, and product-access checklist. The workflow separates Commerce Department directives from Anthropic compliance statements, maps Mythos versus Fable access changes, and tracks licensing language without inferring undisclosed national-security details. It references CNN reporting on June 13, 2026 that Anthropic disabled customer access to its most capable systems after the US government ordered it to suspend all use by foreign nationals of Mythos 5 and Fable 5 over national security concerns about cybersecurity vulnerabilities; CNN said Anthropic complied by removing access for everyone because it could not filter users by nationality in real time; the government did not provide specific national-security details though Anthropic believed officials became aware of a Fable 5 jailbreak demonstrating relatively minor, previously known vulnerabilities other public models can also find; Anthropic disputed that a narrow jailbreak should recall a commercial model deployed to hundreds of millions and argued applying the standard industry-wide would halt frontier deployments; CNN cited Axios that Commerce would require licenses for export, re-export, or domestic transfer; the piece notes Mythos capabilities spooked government and Wall Street, Fable 5 shipped last week as a safer public version, a recent executive order asks companies to share advanced cyber-capable models with government up to 30 days before other partners, and earlier supply-chain-risk designation and lawsuit context with continued White House contact.

Structures verification of frontier-LLM pricing headlines into a finance and procurement checklist. The workflow separates reported price-cut discussions from confirmed public rate cards, maps token-billing impacts to gross-margin assumptions, and tracks IPO-timing context without treating leaks as finalized pricing. It references The Wall Street Journal reporting on June 11, 2026 that OpenAI is considering drastically lowering prices charged for tokens—the unit AI firms use to bill products—in anticipation of similar cuts the company expects at Anthropic, according to people familiar with the matter; WSJ notes discussions are still in flux; both companies' business models are under scrutiny ahead of hotly anticipated IPOs; OpenAI confidentially filed for an IPO earlier that week following Anthropic's filing, and CEO Sam Altman told employees in a recent Slack message the company plans to go public within the next year (as earlier reported by the Information). WSJ framed the move as OpenAI seeking to win customers from rival Anthropic amid an expected token-pricing competition.

Structures verification of EU generative-AI transparency headlines into a compliance readiness checklist for providers and deployers. The workflow separates voluntary Code of Practice signing from mandatory Article 50 obligations, maps provider versus deployer labelling duties, and tracks pending Commission guidelines. It references AI News reporting on June 16, 2026 that the European Commission released a final voluntary Code of Practice on 10 June ahead of Article 50 transparency rules applying from August 2, 2026; the Code is optional but the obligations are not; from August, deepfakes and AI-generated or AI-manipulated text on matters of public interest must carry labels, and interactive AI systems such as customer-service bots must disclose machine interaction; Executive Vice-President Henna Virkkunen is quoted that Europeans have a right to know whether content was made or altered by AI; providers should mark output in machine-readable format while deployers handle visible labelling when public-interest text goes out without human review; the Code uses open technical standards and a common EU icon; it was drawn up by six independent experts with input from more than 180 stakeholders and still awaits Commission and AI Board adequacy judgment plus separate guidelines for gaps AI News says remain unpublished with under two months before enforcement.

Structures verification of AI-infrastructure debt headlines into a treasury and investor-relations checklist. The workflow separates SEC filing facts from unnamed source sizing, compares new issuance to existing debt stacks and prior raises, and maps proceeds language to refinancing versus buyback narratives. It references CNBC reporting on June 15, 2026 that Nvidia disclosed plans in an SEC filing for its first investment-grade corporate bond sale since 2021, with sources telling CNBC the chipmaker is aiming to raise at least $20 billion (possibly closer to $25 billion) in its first bond sale since the AI boom began; CNBC notes Nvidia shares rose 3.5% Monday and are up about 14% year-to-date; the piece situates Nvidia alongside Alphabet ($85 billion equity-related plans plus $55 billion+ debt since November), Super Micro ($7 billion equity-related financing), and Amazon (~$54 billion U.S./European debt plus ~$10 billion Canadian sale plans); CNBC cites Nvidia's ~$7.5 billion long-term and ~$1 billion short-term debt, its $5 billion 2021 raise with notes maturing as late as 2031, revenue growth from ~$27 billion in fiscal 2022 to $216 billion in fiscal 2026, ChatGPT's late-2022 catalyst for GPU demand, a spokesperson saying proceeds are for general corporate purposes including repayment/refinancing of existing debt, and May dividend/buyback moves ($0.25 dividend, $80 billion repurchase plan, ~50% of free cash flow return target, $49 billion quarterly free cash flow).

