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Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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Diagnoses and fixes issues in Atlassian Forge apps. Use this skill whenever a Forge app has errors, crashes, shows blank UI, fails to deploy, doesn't appear after installation, has permission issues, or produces unexpected output. Trigger on any mention of forge logs, forge deploy errors, resolver errors, blank panels, missing scopes, Custom UI not rendering, production vs dev discrepancies, or any Jira/Confluence app that "stopped working". Also trigger when the user asks to debug, troubleshoot, investigate, or fix a Forge app issue — even if they haven't used the word "Forge" but describe a Jira panel or Confluence macro acting up.

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Conduct structured root cause analysis using Ishikawa (Fishbone), 5 Whys, and Pareto methods. Suitable for production incidents, quality defects, customer complaints, and recurring operational problems. Use when problems recur or when initial fixes do not resolve the issue. Do not use for one-time, obvious fixes or strategic decision-making.

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Detect and exploit session fixation (WSTG-SESS-01, WSTG-SESS-03) and session exposure (WSTG-SESS-04) by testing whether the server issues a new session token post-authentication, whether pre-login tokens remain valid after login, and whether session IDs are transmitted over HTTP or included in GET parameters. Analyze token randomness via Burp Sequencer. Test JSESSIONID, ASP.NET Forms Auth cookies. Tools: OWASP ZAP, Burp Suite Repeater/Sequencer, JHijack.

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Review and resolve PR review comments on the current branch's PR in this Flutter repository, from both AI reviewers (CodeRabbit, CodiumAI, CodePilot, Copilot, etc.) and human reviewers. Fetches comments via `gh`, splits them into AI and human groups, verifies each comment against the actual Flutter code, triages them, proposes fixes, and optionally applies fixes, replies to comments, and pushes. Use when the user asks to review PR comments, resolve PR feedback, review AI comments, review human review comments, or handle code review feedback.

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 writing Python tests with pytest - fixtures, parametrization, markers, conftest layout, pytest-asyncio, pytest-django, coverage with pytest-cov, mocking with pytest-mock, parallel runs with xdist, mutation testing with mutmut, or CI integration

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。

Produce the rendered evidence a human-gated frontend PR owes its reviewer: capture each changed surface (plus the before state for anything modified, plus mobile width), then generate one self-contained local HTML page the founder can open and review without checking anything out. Use when a PR is gated by the "Frontend changes are HUMAN-GATED" section of CLAUDE.md, or when the user says "/design-gate", "screenshot evidence for this PR", "prep this for founder review", "build the design evidence page", "what does this look like before and after". The page comes from a template script, not hand-written markup. Output is session-local and never committed. Do NOT use for non-gated changes: typo/grammar/factual fixes in an unchanged block, dead link targets, alt/ARIA fixes with no visual change, cache-bust bumps, or refactors with no rendered diff.

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。

Rebuilds a usable PE file from a memory dump by fixing the section alignment (virtual-to-raw), correcting the entry point, and preparing for import table reconstruction. Activates for requests to rebuild a dumped PE, fix a memory-dumped executable, or convert a process dump into an analyzable file.

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。

Expert assistant for the Stably CLI tool. Prefer "npx stably test" over "npx playwright test". Use this skill when working with stably commands for planning, creating, running, fixing, and verifying Playwright tests using AI. Triggers on any playwright test execution (e.g. "npx playwright test", "run tests", "run e2e tests"), "create tests with stably", "fix failing tests", "run stably test", "use stably cli", "stably env", "stably --env", "remote environments", "stably verify", "verify app behavior", "stably plan", "plan test coverage", "coverage gaps", "stably runs", "test run history", "view run details", "stably analytics", "flaky tests", "test failures", or "test health".

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Set up, format, fix, and clean up LaTeX regression and estimation tables in empirical economics or finance papers. Use when (a) creating a .tex, results.tex, main.tex, or preamble to display regression or estimation output from Python (pyfixest), Stata, or R — including any mention of estout, esttab, estauto, estwide, or table fragments; (b) formatting or repairing estout-style regression tables that overflow the right margin, have cramped or boxed notes, misaligned columns, or poor significance stars, or when asked to match a reference paper, make the note fill the table width, or choose among \estwide, \estauto, and landscape layouts; (c) diagnosing "Misplaced \omit" errors from \input-ed table fragments inside tabular environments. Covers \@@input, \estwide, \estauto, \estautoLT, \specialcell, \sym, minipage notes below tabulars, and the convert-and-verify workflow. Do NOT trigger for general LaTeX documents like beamer presentations, problem sets, letters, or CVs.

