991건 · 관련도순
Run an independent read-only Kimi review over the current working tree or a branch diff. Use when the user wants a second reviewer for defects, regressions, or implementation risks, not edits.
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Validate SEO and LLM-readability claims against live HTML, local fixtures, and generated artifacts. Use this skill when the user asks to prove whether title, meta description, canonical, hreflang, JSON-LD, NewsArticle.image, OpenGraph, Twitter cards, RSS autodiscovery, robots.txt, llms.txt, sitemap, news-sitemap, crawler access, or performance evidence is actually present, and when subjective audits must become timestamped pass/fail reports.
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Independent re-derivation gate for proposed changes during autonomous execution. Evaluates a change (code diff, config edit, engine evolution, schema change) against the work's authority matrix and a 4-check protocol: invariant compliance, scope authorization, detection narrowness, and regression risk. Returns structured JSON with pass/fail per check, an overall verdict (APPROVE / REQUEST_CHANGES / REJECT), and blockers. Profile-driven thresholds (strict for unattended runs, lenient for desk supervision). Use when an autonomous agent proposes an engine change, when reviewing a Claude Code-authored modification before merge, or when an unattended profile requires independent re-derivation. Trigger on: "review/approve this proposed change," "critic-gate this," "is this change safe," "is this safe to merge," "should I let this land," "run the critic." Do NOT use for handoff-readiness checks (use rootnode-handoff-trigger-check), general code review, or design-time correctness. Authority matrix required.
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Audit RuField tests, trust boundaries, privacy projections, and evidence promotion. Use for conformance review, security regression review, release validation, or checking whether field accuracy claims have support.
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Validate RuField CSI or ultrasonic recording replay and its fusion boundary. Use for captured CSI JSONL, BatVu ultrasonic replay, replay adapter integration, or replay regression diagnosis.
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Use when asked to optimize code, make a path faster, make this as fast as possible, reduce allocations, repair a regression, profile a target, grill every inefficiency, or run a subsystem or repo-wide performance campaign against a measured floor; when a performance requirement, slowness report, Core Web Vitals miss, or profiling evidence identifies a bottleneck; or for an "extremely optimize" performance campaign, optimizing suspected hot paths without waiting for benchmarks, or estimating hot and complexity-neutral cold paths while refusing complexity theater. Not for remote, credential, publish, deploy, or irreversible changes.
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Use when a bug, failure, flake, regression, review finding, or ticket needs the core fixed so it cannot recur. Not for greenfield features: use tdd. Not for style-only review or typo-class one-liners.
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Design or implement the smallest complete executable evidence for a governed Proto UI behavior. Use for conformance cases, regression tests, cross-layer verification, test-entity mappings, failure-path coverage, or validation-plan design. Do not use tests to decide unsettled semantics.
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Implement one governed Proto UI Adapter-profile realization slice. Use when target and runtime range, supported or omitted Module role, Host Capability fidelity, lifecycle translation, failure behavior, compatibility boundary, and evidence scope are fixed by current authority. Use pui-adapter-assess for research or readiness assessment and pui-regression first for a governed failing behavior.
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Fix a bug or problem. Supports fix-now or plan-first; no args executes FIX_PLAN.md. When a regression check is needed, reproduce first, then implement and rerun it. Adds logging and suggests extra coverage.
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Fix a bug or problem. Supports fix-now or plan-first; no args executes FIX_PLAN.md. When a regression check is needed, reproduce first, then implement and rerun it. Adds logging and suggests extra coverage.
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Verify NowStack Mobile changes across web, iOS, Android, and Convex with real runtime evidence. Use for `/ns verify`, cross-surface regression checks, acceptance proof, or release verification.
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QA pipeline: explores the running app like a real user using a browser, then produces a QA report and Playwright E2E tests for regression. Orchestrates app-scout → qa-agent.
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Use when reviewing a pull request in this repository — check the description against the PR template section by section, then run a separate regression and user-impact pass that produces impact levels and a review summary.
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Build evaluation frameworks for LLM systems — harness design, graders, datasets, regression tracking, and human-in-the-loop eval. Use when you need systematic measurement of model or agent quality.
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Test frontend code: unit, component, E2E, visual regression, and accessibility tests with a sane pyramid. Use for reliable UI quality.
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Statistical methods for biology and medicine — study design, hypothesis testing, regression, and survival analysis.
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Evaluate AI agents rigorously — task benchmarks, success criteria, failure taxonomy, cost/latency tracking, and regression testing. Use when you need to know whether an agent actually works, not whether it demos well.
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Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
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Perpetual autonomous work loop for Claude Code — no end condition, no memory regression, no context overfill. Maintains a persistent ledger across all sessions.
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Read every application.yaml in the workspace and report the state of the search — the ranked queue, what is waiting on you, what has gone quiet, and the regression of the pipeline's own predictions against actual outcomes. Writes the calibration findings back into job_search.md and preferences.yaml.
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MATLAB testing, validation, and CI workflow for matlab.unittest, MATLAB Test, Simulink Test, coverage, artifact generation, GitHub Actions, and open-source reproducibility. Use whenever MATLAB work needs verification, regression tests, CI, GitHub publication, or acceptance criteria.
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Investigate and fix bugs, failing tests, regressions, build failures, or unexpected behavior using boundary localization, evidence, and bounded hypotheses.
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Strengthen test coverage around changed behavior, edge cases, and regressions without writing noisy or brittle tests.
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