Structures verification of AI-and-jobs labor headlines into a workforce planning checklist. The workflow separates one-month JOLTS spikes from hiring/quit trends, industry composition, and economist quotes about AI displacement narratives. It references CNN reporting on June 2, 2026 that US job openings rose to 7.62 million in April—the highest since mid-2024—from 6.89 million in March; hiring and layoffs both fell after March spikes; voluntary quits hit their lowest level in nearly six years; more than 90% of April's opening increase was in professional and business services; for the first time since June 2025 there were more openings than job seekers; American Staffing Association chief economist Noah Yosif told CNN April data could push back on the narrative that artificial intelligence will be the "great job-killer" while responsibilities shift as technologies permeate the labor market; CNN also notes monthly volatility, revision risk, Iran-war oil uncertainty, and Heather Long/Navy Federal and Bill Adams/Fifth Third caution against overweighting a single report—without treating one JOLTS print as proof AI is boosting junior hiring.

Structures verification of platform-assistant launch headlines into a product, legal, and compliance checklist. The workflow separates announced capabilities from regional availability gaps, third-party model dependencies, and regulator disputes cited in trade press. It references Yahoo Tech reporting around WWDC on June 8, 2026 that Apple will roll out a Siri AI beta later in 2026 based on Google's Gemini models with conversational, cross-app integration across iPhone, iPad, Mac, Watch, Vision Pro, CarPlay, and AirPods; Yahoo Tech notes Siri AI will not ship on iPhone, iPad, or Apple Watch in the European Union because of the Digital Markets Act, though macOS 27 and visionOS 27 users in the EU can access it, and EU watchOS 27 users will lack Siri AI because it requires a paired iPhone with Siri AI; Craig Federighi is quoted saying regulators' "refusal to engage constructively" leaves no timeline for iOS/iPadOS EU availability; Apple also says regulatory issues in China must be resolved first; supported devices include iPhone 17/16 series and iPhone 15 Pro models, iPad with M4+, and Macs with M3+ per the piece—without assuming global feature parity in roadmaps or contracts.

Structures verification of Mythos-class model launch headlines into a security and procurement checklist. The workflow separates publicly available Claude Fable 5 safeguards from restricted Claude Mythos 5 trusted-access tiers, pricing, data-retention policy changes, and marketing rhetoric about capability. It references BBC reporting on June 10, 2026 that Anthropic released Claude Fable 5—a public version of Claude Mythos previewed privately in April—quoting Anthropic: "Fable's capabilities exceed those of any model we've ever made generally available" and "releasing a model this capable comes with risks"; BBC said roughly 150 preview groups gain Claude Mythos 5 with fewer cybersecurity/biology limits for approved uses, preview users reported finding more than 10,000 critical security flaws, Anthropic intends a broader trusted access program, co-founder Jack Clark told BBC Newsnight the industry has "a gas pedal, but it doesn't have a brake pedal", and private valuation neared $1tn amid expected IPO—without treating media hype as signed enterprise contracts.

Structures verification of frontier-model lab IPO headlines into a finance and governance checklist. The workflow separates confidential S-1 filing facts from valuation rhetoric, tender-offer liquidity plans, and competitive IPO timing in the same news cycle. It references CNBC reporting on June 8–9, 2026 that OpenAI confidentially filed for an IPO with the SEC, publicly posted: "We recently submitted a confidential S-1… We have not decided on timing yet; it may be a while because there are things we want to do that are likely easier as a private company"; CNBC said OpenAI is valued at more than $850 billion, has been gearing up to go public as soon as Q4 2026, is working with Goldman Sachs and Morgan Stanley, plans a tender offer letting employees sell at the latest $852 billion post-money valuation, cites ChatGPT supporting more than 900 million weekly active users, raised more than $180 billion in funding while still burning cash for compute, and filed a week after Anthropic's confidential IPO filing at a $965 billion valuation—without treating media valuations as your investment thesis.

Structures verification of hyperscaler and neocloud GPU lease headlines into a capacity-planning checklist. The workflow separates announced monthly fees and GPU counts from delivery SLAs, termination clauses, and bridge-vs-strategic capacity framing in the same article. It references CNBC reporting on June 5, 2026 that SpaceX will receive $920 million per month from Google from October 2026 through June 2029 for about 110,000 Nvidia GPUs plus CPUs and memory in SpaceX data centers, with capacity ramping through September at a reduced fee; Google may end the deal if committed GPUs are not delivered by September 30, 2026; either party may terminate with 90 days' notice after December 31, 2026; a Google Cloud spokesperson cited bridge capacity for surging Gemini Enterprise demand; the deal follows SpaceX's February xAI merger valued at $1.25 trillion and Anthropic's May Colossus 1 arrangement; CNBC noted SpaceX Q1 capex $10.1 billion ($7.7 billion to AI) and AI segment operating loss $2.5 billion on $818 million revenue—without treating SEC filing figures as your signed contract terms.

