「facts」の検索結果

Expert guidance for Cosign, the Sigstore tool for signing, verifying, and attaching metadata to container images and other OCI artifacts. Helps developers implement supply chain security by signing images in CI/CD, verifying signatures before deployment, and attaching SBOMs and vulnerability scan results as attestations.

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Plans, researches and writes comparison pages: "[rival] alternatives", "[product] vs [rival]", "[rival A] vs [rival B]" and switching guides. Builds each page from a ledger of sourced, dated facts and ends with an honest verdict on who should choose which product. Use when someone asks for an "alternative page", a "vs page", a "competitor comparison", "comparison landing pages", "why us instead of them", or wants existing comparison pages checked for outdated prices and claims that cannot be backed up.

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。

ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.

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Citation-integrity forensics: is every reference real, correctly attributed, and used in a context the cited work actually supports? Catches hallucinated references (no paper at the claimed arXiv id/DOI/venue, fabricated authors/year), metadata drift (wrong year/venue/version), and wrong-context citations (a real paper cited for a claim it never makes — or argues against). A hot zone for machine-generated papers. Decidable at L0 (text + canonical sources). Span-anchored to the evidence ledger (claims.json); the executor gathers canonical facts (DBLP / arXiv / DOI), then one FRESH cross-model thread per cited key proposes findings; reviewer != adjudicator. Emits citation-forensics.findings.json; NEVER computes the verdict. Triggers: "citation forensics", "check the references", "hallucinated citations", "wrong-context citation", "verify references", "引用核对".

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Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".

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。

Build the deterministic evidence ledger (artifact_manifest.json + claims.json) that every other Anti-Autoresearch auditor reads. One pass inventories artifacts, derives the observability level (L0 PDF-only / L1 +LaTeX / L2 +repo+results) by fixed rule, and extracts span-anchored, hashed, checkable claims (numbers, comparisons, scope, method, baselines, citations, captions, table cells) into claims.json. An OPTIONAL additive cross-model pass ADDS span-anchored semantic claims — method, theorem statements with their assumptions, definitions, proof/derivation steps and equations, scope, baselines, conclusions, the motivation span, and reproducibility-artifact references (the proof, derivation, and structure anchors the family B/D/G auditors need) — it never invents a number, emits a finding, or computes a verdict. Run FIRST, before any audit skill. Triggers: "build the ledger", "extract claims", "prep for integrity audit", "evidence ledger", "建证据账本".

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Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent (HP-WEAK-BASELINE); 'outperforms' is asserted over overlapping error bars or with no variance/seeds (HP-SIG-OVERLAP); and a cross-row 'improves over baseline by X%' is arithmetically wrong (HP-DELTA-ERROR, cross-row form only). A versioned per-domain baseline profile + a live leaderboard/recency search are assembled by the EXECUTOR as structured facts; a fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per dimension) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Works at L0 (stated comparisons) and deepens at L2 (configs/result files). A completeness question it cannot settle internally becomes needs_external_check, never a guessed missing baseline. Emits baseline-comparison-audit.findings.json; computes NO verdict. Detect-only. Triggers: "baseline audit", "missing baselines", "is the comparison fair", "weak baseline", "baseline 误报", "SOTA earned?".

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Create interactive data visualizations using Vega-Lite declarative JSON grammar. Supports 20+ chart types (bar, line, scatter, histogram, boxplot, grouped/stacked variations, etc.) via templates and programmatic builders. Use when users upload data for charting, request specific chart types, or mention visualizations. Produces portable JSON specs with inline data islands that work in Claude artifacts and can be adapted for production.

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Composes single-file HTML artifacts (PR review writeups, status reports, incident postmortems, slide decks, design systems, prototypes, flowcharts, module maps, feature explainers, kanban boards, prompt tuners) from a small JSON spec instead of hand-written HTML/CSS/JS. Use when the user asks to "compare options side-by-side", requests an HTML version of a report or review or deck, asks for a flowchart, status update, postmortem, design system reference, interactive prototype, custom editor — or explicitly says "HTML artifact", "single HTML file", "self-contained HTML". Skip for ad-hoc HTML snippets (forms, emails, embedded widgets) where there's no template fit.

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The spec-to-plan bridge. /feature supplies an approved spec; initial full-lane HTML sprint review may supply a ready draft only through draft_for_pair. Creates the same-format plan with exact paths, tests, dependencies and complete criterion coverage. The initial paired review has no execution authority until one real user reply approves both artifacts. Ordinary sequential planning retains approved-source binding and plan approval.

