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Fixed-topology, discovery-only extension for Gaia curation. Atomically checkpoints each candidate transition, retries only the failed field, and stops at human L4 review.

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Comprehensive, reusable playbook for multi-component suite intake under Yggdrasil III. Audits component installability, maps origin candidates to generic capabilities, ingests named components with shared-repo baseline caps, manifests suites via gaia dev fuse, calibrates capstone and component ranks, and closes out via Class S regeneration.

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Discovery-only Gaia curation for one manually selected source page. Fetches and normalizes up to five real SKILL.md candidates, deduplicates and maps them for human L4 review, then stops without evidence scoring or registry mutation.

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Phase 5 of the feature-pipeline suite. Use this after /fp-iterate prints the M4 stop hook confirming CI is green. Fetches origin/main, runs gaia dev diff to surface any skill-registry changes the PR introduced that are unrelated to its stated purpose (accidental drift), then prints the final pipeline summary including a CI churn report (via /ci-churn). Also invoke standalone whenever you want to audit whether a branch has unexpected registry drift — e.g. before merging any review/meta or cli/ branch, or when a reviewer asks "did this PR touch anything it shouldn't?". Triggers: "check for drift", "drift check", "final summary", "pipeline done", "wrap up the pipeline", "fp-drift", "Phase 5".

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Measure CI churn cost for a pull request: how many commits and how many CI-compute seconds were burned on avoidable "fix the CI" push rounds. Use when you want to understand the efficiency cost of a PR's iteration cycle, get actionable pre-push check suggestions, or include a churn summary in a pipeline report. Triggers: "ci churn", "how much CI did we burn", "avoidable commits", "ci efficiency", "wasted CI time", "churn report", "/ci-churn". Called automatically by /fp-drift at pipeline close.

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Discovery-only global trending extension for Gaia. Snapshots configured external skill sources, prioritizes five-candidate pages for L4 review, and stops before trust, evidence, intake, or registry mutation.

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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.

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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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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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Locates candidate encryption keys in a binary by finding high-entropy fixed-size regions (16/24/32 bytes) near cryptographic constants or crypto API references, and surfacing printable key/passphrase strings. Activates for requests to extract encryption keys, find an AES key in a sample, or recover hardcoded crypto material.

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Profiles a threat actor by aggregating observed ATT&CK techniques into a TTP profile and comparing it against known-group technique sets to estimate similarity and candidate attribution. Activates for requests to profile a threat actor, compare observed TTPs to known groups, or estimate attribution from technique overlap.

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Detects sandbox and analysis evasion techniques in a sample by scanning static strings, imports, and API traces for VM artifact checks, timing/sleep stalls, anti-debug calls, and environment fingerprinting. Activates for requests to detect sandbox evasion, find anti-VM or anti-debug checks, or explain why a sample did not detonate.

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Sweep a PR's automated + human review comments and act on them — especially for material (large / logic-bearing) PRs. Handles "sweep the PR comments", "check PR review feedback", "address review comments", "did the bots flag anything", and the pre-/post-merge review pass on any non-trivial PR.

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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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Proactive architecture review — explore codebase structure, generate a self-contained HTML report with Mermaid diagrams and candidate improvements, then grill the findings. Use when planning a large refactor, onboarding to an unfamiliar codebase, or before a major architectural change.

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Orchestrate the release team: coordinates release-manager, qa-lead, devops-engineer, and producer to execute a release from candidate to deployment.

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Use when diagnosing structural maintainability problems in existing frontend code before deciding how to refactor it, especially when evidence, false-positive checks, and candidate transformations are needed without editing business code.

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Use when selecting or composing behavior-preserving refactoring techniques after structural evidence is known, especially when multiple candidate transformations, preconditions, or ordering choices must be compared.

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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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.

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Use when a task needs the judgment of a tenure-track/tenured Education Professor in a teacher-preparation program — designing a licensure course unit against InTASC/CAEP standards, deciding whether a candidate's borderline edTPA score needs a targeted or full retake, triaging a failing student-teaching placement, or assembling CAEP self-study evidence for reaccreditation.

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Use when a task needs the judgment of an audiovisual equipment installer/repairer — diagnosing an EDID or HDCP handshake failure between a source and a display, sizing a display's screen size and brightness for a room's viewing distance and ambient light, specifying an HDMI/HDBaseT/AVoIP signal run against its rated distance ceiling, designing conference-room speaker placement and acoustics, or drawing the install-vs-programming boundary with a control-system (Crestron/Extron/AMX) integrator.

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Discover and characterize regulatory elements (enhancers, promoters, silencers, insulators, super-enhancers) using ENCODE data and the cCRE catalog. Use when the user wants to find candidate regulatory elements, identify active enhancers in a tissue, map promoter states, classify chromatin states with ChromHMM, identify super-enhancers with ROSE, understand the functional validation hierarchy (CRISPR > MPRA > reporter), or characterize non-coding genomic regions. Covers the full workflow from ENCODE cCRE lookup through chromatin state segmentation to functional validation and enhancer-gene linkage. Use this skill for ANY regulatory element discovery, classification, or characterization task.

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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。