Turns headlines about corporate AI token budgets into a finance and procurement checklist. The workflow separates fundraising valuation narratives from operational metrics CFOs can verify: provider-level token bills, model-mix efficiency, team attribution, and whether frontier models are used for low-value tasks. It references CNBC reporting on June 4, 2026 that Ramp raised $750 million at a $44 billion valuation led by ICONIQ, GIC, and Ontario Teachers' Pension Plan (~38% step-up), crossed $1 billion in annualized revenue with positive free cash flow per CEO Eric Glyman, serves 70,000 businesses, and is growing partly because clients need to rein in AI spending; Glyman said tokens are a new third pillar of spend, most CFOs did not plan for steep growth, Ramp customers spending the most revenue share on AI grew revenue 12% versus flat for the lowest spenders, and Glyman called the era of tokenmaxxing nearing its end—without treating media quotes as internal budget approvals.

Structures verification of custom-AI chip vendor earnings headlines into a finance and supply-chain checklist. The workflow separates consolidated revenue and EPS beats from AI semiconductor sub-segment growth, full-year AI revenue guidance (raised vs reiterated), and infrastructure software shortfalls cited in the same report. It references CNBC reporting on June 3, 2026 that Broadcom's fiscal Q2 revenue was $22.19 billion versus $22.27 billion estimated (48% YoY), adjusted EPS $2.44 vs $2.40, AI semiconductor revenue $10.8 billion (more than doubled YoY), Q3 revenue guidance about $29.4 billion vs $28.53 billion expected, infrastructure software revenue $7.18 billion vs $7.32 billion expected, CEO Hock Tan reiterating AI semiconductor revenue in excess of $100 billion in fiscal 2027 without raising the 2026 forecast, naming six core custom-chip customers including Anthropic, Google, Meta, and OpenAI, and saying Broadcom would offer chips only rather than complete integrated AI systems—without treating media figures as procurement commitments.

Turns CEO and vendor narratives about agentic AI efficiency into a procurement and strategy checklist. The workflow separates quoted efficiency metrics (for example token- or energy-per-user framing) from product launch facts, orchestration architecture claims, and third-party valuation context in the same article. It references CNBC reporting on June 3, 2026 that Perplexity CEO Aravind Srinivas told CNBC's Elaine Yu the long-term AI winner will maximize what he called the "most taken value per watt per user" by balancing accuracy, latency, cost, privacy, and intelligence; that Perplexity is emphasizing agentic orchestration with Perplexity Computer (announced February) and Personal Computer on Windows (announced the prior Tuesday, with Mac already available); that Srinivas said Personal Computer routes processing between device and cloud; that Perplexity was last reportedly valued at $20 billion versus Anthropic near $1 trillion and OpenAI just over $850 billion with Anthropic confidentially filing for a U.S. IPO that week; and that Srinivas cited tripled annualized revenue since the start of the year tied to integrated Anthropic model improvements—without treating media valuations or CEO efficiency slogans as internal benchmarks.

Structures verification of public-company AI infrastructure financing headlines into a finance and strategy checklist. The workflow separates announced equity program components (underwritten offerings, at-the-market programs, private placements) from previously disclosed capex guidance and debt-market context cited in the same coverage. It references CNBC reporting on June 1, 2026 that Alphabet plans to sell $80 billion in stock—including a $10 billion Berkshire Hathaway private placement—to fund AI compute infrastructure while stating demand exceeds available supply; that Alphabet revised 2026 capex guidance to $180–$190 billion (up from $175–$185 billion); CEO Sundar Pichai cited compute capacity constraints; CNBC noted hyperscaler combined capex expectations above $700 billion in 2026 and prior Alphabet bond issuances—without treating media capex totals as your internal budget.

Converts consumer-AI subscription announcements into a planning checklist for product, finance, and partnerships teams. The workflow separates test-market scope (countries, price tiers, free-tier continuity) from analyst revenue extrapolations and capex guidance cited in the same news cycle. It references CNBC reporting on May 30, 2026 that Meta will test Meta AI subscriptions at $7.99 and $19.99 per month starting next month in Singapore, Guatemala, and Bolivia while keeping a free tier; that nearly 98% of Meta's $56.3 billion Q1 revenue still came from ads; Zuckerberg said a cloud business is "definitely on the table"; Meta raised 2026 AI capex guidance to $125–$145 billion; and Wolfe Research analysts estimated subscriptions could reach about $3 billion in 2027 revenue growing to $16 billion by 2030—without treating media projections as internal forecasts.

