「candidates」の検索結果

Orchestrate a whole-registry sweep of Gaia — fan out 13 parallel audit agents across every skill, run adversarial verification, surface Semantic Fusion candidates, propose new generic skill references, and synthesize a publish-ready HTML report under docs/meta/reports/. Use this skill for broad, systemic analysis across many skills at once: "run a meta sweep", "sweep the meta", "full meta audit", "audit the whole registry against META.md", "widespread nomenclature issues", "find all skills missing a GitHub link", "produce a meta report", "check for evidence type mismatches across the registry", "find skills that document a third-party tool with no attribution", or explicitly types /gaia-meta-sweep. This is the registry-wide macro companion to /gaia-meta-audit (a prioritized queue, single-pass) and /gaia-audit (fixes one skill). Use it when you need the full surface — 13 audit dimensions, adversarial verification, fusion proposals, and a durable findings artifact — not just a queue.

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Generate a prioritized review queue of registry skills and catalog items that need attention. Use this skill whenever someone asks: "what needs review?", "what's overdue for audit?", "where are the weak spots in the registry?", "what should I audit next?", "run a health check on the registry", "show me flagged skills", "find problems in the registry", "what skills need fixing?", or "triage the registry". This is the triage layer — it surfaces candidates ranked P0–P4 so that focused work (via /gaia-audit) targets the right things first. Run this before any audit pass. Also invoke proactively when a PR changes registry/named/ at scale, or before a release, to catch regressions early.

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。

Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.

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Replaces the voice in a video or audio file with one from the Novoads voice catalog, over the REST API. Converts the SPEECH in the source to a voice you pick, keeps the original timing so lip-sync survives, and returns audio that gets muxed back over the untouched picture locally. Casts by MEASURING the source voice against auditioned candidates instead of reading labels, refuses a source with no speech before anything is charged, fences sound effects and music so they survive from the original track, and verifies the finished file by transcribing it. Use when an actor's voice sounds robotic, flat, thin or simply wrong, and for "change the voice", "voice swap", "swap the voice on this ad", "re-voice my ad", "the actor's voice sounds bad", "make him sound human", "different voice for this video", "speech to speech", "same video better voice". Not for narrating a silent clip (that is a voice-over), not translation, and it changes neither pronunciation nor accent.

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Diagnose whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).

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Validate OpenClaw release candidates through OCM using the built OpenClaw artifact, a broad reusable scenario matrix, copied existing-user state, clean new-user state, concise pass/fail reporting, and focused failure notes. Use when asked to test OpenClaw origin/main, release readiness, upgrade behavior, plugins, services, channels, doctor recovery, packaging, or broad release risk.

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。

Lists local and remote git branches that are candidates for cleanup — merged but not deleted, and inactive branches with no commits in a configurable period (default 3 months). Use when user mentions "stale branches", "old branches", "branch cleanup", "prune branches", "dead branches", "unused branches", "inactive branches", "branch hygiene", or asks to "list branches to delete", "find stale branches", or runs /stale-branches command.

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。

Audit Go nolint directives for staleness and lazy justifications. Mechanically verifies each suppression with golangci-lint, then runs adversarial Red/Blue/White debates on the top candidates for removal. Use when inheriting a Go codebase, during periodic cleanup, or when nolint count is growing unchecked.

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Analyze franchise expansion plans, model new market entry, score site selection candidates, map territory density, assess cannibalization risk, and build multi-year growth scenarios.

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Analyze a site's backlink profile, anchors, toxic signals, competitors, gaps, and disavow candidates. Use only when links or referring domains are the requested focus.

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。

Phase 8 of the SRS workflow. Generates an improvement report covering deferred features, technical risks, validation warnings, and next-version candidates. No word limit.

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。

Phase 8 of the SRS workflow. Generates an improvement report covering deferred features, technical risks, validation warnings, and next-version candidates. No word limit.

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 SmokingGun to find complexity candidates and organize imported optimization evidence.

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ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction for drug candidates. Use for assessing drug-likeness, PK properties, and safety risks early in drug discovery. Keywords: ADMET, PK, toxicity, drug-likeness, DILI, hERG, bioavailability

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。

Automate recruiting and hiring workflows in Ashby -- manage candidates, jobs, applications, interviews, and notes through natural language commands.

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。

Researches and compares free and open source software projects in a supplied domain, using either user-named candidates or a discovered shortlist. Use when evaluating FOSS alternatives, comparing GitHub popularity, maintenance, licences, domain fit, trade-offs, or producing a linked HTML comparison report.

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。

Capture, gate, and query long-term project knowledge via the gated wiki subsystem: propose candidates, recall, or search; agents propose, humans approve. NOT for short-lived memory (mk:memory).

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。

Fetch external sources (web/arXiv/GitHub) into scanner-gated wiki CANDIDATES, never canonical pages: guarded, capped, scanned. NOT for local knowledge (mk:wiki) or markdown fetch (mk:web-to-markdown).

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。

Fetch external sources (web / arXiv / GitHub) and turn them into scanner-gated wiki CANDIDATES — never canonical pages. Every fetched byte is DATA; url-guarded, size-capped, redirect-re-validated, injection-scanned, and secret-scrubbed before a candidate is even created. Requires network. NOT for capturing local knowledge (see mk:wiki); NOT for one-shot page-to-markdown (see mk:web-to-markdown).

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 surfacing architectural friction and proposing deepening refactors (shallow modules → deep modules) for testability and AI-navigability. Triggers on "improve architecture", "find deepening opportunities", "where is this codebase shallow", "architecture review". Emits structured candidates + precise patches; delegates ALL rendering to mk:preview. NOT for diagram/HTML rendering (see mk:preview); NOT for trade-off deliberation (see mk:party); NOT for plan critique (see mk:plan-ceo-review).

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。

Analyze task trajectories to propose reusable SKILL.md candidates from successful patterns

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。

Prepare qualified Fusion CAM setups, operations, toolpaths and quarantined NC candidates using explicit manufacturing assets and authority. Never controls physical equipment.

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。

Safely localize unknown Unity games, especially Japanese-to-Chinese projects. Use to discover Unity version/layout/backend, migrate translations across game versions, extract occurrence-mapped text from serialized assets, embedded CSV, bytecode, UI, Localization StringTables, Utage, or middleware, bridge rows through AiNiee-style translation, validate and transactionally write approved candidates, audit residual and runtime-only text or AutoTranslator diffs, diagnose TMP fonts and Addressables/IL2CPP failures, locate saves, or acceptance-test SHA-verified patches.

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。

Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).

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。