「artifacts」の検索結果

Verify a claim with fresh local evidence: restate it falsifiably, capture baseline and treatment, compare artifacts, and return VERIFIED, NOT VERIFIED, or INCONCLUSIVE.

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Verify implementation matches change artifacts. Use when the user wants to validate that implementation is complete, correct, and coherent before archiving.

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Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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Adopt, read, reconcile or revise project FF&E item and schedule records with permanent identities and exact revision pins; preserve native workbook views and recover new artifacts.

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Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.

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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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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 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.

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。

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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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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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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After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.

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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.

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。

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。

Report which workflow artifacts exist, which RFCs are implemented and reviewed, what has drifted, and the next step.

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Splits a body of context into a sequence of vertical-slice build phases where each phase is independently demonstrable to a real user and each builds on the previous. Use when the user wants to plan, sequence, phase, slice, break down, or order the build of a feature, capability, system, or initiative, and produces a plain-language phased build outline. Does not produce implementation detail — use plan-implementation. Does not specify behavior that has not been decided — use plan-a-feature. Does not perform gap analysis between two artifacts — use gap-analysis. Does not break a plan into independently-grabbable work items — use plan-work-items.

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Researches an open-ended question — options, possible solutions, prior art, trade-offs, or how something works — and produces a durable, evidence-backed, adversarially-validated report that recommends an option without committing the team to any artifact. Use when you want to research approaches, weigh options, survey prior art or the state of the art, or understand how something works before committing to a direction. Does not diagnose a bug, failure, or root cause — use investigate. Does not specify a feature — use plan-a-feature. Does not create or update a coding standard — use coding-standard. Does not compare two concrete artifacts for gaps — use gap-analysis. Does not assess an existing module's architecture — use architectural-analysis. Does not capture feedback on Han's own skills — use han-feedback.

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Performs a gap analysis between two artifacts (a current state and a desired state) and produces a plain-language, stakeholder-readable report indexed by stable gap IDs. Use when the user wants to compare, evaluate, audit, or reconcile one artifact against another. Does not investigate runtime bugs — use investigate. Does not assess module-level architecture — use architectural-analysis. Does not research open-ended options with no second artifact to compare against — use research.

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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。