Structures verification of headline private-market AI funding rounds into an evidence checklist for strategy, finance, and partnerships teams. The workflow separates announced valuation, round size, lead investors, previously committed capital, and revenue run-rate figures from independently confirmable filings or issuer press releases. It cites CNBC reporting on May 28, 2026 that Anthropic announced a $65 billion Series H at a $965 billion valuation led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital—including $15 billion of previously committed investments with $5 billion from Amazon—surpassing OpenAI's reported $852 billion valuation after its March funding round, while Anthropic cited a $47 billion revenue run rate and releases of Claude Opus 4.8 and Claude Mythos Preview—without treating media valuations as internal planning numbers.

Turns announcements of multi-year cloud spend commitments and earnings-day infrastructure deals into a finance-and-platform checklist. Teams separate headline dollar totals (for example five-year AWS purchase obligations) from average annual run rates, prior amended agreements, and what is actually earmarked for AI GPUs versus general-purpose silicon. The workflow maps public claims to internal FinOps data before revising data-platform budgets or agentic-AI roadmaps. It cites CNBC reporting on May 27, 2026 that Amazon disclosed a $6 billion five-year Snowflake commitment covering Graviton and AI GPUs alongside Snowflake's fiscal Q1 beat ($1.39 billion revenue, 39-cent adjusted EPS vs analyst expectations) and an undisclosed Natoma acquisition—without treating media figures as procurement instructions.

Structures verification of public claims about AI-driven memory shortages, high-bandwidth memory (HBM) demand, and trillion-dollar memory-chip valuations into an evidence checklist for finance, procurement, and platform teams. The workflow separates analyst price-target moves, year-to-date equity rallies, and vendor statements about agentic-AI workloads from independently observable supply signals (long-term agreements, stated capacity constraints, peer pricing power). It cites CNBC reporting that Micron crossed a $1 trillion market cap on May 26, 2026 after UBS raised its price target from $535 to $1,625, and that SK Hynix joined the trillion-dollar club on May 27, 2026 with shares up roughly 250% year to date amid AI chip demand lifting South Korea's Kospi—without endorsing any single stock call.

Turns public semiconductor announcements into a verification checklist when vendors claim novel scaling laws, stacked logic architectures, or nanometer-class equivalence without independent benchmarks. Teams separate marketing nomenclature from manufacturing readiness by demanding yield, thermal, packaging, and third-party validation evidence—patterns highlighted when CNBC reported Huawei's LogicFolding and τ Scaling Law claims alongside analyst skepticism about true 1.4nm-class process breakthroughs without EUV access. The skill also maps export-control context (ASML EUV restrictions) and competitive implications for GPU vendors operating in constrained geographies.

Converts public-policy and labor-relations guidance around AI-driven wealth into a planning checklist for organizations operating in semiconductor-heavy economies. Teams document how AI productivity gains translate—or fail to translate—into worker bonuses, public dividends, or reinvestment; assess concentration risk when chipmakers dominate equity indices; and prepare dialogue frameworks for recurring labor-management disputes as agentic automation scales. The skill cites CNBC reporting on South Korea's deputy prime minister urging that AI benefits reach the public amid Samsung strike negotiations, Kospi gains led by Samsung and SK Hynix, and debates over distributing AI-sector tax windfalls—without prescribing specific tax policies beyond verifying stakeholder messaging against cited facts.

Turns Microsoft Learn responsible AI modules and accessibility remediation patterns into a checklist for teams shipping generative features that emit images, code, or UI copy. Practitioners verify training-data gaps (for example stereotypical depictions of disabled users), audit metadata labels on inclusive datasets, document human-in-the-loop fixes, and align with published principles that people remain accountable for AI outcomes. The skill references learn.microsoft.com training on responsible AI practices and real-world corrections such as purchasing supplemental multimodal data when model outputs misrepresent blind users—without skipping metadata-layer bias reviews emphasized by ML fairness practitioners.

Turns platform reliability and multi-vendor coding-agent guidance into a checklist before standardizing on a single AI coding stack. Teams inventory host-platform SLAs (for example GitHub availability incidents documented on githubstatus.com), compare primary and backup agents (GitHub Copilot, Cursor, Claude Code, Codex, etc.), verify observability hooks through Braintrust or similar tracing, and rehearse workflows when the code host or agent API is degraded. The skill cites public status pages and vendor billing changes—such as usage-based Copilot pricing announced on github.blog—so procurement and engineering sign off with eyes open about downtime, leadership churn, and feature parity gaps reported in trade media.

Converts export-control and multi-vendor routing guidance into a planning checklist for teams that cannot assume a single geography or chip supplier will stay available. Practitioners document primary and contingency model routes (including gateways such as Helicone or LiteLLM Router configs), quantify revenue or latency exposure if a region is blocked, and set investor/customer messaging when leadership advises to "expect nothing" from a market—as publicly reported when semiconductor vendors discuss China licensing uncertainty. The skill cross-checks legal/compliance sign-off, drills failover to alternate regions or domestic stacks, and records evidence before production launches tied to geopolitically sensitive deployments.