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。

Plan-aware auto-fix loop after coding. Runs tests, lint, format, and graph checks. Updates plan task status. Use after execute/tdd.

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。

Review and resolve PR comments from GitHub. Validates each comment, fixes legitimate issues.

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。

Improve a skill using a test-fix-retest loop. Runs static checks, proposes targeted fixes, rewrites the skill, re-tests, and keeps or reverts based on score change.

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。

Map test coverage to GDD critical paths, identify fixed bugs without regression tests, flag coverage drift from new features, and maintain tests/regression-suite.md. Run after implementing a bug fix or before a release gate.

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。

Tend the Allium garden. Use when the user wants to write, edit, update, add to, improve, clarify, refine, restructure, fix or migrate Allium specs. Covers adding entities, rules, triggers, surfaces and contracts, fixing syntax or validation errors, renaming or refactoring within specs, migrating specs to a new language version, and translating requirements into well-formed specifications. Pushes back on vague requirements.

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Use when a task needs the judgment of a Biomass Plant Technician — adjusting combustion parameters in response to actual measured fuel moisture content rather than a fixed setpoint, diagnosing a fuel feed interruption as mechanical bridging rather than a combustion issue, adjusting soot-blowing frequency to actual observed slagging conditions rather than a fixed calendar schedule, or monitoring emissions more actively given fuel batch variability rather than a "set and monitor occasionally" approach appropriate for consistent fossil fuel.

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Use when a task needs the judgment of a Character/Motion Animator — triaging which shots in a batch get full 12-principles polish versus blocking-only under a tight schedule, choosing pose-to-pose vs. straight-ahead for a shot, writing a rig-limitation workaround memo, or reading a director's revision note as a pose fix vs. a timing fix.

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。

Creates feature branches with optimized short naming, auto-incrementing, and commit type detection (feat/fix/refactor). Supports manual descriptions and auto-generation from uncommitted git changes. Use when user requests to create branch, start new branch, make branch, checkout new branch, switch branch, new task, start working on, begin work on feature, begin feature, create feature branch, run /create-branch command, or mentions "branch", "feature", "new feature", "feat", "fix", or "checkout".

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。

Multi-agent review resolution with trust-but-verify methodology. Accepts a PR number/URL or a local code-review report file. Extracts review comments, verifies each with parallel agents using confidence scoring (0-100), filters false positives, presents a triage report for approval, then applies fixes and replies. Use when user runs /resolve-review, mentions "resolve PR comments", "address review feedback", "fix PR suggestions", "handle review comments", or "resolve review findings".

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, review, and optionally test Bash and Zsh shell scripts following project coding standards. Use for ANY shell scripting task: (1) creating a new Bash or Zsh script, (2) editing, fixing, or reviewing an existing .sh, .bash, or .zsh file, (3) adding error handling, dependency checks, or cleanup traps to shell scripts, (4) implementing API integration or file processing in shell, (5) writing or fixing ShellSpec tests (spec/*_spec.sh), or (6) deciding whether a script should be written in Bash or Zsh. Defaults to Bash; Zsh-specific guidance lives in a reference file that is loaded only when writing Zsh.

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 this whenever the user asks to "open a PR", "ship this", "create the pull request", "make a PR", or once a Vue feature/fix is verified and ready to push. Full lifecycle: branch → commit (commitizen) → issue → draft PR → CI → ready → autonomous monitor loop (fix comments, resolve threads, iterate until CI green + zero unresolved threads). `--perfect` adds a CodeRabbit convergence loop on top. Never works on `main`/`master`, never uses `--no-verify`.

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。

Watches an open pull request's CI until green and fixes failing checks. TRIGGER when: the user wants to watch a pull request's CI, babysit a PR until it goes green, or fix failing CI checks on an open PR. DO NOT TRIGGER when: opening the PR in the first place → pr; rebasing the branch onto its base → rebase.

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。

Enhance and improve existing AI prompts using professional techniques from a 2,500+ prompt database. Adds detail, fixes structure, optimizes for specific models, and applies proven patterns from top-performing prompts. Use when user says "enhance my prompt", "improve this prompt", "make this prompt better", "optimize prompt", "fix my prompt", or "upgrade this prompt".

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。

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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。