「verdict」の検索結果

Before dispatching a multi-agent team, run a spectral diagnostic on the proposed communication graph and emit a (ρ, Δ, κ) coordination signature plus a pass/fail verdict against per-task-class thresholds. Builds the row-stochastic operator P from the team graph, computes the successor representation M = (I − γP)⁻¹, and ranks the topology for robustness (κ, condition number), consensus (Δ, spectral gap), and drift (ρ, spectral radius). Per Parks & Alharthi (arxiv 2605.11453), rank order on (κ, Δ, ρ) predicts coordination quality pre-execution with rank correlations of 1.0 / 0.5 / −1.0. Triggers: "dispatch the team", "team topology check", "before running the agents", "is this team configuration OK", "team pre-flight".

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Invoke the rubber duck reviewer for a second opinion. Use after planning, after complex multi-file implementations, or after writing tests. Produces a focused CRITICAL/WARN/INFO list and a APPROVE/REQUEST_CHANGES verdict.

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Enforcement layer for chunk and story validation. Invokes the chunk-validator agent (haiku), enforces the verdict, routes failures to refine-chunk or test-fix, captures CLI learnings. The orchestrator invokes this skill — never the agent directly.

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Use when reviewing a skillsaw PR. Dispatches 7 specialist reviewers (Architecture, Python Expert, Security & Supply Chain, QA Engineer, Technical Writer, Ecosystem, Slopinator) as parallel sub-agents by default, then synthesizes a single verdict. Use --serial for cheaper inline execution.

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Use when a diff needs four parallel perspective reviewers (Security, Performance, Quality, Accessibility). Creates an ephemeral SAM plan, collects structured verdicts, synthesizes them into one deduplicated cross-referenced punch list, prints one summary line per perspective, and exits non-zero if any perspective returns REJECT. SKIP is a passing outcome.

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Verifies claims in backlog items, skill documentation, or plugin content against primary sources using web lookups. Spawns parallel verification agents that must use WebFetch/WebSearch/gh — training data recall is explicitly rejected as evidence. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Use when items are marked UNVERIFIED or when verifying tool API claims, CLI flags, or documented software behavior.

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Builds or audits a literature review, producing an evidence map, the closest prior work, source clusters, a gap verdict, and a synthesis plan for the introduction. Use when the user asks whether a contribution is novel, who has studied a question, what the literature establishes, or how to organize a review, or wants a reading list, Zotero export, or pile of papers turned into one. For systematic reviews or meta-analyses it produces the protocol scaffold and routes registration (PROSPERO or OSF) and screening to dedicated tools.

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。

Builds or audits a literature review: evidence map, closest prior work, gap verdict, and synthesis plan. Use to check whether a contribution is novel, map what a literature establishes, or organize a review; systematic reviews get a protocol scaffold.

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Аудит пользовательской инсталляции IWE. Запускает scripts/iwe-audit.sh + MCP healthcheck + smoke-test ритуала через sentinel-механику (контракт dry-run-contract.md), передаёт отчёт subagent'у в роли VR.R.002 Аудитор (context isolation) → verdict ✅/⚠️/❌ по 6 компонентам (Inventory, L1 drift, {{GOVERNANCE_REPO}}, L3 customizations, MCP, ритуал). Используй после restore из бэкапа, после update.sh, или при еженедельной сверке.

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Investigate one Anabasis v2 campaign from its log and turn what it shows into findings with one owner each. Answers whether a comparison's reading (step, no-step, not-scored) may be believed, whether a fail was the solver's or the exam's, whether the harness could know the exam, whether the tasks bracket the solver, whether the campaign is climbing, and where the hours and dollars went. Reads evidence; starts no solve, session or round, and changes no verdict.

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Resolve the Git merge conflicts of a working directory -- of an in-progress merge, rebase, cherry-pick, revert, stash apply, or of conflict markers left by patch -- carefully and semantically, never losing any change, escalate unresolvable hunks as-is, continue the in-progress operation, and emit a RESOLVED, PARTIAL, NONE, or FAILED verdict. Use when the user calls to "resolve" or "fix" merge conflicts.

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Merge a Git source branch, including its still uncommitted changes, into a target branch through a regular merge, a rebase with fast-forward, or a squash merge, resolving merge conflicts semantically, check that the source branch landed, and emit a MERGED, CONFLICT, or FAILED verdict. Use when the user calls to "merge", "integrate", or "land" a branch.

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Perform a holistic, human-reviewer-style critique of the currently staged Git changes (or, with `--working-copy`, of all uncommitted changes, with `--branch`, of a branch against its base, or, with `--commit`, of a single commit) and emit an approve/reject verdict with prioritized, severity-tagged, line-cited findings. Use when the user wants the staged diff, a branch, or a commit "reviewed", "critiqued", or "code-reviewed".