Translates LiteLLM routing documentation into a pre-flight checklist before promoting multi-deployment LLM routes to production. Teams verify Router configuration covers primary and fallback model lists, retry policies, and load-balancing strategy documented at docs.litellm.ai/docs/routing, confirm proxy virtual keys and spend limits if traffic flows through LiteLLM Proxy, and rehearse provider outage drills using OpenAI-mapped exceptions (AuthenticationError, RateLimitError, APIError). The skill also points operators to enable `store_model_in_db` when MCP tools must persist alongside router definitions and to validate MCP server names comply with SEP-986 guidance referenced in LiteLLM v1.80.18 release notes.

Turns LangSmith observability documentation into a repeatable incident workflow for LLM and agent outages: start from a failing run ID or thread, use the UI or LangSmith MCP tools (`fetch_runs`, `get_thread_history`) to reconstruct prompts, tool calls, and errors, then narrow scope with documented filters (run_type, is_root, FQL `filter` / `trace_filter` / `tree_filter`) before proposing code or prompt changes. The playbook cites official pagination rules (character-budget pages with `page_number` and `total_pages`) so investigators do not assume single-shot dumps, and it reminds teams to separate Cloud OAuth Remote MCP paths from self-hosted `LANGSMITH_ENDPOINT` configurations when collecting evidence.

Maps the authoritative OWASP "Top 10 for Large Language Model Applications" (version 1.1) taxonomy—LLM01 Prompt Injection through LLM10 Model Theft—into an actionable readiness checklist for architects red-teaming Retrieval-Augmented Generation, Agents, plugins, training pipelines, or hosted inference gateways. Official project pages summarize each risk bucket (prompt injection bypassing safeguards, unchecked outputs enabling downstream exploits, poisoned corpora distorting reasoning, abusive workloads starving capacity, brittle supply-chain dependencies, sensitive data resurfacing inside generations, excessively privileged plugins/agents/autonomy, misplaced trust producing compliance failures, loss of proprietary model weights via API abuse). The skill pairs each category with tangible controls (policy, monitoring, toolchain limits) anchored to genai.owasp.org releases rather than anecdotes.

Distills Appendix C (“Results of Postmortem Analysis”) from Google’s SRE workbook: it explains why Google catalogs standardized postmortem fields—linking outages to observable triggers versus deeper root-cause categories—so reliability leaders can prioritize systemic fixes rather than anecdotal fixes. The appendix cites a multi-year corpus (labeled 2010–2017 in the workbook) highlighting that binary pushes accounted for roughly 37% of outage triggers while configuration pushes were about 31%, with additional slices for user-behavior spikes, pipelines, upstream providers, performance decay, capacity, and hardware. A companion table correlates outages with qualitative root causes such as faulty software (~41%), development-process gaps (~20%), emergent complexity (~17%), deployment planning weaknesses (~7%), and network failures (~3%). Teams use these distributions to sanity-check whether their incident queues skew differently and to steer investment into the failure classes that statistically dominate historically.

Operationalizes Appendix A from Google’s SRE workbook by translating the illustrative “Example Game Service” SLO dossier into a checklist teams can mimic: articulate the user-facing workload, nominate rolling measurement windows (the appendix uses four weeks), pair each subsystem with tightly defined SLIs (availability from load balancers excluding 5xx, latency percentile gates, freshness for derived tables, correctness via probers, completeness for pipelines), cite explicit numerator/denominator language, rationalize rounding policies, quantify per-objective error budgets, and cite the sibling error budget policy for enforcement.

Translates Google’s worked example error-budget policy into a repeatable playbook for tying release tempo to measured reliability: define goals (protect users from repeated SLO misses while preserving innovation incentives), spell out what happens when the rolling window consumes its budget (freeze changes except urgent defects or security work), codify outage investigation thresholds, and document escalation paths when stakeholders disagree about budget math.

Anchors facilitation workshops to NIST's voluntary Artificial Intelligence Risk Management Framework (AI RMF 1.0, formally NIST.AI.100-1 with DOI https://doi.org/10.6028/NIST.AI.100-1): the playbook issued alongside the Framework emphasizes structuring programs around the mutually reinforcing core functions GOVERN → MAP → MEASURE → MANAGE rather than improvising unrelated security tickets. NIST contemporaneously publishes companion assets such as the Trustworthy AI Resource Center playbook (airc.nist.gov), roadmap, crosswalks, and—for generative workloads—the Generative Artificial Intelligence Profile (NIST AI 600-1, July 26, 2024, DOI https://doi.org/10.6028/NIST.AI.600-1)—so teams can reconcile novel failure modes against documented categories of trustworthiness. This operational skill folds those authoritative layers into scripted prompts for cross-functional councils that must evidence documentation, escalation paths, quantitative trustworthiness analyses, prioritized mitigations, and alignment with externally referenced stakeholder expectations—not marketing slides.

