「facts」の検索結果

Maintain an append-only project INVOICES.csv ledger from explicit billing facts and recorded lifecycle evidence; never infer charges or payment from activity or time logs.

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Use when you need to CAPTURE / PRODUCE XProf (jax.profiler) profiling artifacts from sgl-jax (SGLang on JAX/TPU) for later performance analysis: driving a live server (/start_profile, stage-separated prefill/decode), offline bench_one_batch --profile, or kernel microbench. This skill produces the trace files; it does not analyze them.

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Use when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.

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Generate sports trivia questions by sport/era/difficulty. Multiple formats with obscure stats, fun facts, pub quiz ready content.

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Pulls financial statement numbers for US public companies straight from SEC EDGAR's free official XBRL APIs (companyfacts, companyconcept, frames, submissions) into a cited table. Every value carries its accession number, form, fiscal period, filed date, XBRL tag and a link to the filing, and the tool handles fiscal-year ends, quarterly vs YTD vs TTM, derived Q4, restatements and units. Use this whenever the user wants 10-K numbers or 10-Q numbers, says "pull financials for", asks for public company revenue, net income, EPS, cash, debt or capex, wants comps or a competitor side-by-side, a credit check, prospect research, or model inputs, or mentions SEC filings or EDGAR, even if they never say XBRL. Free and primary-source, no paid data terminal needed. Do not use it for private companies, stock prices or valuation multiples.

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Merges ephemeral report and analysis artifacts into permanent documentation. Use when LLM-generated markdown files have accumulated and need consolidation.

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Run and ship this repo: make targets, artifacts, release runbook. Use when testing, linting, or releasing. Do not use for setup; use night-market-build-and-env.

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Use when a trained medical-imaging model needs its documentation. Fills a Model Card and a Datasheet for its dataset from facts you supply, adds a METRIC-informed data-quality pass and gates that no required section is empty. Never invents numbers, provenance or licence.

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Use when preparing a release, tagging a version, or when user says "release", "tag", "ship it", "push to production" - ensures all release artifacts are verified before tagging to avoid the retag-four-times failure pattern

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Use ExecuTorch to export PyTorch models to edge runtime artifacts, build host/device runtimes, choose delegates, profile/debug execution, and operate specialized Qualcomm, Cortex-M, LLM, and binary-size workflows.

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Routes automl_gs tabular AutoML searches and the generated runtime artifacts that those searches produce.

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CTF challenge router - fingerprint a challenge (file type / prompt / artifacts) into its category (pwn, rev, crypto, forensics, stego, web, osint, hash) and route to the matching wiki page, tools, and first moves. Wiki-first.

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Adaptive, targeted web fuzzing - deterministic wordlist selection (wl-pick.sh) plus judgment. Picks the right SecLists list per surface (content/vhost/api/params/artifacts) smallest-first, calibrates filters against soft-404s, recurses, escalates T0 harness -> T1 seclists -> T2 cewl -> T3 app-specific on signal, pivots to hidden-param fuzzing, and detects/handles WAF/Cloudflare/throttle (backoff, origin-bypass, or hard STOP on the DoS tell). Engagement-type aware (ctf loud, pt calibrated, bb stealth). Use for "fuzz", "content discovery", "directory brute", "vhost fuzz", "find hidden params", "which wordlist", "gobuster/ffuf/feroxbuster/cewl/arjun".

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Honey for plain Claude — the terse-prose core with no agent-harness features. Paste into a claude.ai Project's custom instructions, a Style, or an API system prompt. Strips filler, hedging, and pleasantries from every response while keeping facts, names, numbers, steps, and code exact. No tools required.

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Create and maintain one committed per-project memory file (PROJECT.md) so agents stop re-discovering the same facts every cold session. Stores only the stable, expensive-to-rediscover, not-in-the-code context — architecture + why, build/test/run commands, decisions and dead-ends — as human-readable markdown, versioned with the code so it can't silently rot. Use when asked to set up project memory/context, stop agents re-grepping the codebase, scaffold or refresh a PROJECT.md / CLAUDE.md memory section, or cut cold-start rediscovery cost. Prose context only — never code, config, or data.

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Per-project persistent memory: save and recall durable facts across sessions as small frontmatter files, indexed in MEMORY.md.

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Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.

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Use when finding literature, datasets, domain facts, citation details or licensed figure references.

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Maintains the GDPR Article 33(5) breach register documenting all personal data breaches regardless of whether supervisory authority notification was required. Covers mandatory register fields including facts, effects, and remedial actions, retention periods, audit readiness, and integration with the accountability framework. Keywords: breach register, Article 33(5), breach documentation, accountability, audit readiness, remedial actions.

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Triage failed Agent QA runs with MCP evidence, artifacts, logs, fixed failure categories, confidence, and actionable next steps.

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Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.

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Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

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This skill should be used when the user asks to "research", "fact-check", "check the history", "is this accurate", "research notes", "sources", "historical accuracy", "technical accuracy", "how would this really work", "verify a detail", "interview an expert", "plan a site visit", "expert review", or needs to investigate and record the real-world facts a story relies on and the chapters that use them.

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Remove signs of AI-generated writing from prose, and keep it out of a repo. Use when drafting, editing, or reviewing text to make it sound natural and human, or when auditing a whole docs folder for AI slop. Detects 41 patterns across content, language, style, communication, filler, and rhetoric, including: significance inflation, promotional language, -ing tails, vague attributions, AI vocabulary, copula avoidance, negative parallelisms, false agency, em dash overuse, chatbot artifacts, hedging stacks, staccato drama, and aphorism formulas. Includes voice calibration, a no-fabrication rule, statistical tells, and a draft -> audit -> final rewrite loop.

日本語の概要は準備中です。原文の説明を表示しています。