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Rates any SKILL.md on a 0-100 scale across 10 dimensions with a SHIP / REWORK / SCRAP verdict. Evaluates trigger precision, instruction clarity, output predictability, edge case coverage, anti-hallucination guardrails, developer experience, composability, open-source readiness, wow factor, and real-world utility. No flattery — calibrated against Anthropic's own skill-creator rubric. Use this skill whenever someone says "score this skill", "rate my skill", "is this skill good", "skill review", "skill audit", "roast my SKILL.md", "grade this", "will this skill work", "evaluate my skill", "how good is this", or pastes a SKILL.md and asks for feedback. Also trigger when comparing two skills, benchmarking a skill collection, or asking "what's wrong with this skill" — even without the word "score".

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。

Evaluates completed agent pipelines across 5 scoring dimensions and produces a composite verdict with actionable recommendations

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Onboarding workflow for cross-library KB query in a consulting practice. Discovers existing user-scope library registrations, prompts for additions, validates each library, helps activate the relevant ones for the current project, runs a smoke test, and reports a clear "ready" or "issues to fix" verdict. Use --verify-only to skip prompting and just validate existing setup (sub-task 23 of

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Trains the HUMAN user's critical thinking through four modes: drill (argument-analysis items with a single defensible answer, judge stance), scene (Socratic exploration of synthetic scenes or user-supplied material, no verdicts on interpretations; includes a configure track — design the information request and verification plan for a decision before any analysis), expedition (guided audit of impossible-tier problems from verified packs), and detective (a runtime-generated multi-layer case worked as an escape room, guide-and-judge stance). Use when the user wants to practice critical thinking, analyze arguments, hunt assumptions, examine bias, train reasoning, or decide what information a decision needs first. Triggers: critical thinking practice, train my thinking, drill, scene, byom, configure, information plan, what would I need to know, spot manipulation tactics, scam literacy, 批判思考練習, 話術辨識, detective, 查案, 破案, 偵探.

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Local speech-to-text on Apple Silicon with two cross-checking MLX engines: NVIDIA Parakeet TDT v2 (parakeet-mlx) and Qwen3-ASR 1.7B (mlx-audio, hotword biasing). Batch JSONL with word timestamps, confidence, provenance, and an agreement verdict. No NeMo or PyTorch. Use when transcribing audio files, identifying game or legacy voice clips by content, telling speech from SFX, serving an OpenAI-compatible STT endpoint, or dictating. For push-to-talk into any text field, use the Handy app.

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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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Evaluates whether work-in-design is ready to hand off from a chat design conversation to autonomous execution (Claude Code, n8n, agent runtime). Runs a 7-condition gate: spec stability, verification surface, invariants, pump-primer, work decomposition, rollback cost, token budget headroom. Three invocation modes — deliberate (caller provides work_context, returns JSON verdict), proactive sensing (Claude detects handoff signals in design conversation and offers the check), conversational walkthrough (Claude walks the 7 conditions as discussion topics). Returns structured JSON in all modes. Use when designing a Claude Code deployment, scoping an autonomous run, or deciding next move. Trigger on: "ready to hand off," "ready for execution," "is this CC-ready," "handoff check," "walk me through readiness," "are we ready to ship this," "should I just hand this off," or proactive detection. Do NOT use for evaluating completed work, runtime selection, or prompt quality. Profile-agnostic.

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Adversarial, security-first review of a pull request from an EXTERNAL or untrusted contributor — a fork PR, a first-time or unknown author, anyone without write access — before a maintainer merges it. Use this whenever you're reviewing, triaging, or deciding whether to merge a PR opened against tbh-meter by someone outside the maintainer team: 'review PR #123', 'someone opened a PR — is it safe to merge?', 'check this contributor's changes', 'can we take this fork PR?', 'is this contribution legit?'. This is supply-chain DEFENSE, not ordinary code review: every merged PR auto-updates onto every user's machine and ships a memory-reading .exe plus an unsandboxed Electron app, so the author and the PR description are UNTRUSTED and the diff is the only evidence. It posts its verdict as a real GitHub review — an inline comment pinned to each problematic line (via the pulls/reviews API), not just one summary comment. NOT for your own or a fellow maintainer's branch (use self-review for that), and prefer this over the generic review flow for anything coming from outside the team.

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Drive a GitHub Copilot (or any bot reviewer) pull-request review to a clean pass without babysitting it: fix each finding in its own commit, reply to the review thread with the fix sha, resolve the thread, re-request the bot, and poll for the async re-review. Covers the thread node-id (PRRT_...) vs comment databaseId distinction, the resolveReviewThread GraphQL mutation, the copilot-pull-request-reviewer[bot] slug, and how to read the bot's verdict (COMMENTED plus a "human review recommended" banner is not a blocking finding). Use when Copilot has left review comments on your PR, when bot review threads must be closed out with an auditable fix-reply-resolve trail before merge, or when you need to verify that a re-review actually landed on the new HEAD rather than the old one.

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Bring how named experts would think about the question. Use when the user wants expert thinking on a tradeoff, choice, or stuck problem — not a verdict.

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Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability triage.

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。