Defines how to author, revise, and validate SKILL.md files so agent skills stay executable, scoped, and testable. It focuses on turning vague know-how into reusable operational instructions with clear triggers, deterministic steps, and verification checks.

This skill covers when and how to ask an LLM for machine-readable payloads: define a JSON Schema (or the vendor's equivalent), enable the structured-output feature your provider documents, validate responses in application code, and handle refusals or validation errors explicitly. It applies to tool-calling agents, extraction pipelines, configuration emitters, and any workflow where brittle text parsing creates production risk.

Follow Cursor's official Rules documentation when you want persistent Agent guidance tied to a repository. Project rules encode architecture expectations, risky-folder guardrails, or repeatable workflows; Cursor applies them via Always Apply, intelligent relevance, glob-scoped attachments, or manual @mentions. Use .mdc frontmatter for finer control and reference templates with @file instead of pasting large snippets.

Converts raw meeting transcripts into structured, actionable notes with decision logs, assigned action items, and key context preserved for future AI retrieval. This skill bridges the gap between what was discussed in a meeting and what AI agents need to know when acting on outcomes days or weeks later.

Structured process for handling production incidents from detection to resolution and post-mortem. Covers severity assessment using P0-P3 grading, team coordination with a designated incident commander, communication templates for stakeholders and users, and structured post-mortem requirements to drive organizational learning from every significant outage.

Context-Aware QA is a prompting technique where an AI model is instructed to retrieve and cite authoritative sources before answering factual questions. By combining retrieval-augmented generation (RAG) with explicit verification instructions, it dramatically reduces hallucinations in production AI systems.

Diagnoses live production incidents using log triage, metric spike correlation, deploy window filtering, and safe reproduction steps without causing further disruption. Production debugging applies systematic debugging principles in a live environment where the cost of wrong actions is high and the ability to reproduce the issue is limited.

A structured checklist for upgrading npm, pip, Cargo, or similar dependency managers without breaking production. This covers changelog analysis, semver risk assessment, lockfile handling, and smoke testing so that routine dependency updates do not become sources of production incidents.

Builds production-ready retrieval-augmented generation pipelines with deliberate chunking strategies, embedding model selection, vector store configuration, hybrid search blending, and reranking so agents answer from your documents with reduced hallucination and cited sources. This skill focuses on the engineering decisions that separate a working prototype from a production-quality RAG system.

Designs clean handoff protocols between specialized agents so work passes between planner, coder, reviewer, and executor agents without losing context, creating circular dependencies, or introducing race conditions. Handoff design treats agent-to-agent communication as an API contract with versioning, error handling, and explicit acknowledgment requirements.

Extracts architecture decisions, API contracts, and usage patterns directly from code to produce accurate documentation that stays in sync with implementation. Documentation-from-code treats code as the source of truth and generates prose from it rather than maintaining documentation as a separate artifact that diverges over time.

Diagnoses indexing, crawlability, and on-page SEO issues across an entire site using automated crawls, Lighthouse checks, and structured output. An SEO audit surfaces actionable findings ranked by priority before manual review, making it possible to address critical issues quickly rather than discovering them through traffic drops.

Structures multi-step agent tasks with explicit inputs, outputs, fallback behavior, and handoff protocols so agents reliably complete complex workflows instead of stopping at the first blocker. Agentic workflow design applies software engineering discipline to AI agent pipelines, treating each step as a function with typed inputs and outputs.

Builds and maintains semantic indexes of a codebase so AI coding assistants can retrieve relevant context—file relationships, symbol usage, historical decisions—without re-parsing the entire codebase on every query. Codebase indexing is essential for large codebases where context window limits prevent feeding the entire codebase to the model.

Writes product requirements documents that AI agents can act on reliably, with explicit constraints, edge cases, and acceptance criteria that minimize the gap between what you mean and what the agent builds. This skill bridges the ambiguity of natural language product specs and the precision that AI agents require to produce consistent results.

Reviews AI-generated code for security failure modes that AI assistants commonly miss: prompt injection risks, credential exposure, dependency vulnerabilities, insecure deserialization, and access control gaps. This skill catches what agents miss when they optimize for functionality over safety, especially in code that handles user input, authentication, or external data.

Curates, deduplicates, and formats training datasets for fine-tuning so that the resulting model actually improves on target behaviors rather than learning noise. Fine-tuning preparation covers dataset quality filtering, output format consistency, train/test splits, and avoiding common pitfalls like data leakage that invalidate fine-tuning results.

Builds evaluation suites with ground-truth answers, automated scoring, and regression detection so you can measure whether model or prompt changes actually improve outcomes before shipping. Without systematic evaluation, teams ship changes that seem better anecdotally but may degrade specific edge cases silently.

