테스트·보안 스킬

Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation — verdict-bearing at L1 (the LaTeX source; PDF-extracted math is unreliable, so an L0 PDF-only run surfaces info only) — never asserts 'fabricated', only that the step shown does not hold. A fresh cross-model reviewer reads the theorem/proof + an extraction-only obligation scaffold and proposes per-obligation findings, each span-anchored to the evidence ledger (claims.json); reviewer≠adjudicator. Emits proof-derivation-forensics.findings.json; NEVER computes the verdict. dimension=proof, can be critical. Triggers: "proof forensics", "check this proof", "derivation integrity", "audit the math", "证明审计", "推导有没有漏洞".

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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".

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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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Lighthouse audits and Core Web Vitals — LCP, CLS, INP. Use when optimising page load, layout shift, input delay, images, fonts or render-blocking resources.

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Orchestrator that routes a UI/UX task through brand, tokens, layout, components, polish and accessibility. Use for multi-concern work such as a design review, UI build or interface audit.

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Nielsen's 10 usability heuristics. Use when auditing an interface, designing interaction flows, writing error messages or reviewing usability.

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Use when the user wants a Xiaohongshu posting calendar, weekly review board, first-10-post launch plan, 10-20-post positioning test, or data-based next-step plan. Trigger phrases: "排期", "复盘", "30 天计划", "前10条", "10-20条测试", "发了没数据", "weekly review", "content calendar", "XHS schedule". Do not use for first-draft editing or initial account positioning.

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Calibrated grilling session for stress-testing a plan, design, idea, or decision. First assesses the user's topic knowledge, confidence, and desired pressure level, then asks one question at a time with recommended answers. Use when user says "grill me", "stress-test this", "challenge my plan", "interview me", or wants a plan probed without being overwhelmed.

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Black-box QA audit of slop-guard across MCP, CLI, fit, docs, agent workflows, and writing-effectiveness. Files GitHub issues for real problems found.

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Audits font loading, type scales, measure, spacing, OpenType, and rendered punctuation with 78 scoped rules. Use when asked to "audit typography", "fix the fonts", or "review my type system". For a new visual direction use ui-design Direction; for general UI defects use ui-design Audit.

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Reviews a local diff, branch diff, or PR with file:line findings in confirmed and plausible tiers, gates any test the diff adds or changes, and in apply mode lands the fixes and diff-scoped simplifications. Use when asked to "review my changes", "code review", "tidy this", "simplify my diff", "deslop this", "structural review", "security audit", or "audit these tests in my PR". For the PR itself use pr-creator; for CI and review threads use pr-babysitter; for UI defects use ui-design; for repo architecture use codebase-architecture; for pruning the whole suite to a target use test-audit.

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Prunes low-value tests across a whole suite to a measured target while holding coverage. Use when asked to "remove useless tests", "prune the test suite", "we have too many tests", "agents keep writing pointless tests", or "cut 20% of tests without losing coverage".

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Creates and reviews executable implementation plans grounded in repository evidence, with vertical slices, explicit decisions, and verification criteria. Use when asked to "plan this feature", "stress-test this plan", "grill me", or "split this into tickets". For architecture use codebase-architecture; for code review use tidy.

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Audits libraries, CLIs, and SDKs using 38 rules for public contracts, package exports, piped output, errors, and configuration. Use when asked to "audit my CLI", "review my SDK", or diagnose package type resolution.

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Speeds up CI and the merge path: finds where changes wait between push and deploy, then fixes the critical path, check placement, merge queue or post-merge checks with auto-revert, flaky tests, and deploy rate. Use when "CI is slow", "speed up CI", "do we need a merge queue", "main keeps going red", "flaky tests block merges", agents open PRs faster than they merge, or building a self-healing merge loop.

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Audits agentic products for tool parity, authority, approval payloads, recovery, and trust using 24 rules and a ship verdict. Use when asked for an "AX audit", to review an agent approval flow, or whether an agent can operate the product.

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Audits and edits agent instruction files, verifies repository commands, and migrates repositories to AGENTS.md as the single shared source. Use when asked to "improve my AGENTS.md", "migrate CLAUDE.md to AGENTS.md", or make instructions work across agents.

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Creates and improves portable Agent Skills with a validator, routing scenarios, and evidence-based keep, cut, merge, or retire decisions. Use when asked to "write a skill", "update all skills", "audit my SKILL.md", "remove redundant instructions", or fix skill triggering.

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Closes a large backlog of findings (a gap analysis, an audit, or 10+ bugs) in prioritized waves. Loops through triage, dependency analysis, parallel fix streams, a test gate, and re-audit until the target is met. Use after a gap analysis, audit, or review produces 10+ findings, or to burn down a large bug or tech-debt backlog.

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Enforces tiered test coverage standards with three dimensions — line coverage by tier, spec-to-test traceability, and spec-to-code implementation mapping. Use before claiming code is complete.

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Generates a behavioral scenario acceptance matrix for comprehensive test coverage planning. Maps user scenarios to acceptance criteria with stable IDs. Use when planning test coverage for a complex feature, before a release or milestone review, or when someone asks what is tested.

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Scans staged changes for secrets, debug statements, TODOs without references, and other common commit mistakes. Use before creating any commit.

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Strips AI-generation "smell" from prose before it ships (READMEs, docs, release notes, PR descriptions, posts, emails). Counts measurable tells first (keyword saturation, tricolons and anaphora, the "not X, it's Y" reflex, fragment-for-emphasis, bolded thesis lines, listicle-ization, signpost phrases, filler vocabulary, em-dashes, engineered-quotable closers), then applies a "doing work or reflex?" test to each instance so genuine voice survives. Use when asked to de-slop text or make it sound human, when text reads as machine-written, and as the final pass on prose the agent drafted.

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