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Manage git worktrees across any repository with the `gwm` Rust binary (CLI + ratatui TUI). Use when the user asks to create / list / remove / bootstrap / switch / link worktrees, run a command across worktrees (`gwm exec`) or reclaim build artifacts (`gwm clean`), materialise a PR into a worktree (`gwm review`), drive a multi-repo workspace (`--workspace`), run the JSON daemon / statusline (`gwm daemon`, `gwm statusline`), list or pin AI-agent sessions per worktree (`gwm agents`, Claude Code / Codex / opencode / Mistral Vibe detection), seed config from a preset (`gwm init --preset`), diagnose with `gwm doctor`, drive tmux/zellij, or wire `gcd` via `gwm shell-init`. Also covers multi-forge (#419): GitHub via `gh` **or** GitLab via `glab`, the `forge` / `[forge_hosts]` keys, and `gwm trust add`. Triggers on `gwm`, `gwq`, `git worktree`, `.gwm.toml`, `forge`, `forge_hosts`, `glab`, `gwm trust add`, GitLab MR, `gwm create`, `gwm list`, `gwm exec`, `gwm clean`, `gwm review`, `gwm daemon`, `gwm statusline`, `gwm agents`, `gwm note`, `gwm bootstrap`, `gwm doctor`, `gwm switch`, `gwm tmux`, `gwm zellij`, `gwm link`, `gwm status`, `gwm shell-init`, `gcd`, `feat/#`, `fix/#`, GitHub issue/PR linking on a worktree.

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。

Use when running or explaining the ClawScan CLI, including one-off agent-skill scans, benchmark runs, scanner fixtures, judge harness commands, env var validation, and interpreting clawscan-run-v1 and clawscan-benchmark-v1 artifacts.

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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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Help the user understand the current topic visually with concise diagrams, code-shape sketches, and focused HTML artifacts.

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Create stakeholder alignment artifacts including responsibility matrices, decision frameworks, and communication plans.

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。

Translate existing blog posts into one or more target languages with SEO-optimized localization. Produces native-quality translations that preserve markdown structure, frontmatter, schema JSON-LD, image and chart embeds, and citation capsules. Localizes keywords, meta tags, numbers, dates, currencies, and quote styles per locale. Flags machine-translation artifacts for review. Run BEFORE blog-localize: this handles language conversion; localize handles cultural adaptation after translation completes. Use when user says "translate blog", "blog translate", "uebersetzen", "traduire", "traducir", "translate post", "blog auf Deutsch", "blog en espanol".

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。

Edit drafts into natural prose, inspect AI-sounding patterns, or review how a specified audience might respond passage by passage. Zero Slop runs inside the user's existing AI assistant with local tools that protect source details. Use for humanizing or de-slopping writing, polishing outward-facing prose, social drafts, final editorial checks, or an explicit simulated reader review. Preserve facts, voice and format; reader simulations are hypotheses, not human feedback.

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Repurpose existing content into a native X (Twitter) post or thread. Take a LinkedIn post, blog, YouTube script, or newsletter and rewrite it for X: re-hook for the no-fold first line, refit to 280 chars or a thread, strip off-platform artifacts, run the humanizer, publish via Publora on approval. Not for writing from scratch (use x-post-writer or x-thread-builder), not for auditing a draft (use x-humanizer --mode audit).

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。

Resolve stale facts and authority conflicts.

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。

Suede Slop Stop: Suede Labs context-aware anti-slop pass for finished prose. Find or remove generic filler, manufactured emphasis, false agency, formulaic structure, chat and draft residue, and formatting applied by rule, without flattening the author's voice. Use before copy, a README, an email, a social post, or a doc ships; after a long assisted-writing session; or for a findings-only slop audit. NOT FOR: writing new copy (use suede-copy); deciding whether a person or model wrote text; changing or certifying facts, which require primary evidence.

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。

Run outcome-focused Firecrawl workflows that produce deliverables such as research reports, literature reviews over published papers, SEO audits, QA reports, lead lists, knowledge bases, website design systems, and other structured web-data artifacts. Use when the user wants Firecrawl to complete a business, marketing, product, or creative workflow rather than merely scrape a page or integrate API calls into code.

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Repository quality gates for Rust and generated artifacts. Use when validating changes locally or before committing Rust, schema, or documentation updates.

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Manage persistent memory across sessions. Use when asked to remember, recall, forget, or review what was stored. Covers long-term facts (MEMORY.md), daily session logs, search, and memory housekeeping.

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Use when the user wants to save knowledge as a file so others don't have to rediscover it — "turn this into a doc", "write this up", "document how X works", "we figured this out and want to capture it", "nobody should have to figure this out again". Covers any request to create or update durable written artifacts: onboarding guides, runbooks, ADRs, API docs, architecture notes, postmortems, changelogs, setup guides. The trigger: user wants knowledge captured in a file for future reference, not just a conversation. Do NOT use when still making decisions (→ give-plan), just asking for explanation without a file (→ ask), or writing code (→ cook).

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。