Coordinates multiple AI agents on shared tasks with explicit handoff protocols, shared state management, and conflict resolution so parallel work stays coherent. Multi-agent orchestration is more structured than simple parallel dispatch because agents take on distinct roles with explicit dependencies rather than running identical briefs on independent data.

Audits token usage, model selection, caching strategy, and prompt compression to prevent runaway inference costs as AI features scale. This is especially important for high-volume agentic workflows where repeated calls compound quickly, and where the gap between a well-optimized and a careless implementation can be orders of magnitude in cost.

Crafts prompts with explicit task framing, role definition, output constraints, citation requirements, and few-shot examples so model responses are consistent, grounded in evidence, and actionable for downstream tasks. Prompt engineering reduces the variability and hallucination risk that comes from under-specified prompts.

Builds retrieval-augmented generation pipelines that ground model responses in your own documents rather than generic training knowledge. A RAG implementation covers document ingestion, semantic chunking, embedding, vector storage, hybrid search, reranking, and answer synthesis—so assistants answer from your data with cited sources.

Establishes golden signals (latency, traffic, errors, saturation), SLO windows, and dashboard checks before agents automate deployments so that 'healthy' and 'degraded' have measurable definitions rather than subjective interpretations. This is essential when AI agents are managing deploys because agents need objective metrics to make decisions, not human gut feelings.

Captures the full incident timeline, blast radius, contributing factors, and concrete follow-up actions after production incidents so teams build institutional memory rather than repeating the same surprises. A well-written postmortem separates root cause from triggers, avoids blame, and produces tracked action items that prevent recurrence.

Keeps AI assistant answers anchored to the actual library documentation, changelog, and typed signatures that are shipped rather than to memory or stale blog summaries. This is essential during major version bumps, unfamiliar SDK integration, or on-call hotfixes where confident but incorrect guesses about API behavior cause more damage than the original bug.

Locks API expectations between services using consumer-driven contracts so that when one team changes their implementation, it fails in CI rather than during a coordinated production deployment. Contract testing prevents the common integration failure pattern where both sides of an API appear to work in isolation but break when connected in production.

Deploys a new version to a small percentage of production traffic first, monitors error budgets and latency against baseline, and automatically widens or rolls back based on pre-defined criteria. This keeps the blast radius of a bad deployment small—particularly important when AI agents are modifying deployment pipelines where a single bad command could affect many users.

Defines a consistent set of log fields—request ID, user ID, feature flag, latency bucket, error code—so production debugging does not rely on grep across inconsistent printf-style strings. Structured JSON or key=value logging enables dashboards, alerts, and log aggregation tools to parse and query logs programmatically rather than through manual text searching.

Systematically identifies threats to a system by mapping data flows, defining trust boundaries, and enumerating adversaries and misuse cases before shipping. This produces a security-focused diagram and prioritized mitigation list that makes subsequent security reviews faster and more substantive than starting from a generic checklist.

Executes refactoring changes in small, test-backed steps so behavior is preserved while structure improves. Each refactoring operation—rename, extract, inline, move—is validated by the test suite before proceeding to the next, preventing the common pattern of refactoring into subtle behavioral regressions that are only caught in production.

Removes the common AI-generated writing patterns—significance inflation, filler -ing constructions, em-dash chains, and formulaic closers—that make machine-generated prose feel generic or overproduced. Runs a final 'still obviously AI?' audit pass before shipping any prose intended for human readers.

Finds genuine performance bottlenecks using CPU profiles, flame graphs, memory traces, and system metrics under realistic load before rewriting code. This prevents the common anti-pattern of spending days optimizing code paths that are not in the critical path, based on intuition rather than measurement.

Tightens Chinese drafts by removing translationese, slogan-like endings, stacked abstractions, and stiff AI rhythm while preserving factual accuracy. This addresses the specific failure modes of machine-translated or AI-generated Chinese text: word-for-word English structures, Western rhetorical patterns that feel unnatural to Chinese readers, and filler phrases that add length without meaning.

Checks whether a claim is backed by a primary source, a current official page, or a reputable secondary source before that claim becomes published copy. This skill is essential for AI tool directories, MCP server listings, and news summaries where accuracy and trustworthiness directly affect reader decisions and SEO credibility.

Runs a scheduled audit of existing tool, MCP, skill, and news entries to identify and address stale pricing, broken documentation links, outdated capabilities, and weakened prose that quietly degrades directory quality. This maintenance rhythm prevents the directory from accumulating digital rot as tools evolve and entries grow outdated.

Reviews sitemap completeness, canonical URL configuration, hreflang pairing for bilingual sites, robots.txt directives, and Search Console signals before publishing a content batch. This is especially important for bilingual static sites where indexing misconfigurations can cause search engines to index the wrong locale or deprioritize pages unfairly.

