「rev」の検索結果

Configure Claude Code permissions — tool approval rules, permission modes, managed policies, and sandboxing. Use when setting up permission rules, configuring allow/deny/ask policies, debugging permission prompts, deploying managed settings for organizations, or controlling Bash/Read/Edit/WebFetch/MCP/Agent tool access.

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。

Cross-platform hooks reference for AI coding assistants — Claude Code, OpenAI Codex, GitHub Copilot, Cursor, Windsurf, and Amp. Covers hook authoring in Node.js (ESM and CJS) and Python, per-platform event schemas, inline-agent hooks and MCP in agent frontmatter, common JSON I/O, exit codes, best practices, and a fetch script to refresh docs from official sources. Use when writing, reviewing, or debugging hooks for any AI assistant.

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。

Format and score completed plugin assessment findings, including whether evidence admits a finding to the requested outcome. Use when a plugin assessment or review needs the standard report structure, scoring criteria, severity breakdown, marketplace-readiness result, or finding admission.

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。

This skill should be used when the user asks to "validate Perl script", "check Perl syntax", "verify Perl code", "/perl-validate", or mentions script validation, compile check, security review, or best practice compliance for Perl code.

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。

Compatibility routing for callers that previously used the holistic-linting orchestrator workflow. Use only when an existing caller explicitly invokes this skill.

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。

Rigorous self-assessment checklist before marking any task as complete. Use when about to claim task completion, before final commit, when user asks "is it done?", or when transitioning from implementation to reporting. Prevents premature completion claims by requiring evidence for every assertion.

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。

Compare explicit design/specification contracts with the implementation in a defined change scope. Use to verify signatures, types, schemas, invariants, and other stated contracts without expanding into general code quality 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。

Use when building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-verified v3 gotchas — zero speculation.

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。

Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

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Same reverse-thinking method as dh:rt-ica (see dh:rt-ica or dh-glossary for the definition), but localizes any MISSING input to the affected task only, as a non-blocking information-completeness pre-pass before task decomposition and plan generation. Use when grooming backlog items, generating plans, decomposing tasks under uncertainty, or working in brownfield and refactor scenarios. Produces completeness summary (APPROVED-FOR-PLANNING, APPROVED-WITH-GAPS, or BLOCKED-FOR-PLANNING), missing input report with dependency mapping, required unblock actions, and planning annotations for downstream tasks. Use dh:rt-ica instead at the S2 implementation gate where missing inputs must halt execution.

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 all tasks for a feature are marked COMPLETE — runs holistic quality gates including code review, feature verification, integration check, documentation drift audit and update, and context refinement. Creates follow-up plans when issues are found.

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Certifies that a feature achieves its original objectives via goal-backward verification (SAM Stage 7). Use when all tasks pass forensic review — starts from expected outcomes, works backwards to verify each was achieved, and returns CERTIFIED or NOT_CERTIFIED with specific gaps.

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。

Enforce anti-AI UI design rules based on the Uncodixfy methodology. Use when generating HTML, CSS, React, Vue, Svelte, or any frontend UI code. Prevents generic AI aesthetic — soft gradients, glassmorphism, hero sections in dashboards, oversized rounded corners, and decorative copy. Applies constraints from Linear/Raycast/Stripe/GitHub design philosophy for functional, honest, human-designed interfaces. Triggers on UI generation, dashboard building, component creation, CSS styling, landing page design, or any task producing visual interface code.

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Manages the research/ knowledge base of verified tool and library research entries via knowledge-explorer.py. Commands — list (browse by category or SDLC layer), fetch-github (draft entry from GitHub repo), add (validate and route to category dir), update-append (append dated revision), generate-descriptions (repair missing or low-quality descriptions), migrate (convert inline-header entries to frontmatter). Use when browsing KB topics, adding or updating research entries, fetching GitHub repo metadata, generating descriptions in parallel via Haiku subagents, or migrating old-format entries to skill-spec frontmatter.

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。

Transforms validated /linear-walkthrough artifacts into one presentation-ready deck outline per major codebase component. Use when the user asks to create a presentation, prepare a walkthrough deck, build an onboarding deck, produce an architecture review deck, or summarize a codebase for a technical audience. Requires walkthrough directory output -- reads unified walkthrough, per-section files, validation reports, coverage maps, entry points, and open questions -- then orchestrates four parallel agent phases to produce slide outlines with speaker notes, evidence references, and suggested visuals.

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This skill should be used when analyzing Claude Code session transcripts, reviewing agent performance, finding anti-patterns or tool misuse, mining workflow patterns, running kaizen analysis, debugging agent behavior, or performing session forensics.

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 a skill from official documentation through categorized research, citation review, integration, and final validation. Use when researching a tool or library before creating a skill, when "research for skill" or "create comprehensive skill" is requested, or when extensive multi-source documentation gathering is needed.

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。

Decompose substantive work into phases, dispatch each phase to a sub-agent, and adjudicate what comes back. Use whenever a request asks for implementation, investigation, a fix, a review, or any change to files — including small ones — and whenever you are about to read source or run a diagnostic yourself instead of handing it off. Also use when a report from a sub-agent needs judging, when a phase needs re-dispatching, or when a user names one instance of a pattern. Does not apply when your own prompt begins "Your ROLE_TYPE is sub-agent." — then follow references/sub-agent-contract.md instead.

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。

Modern Git workflows, best practices, and commands. Use when the user asks about Git branch management (git switch vs checkout), file restoration (git restore), fixup commits, autosquash rebasing, git worktrees, rerere, force-with-lease, repository cleanup (git clean, stale branches, bloat analysis), history navigation (revision syntax, range notation, pickaxe search), recommended global git config, or GitButler and the but CLI.

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。

Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.

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Runs a deliberative two-model committee of GPT-6 Astra and Claude Opus under a selectable chair, Fable by default, with `/model-committee-astra`, `/model-committee-opus`, and `/model-committee-sol` selecting the others. Use when a consequential decision has several defensible options and two model families should propose, critique, revise, and cross-rank them against a predeclared rubric before converging, as in research design, interpretation, architecture, manuscript strategy, or evaluation design. Not for factual lookups, independent-coder reliability, brainstorming, routine implementation, or final professional judgment.

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Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

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Designs survey instruments, covering question wording, scale construction and index reliability, flow and treatment placement, pretesting and soft launches, respondent burden with attention and speeding rules, sensitive questions and social-desirability bias, and treatment delivery. Use when the user writes or revises survey questions, chooses a scale, orders blocks, plans a pilot, or worries respondents will not answer honestly. Indirect measurement goes to list-experiment, multi-country instruments to cross-national-design, and live-survey operations to qualtrics-ops.

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。