「facts」の検索結果

Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.

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Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP results.

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Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.

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ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.

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Phase 10.5 — move the slug's workflow artifacts (intake, scout, research, spec, approvals, swarm state, security reports, rendered diagrams) to docs/archive/<YYYY-MM-DD>/<slug>/. Runs before /commit so the committed tree is clean of work-in-flight files. workflow.json stays live and gets archived as the first step of /commit.

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Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.

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Capture baseline evidence from a live website URL: screenshots, scroll states, hover/click behavior, parallax, timers, and transition recordings. Use when reference artifacts are needed before implementation or comparison; not to build or diagnose a clone.

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Use when Amazon Ads optimization analysis, recommendations, approval packets, demos, recordings, or public artifacts must preserve exact source KPIs while masking account/profile/project names, products, ASINs, SKUs, campaigns, ad groups, keywords, search terms, targets, placements, filenames, URLs, and source-derived identifiers. Produces masked approval packets only; it must not directly mutate Amazon Ads.

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Assign confidence scores (0-100) to every claim in a response. Helps users understand which parts are verified facts and which are educated guesses. Use when the user needs to know how much to trust each part of the answer.

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OpenClaw-native version of the foreign-trade outreach email skill. Use structured operator input plus customer-intel source context to generate conservative first-touch or follow-up email drafts without overstating inferred facts or inventing recent customer events.

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Run or resume a deliverable active-outreach workflow across lead discovery, screening, customer intel, and outreach email. Use when an operator or system pipeline needs durable run state, visible intermediate artifacts, evidence gates, report-bound human approval, and a final editable email draft that is never auto-sent.

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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.

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Use for Six Thinking Hats or de Bono's parallel thinking, or when a decision or idea needs facts, gut feel, risks, benefits and new options kept apart: "look at this from all sides", "full perspective check". Skip for plain factual questions or single-angle hot takes.

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Design one-off HTML artifacts (landing, deck, prototype).

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Turn briefs, mockups, and product context into Stitch UI screens, design systems, variants, prototypes, Tailwind-friendly HTML, and screenshots. Use when the user wants to explore a screen, edit an existing Stitch project, compare visual directions, build a multi-screen prototype, or save local design/code-export artifacts from natural-language input.

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Builds the "Report Sources" child page for a finished Weekly Community Signal report — an evidence-backed defense of WHY every item landed in the column/section/rank it did. The agent argues each placement like a lawyer (claim → evidence → rule → rebuttal) but is under oath: it CANNOT invent, spin, or overstate — every claim ties to a verifiable fact (issue #, label, PR type, quoted comment, npm output, the front-door test). Not CYA — it's so Nathan can defend the report with facts and see the exact reasoning behind each call. Runs after the report (invoked by weekly-report, last steps) and on "report sources", "defend the report", "why is this here".

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Generate beautiful, on-brand HTML artifacts — reports, diagrams, diff reviews, slide decks, plans, recaps, dashboards. Loads on creation intents ("create a plan", "make me an implementation plan", "generate a diagram", "build a slide deck", "draft a recap", "visualize this", "render this as HTML", "make me a status update") and exploration intents ("show me three layouts", "explore document shapes", "compare visual treatments", "explore a new design system", "compare replacement brand directions") and iteration intents ("make this beautiful", "polish this report", "make this less generic", "design this", "theme this", "use my brand", "iterate on this design", "review this artifact", "make this bolder", "make this quieter", "simplify this design", "animate this", "improve the look of", "give this a refresh"). Captures brand from the user's codebase, asking, or live site, then applies it to every artifact.

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Answer questions about the venue rate facts recorded in this model and identify what its source leaves unspecified.

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Write commit messages under the agent-commits convention - a Conventional Commits fork with no chore catch-all, intent-only type tokens, facts in trailers, and legally-grounded AI provenance (Assisted-by, never Co-authored-by). Use when composing a commit in a repo whose commitlint.config.js extends agent-commits, when deciding between feat/fix/refactor/perf, or when recording AI participation.

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Coordinate multi-agent work through durable artifacts in git instead of chat. Use this whenever a task involves sending a message to another agent, agent mailboxes or handoffs, claiming work from a shared queue, notifying another agent or crew (OpenClaw, Grok Bot, Claude Code, Codex, Gas Town/Gas City workers) that work is ready, or whenever the user mentions the artifact bus, message bus, agent mail, beads mail, doorbells, or pings between agents — even if they don't say "bus" explicitly.

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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

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Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context. Call with 'craft' to run the full shape-then-build flow, or 'teach' for design context setup.

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Implements any user intent, requirement, story, bug fix or change request by producing clean working code artifacts that follow the project's existing architecture, patterns and conventions. Use when the user wants to build, fix, tweak, refactor, add or modify any code, component or feature.

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。