Shapes REST or RPC API surfaces with consistent resource modeling, predictable error responses, paginated list endpoints, and an explicit deprecation policy before implementation locks you into contracts that are costly to change. Good API design prevents client breakage, reduces support burden, and makes feature additions less disruptive.

Frames a pull request so reviewers understand the risk profile, what has been tested, and where to focus their limited attention. This produces faster, more useful reviews because reviewers spend less time reconstructing context and more time evaluating the actual changes.

Executes a pre-written implementation plan in disciplined order, stopping at defined checkpoints to verify assumptions before moving forward. This skill prevents the common pattern of diverging from the plan silently when reality proves it wrong, and it creates natural opportunities to course-correct before small errors compound into large rework.

Converts vague or frozen requirements into precise, step-by-step implementation plans with file-level touchpoints, decision checkpoints, and verifiable acceptance criteria before any code is written. This bridges the gap between what stakeholders want and what engineers can actually ship, reducing mid-sprint surprises and wasted refactors.

Uses Git worktrees to create isolated working directories attached to the same repository, each on a different branch, so parallel experiments or long-running tasks do not interfere with the main working tree or require repeated stash-and-reapply cycles. This is especially useful when one branch requires a heavy build or test run while work continues on another.

Drives development through red-green-refactor cycles where you write a failing test that names the desired behavior before writing any implementation code. TDD produces tests that document intent, catches regressions immediately, and forces small, verifiable increments—making it especially valuable for complex features, bug fixes with known failure cases, and any code that needs a long-term safety net.

Distributes embarrassingly parallel work across multiple AI agents with clear briefs and crisp handoff protocols, then aggregates their results through a single integrator. This technique maximizes throughput when tasks are independent and the coordination overhead is low, making it ideal for research chunks, file batches, or parallel data processing.

Replaces trial-and-error debugging with a hypothesis-driven process: state a falsifiable hypothesis, construct the smallest possible reproduction, and verify evidence before touching code. This structured approach is most valuable during production incidents, flaky CI builds, and confusing regressions where intuition-led debugging wastes hours on correlated but non-causal symptoms.

Coordinates multiple AI subagents on slices of a larger plan where each subagent handles a defined scope while a single parent agent retains accountability for integration, quality, and final delivery. This approach is valuable when a single agent working sequentially would be too slow, but you still need coherent end-to-end quality rather than fragmented outputs.

Creates or edits bitmap artwork for covers, concept mockups, and rapid visual exploration when the deliverable requires photographic quality, complex textures, or artistic styles that are impractical to hand-code in SVG or CSS. Image generation accelerates the early design phase by producing concrete visual references before committing to a final style.

Systematically closes out a development branch by running verification, cleaning up the commit history, pushing with proper tracking, and making an explicit choice between merge, squash, or follow-up tickets. This prevents the common pattern of abandoned branches, stale PRs, and lost context when work is not deliberately concluded.

Bootstraps a complete plugin project structure with manifest files, entry points, configuration schemas, and baseline tests so new Codex or editor extensions follow a consistent, reviewable template from day one. This eliminates the setup tax for creating new plugins and ensures every plugin in a codebase shares the same conventions for configuration, logging, and error handling.

Explores goals, constraints, risks, and design options before committing to a specific implementation path. This technique is most valuable when facing product or UX decisions where the wrong choice is expensive to reverse—new features with uncertain user value, architectural pivots, or cross-functional dependencies where each team has a different mental model of the problem.

Creates production-grade UI layouts and components with deliberate spacing, typography hierarchy, color application, and motion design so the interface communicates structure and state clearly. This skill is applied when building new UI sections, redesigning existing pages, or establishing component patterns that need to feel intentional and cohesive rather than defaults from a component library.

Runs the minimal set of checks—tests, builds, manual verifications, or environment-specific validations—that confirm a task is truly complete before it is marked done. This practice prevents the common pattern where 'done' means 'written' rather than 'working in production,' and it creates a shared definition of completion across the team.

Prioritizes official OpenAI documentation, model cards, and API references when researching integration details, model capabilities, or API behavior changes. This avoids the noise and staleness of third-party blog posts that may summarize older model versions or incomplete information.

Structures how you respond to code review feedback so the review process stays focused, respectful, and productive. This skill separates substantive feedback from nitpicks, tracks follow-ups without losing them, and produces a record that makes merges faster and post-mortems clearer.

This skill guides you through connecting an AI agent to a PostgreSQL database using the pg-mcp-server Model Context Protocol implementation. It covers installing the MCP server, configuring the database connection, exploring schemas as MCP resources, and running queries through natural language. The workflow is designed for developers who need to understand, document, or query an unfamiliar database without writing raw SQL manually.