「check」の検索結果

Curate .unikit/MCP-RECHECK-NOTES.md — the project's log of findings about the configured engine MCP server. Four jobs: re-stamp (the server moved, so every entry is suspect), replay (reproduce a `replay: safe` finding inside a disposable sandbox to see whether it still holds), retire (offer to drop what was fixed or went upstream), and upstream (print a ready diff for the packaged INDEX.md). Use for "audit the MCP notes", "are these findings still true", "recheck the MCP traps", "clean up MCP-RECHECK-NOTES", "the server was updated — revisit the notes". Replaying mutates a live editor, so it starts by telling you how to prepare it — save your scene, open an empty one — and asks once before touching anything. Everything it creates lives under a single UNIKIT_AUDIT_<runid> prefix, is deleted in one action, and the scene is never saved. To record a NEW finding use /unikit-mcp-trap instead.

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Diagnose and test Claude Code skills against Anthropic's 7 principles. Scans SKILL.md files, checks 8 rules (gotchas, description, allowed-tools, file-size, structure, frontmatter, conflicts, usage-hooks), classifies skill types, generates prescriptions, and runs eval tests. Use when checking skill quality, auditing skills, testing skills, or before publishing skills. Triggers on "스킬 점검", "스킬 진단", "스킬 테스트", "check skills", "audit skills", "test skills", "skill health", "pulser", "pulser eval".

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Check the local environment for Spec Kit by running `specify check` (and `specify version`). USE FOR: verifying required tools are installed, diagnosing a broken spec-kit setup, reporting CLI version/feature capabilities. DO NOT USE FOR: installing or upgrading the CLI itself (use the speckit-self 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。

One-shot GEO visibility check for a brand using Xpoz. Runs the buyer questions that matter through Claude, ChatGPT, and Gemini, and reports where the brand appears, who wins instead, and which surfaces the answers are assembled from. Use when asked to "check my AI visibility", "does ChatGPT recommend us", "GEO audit", "where do we show up in AI answers", or "who wins our category in AI answers".

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Internal utility for the skills library itself — opt-in, local-first, privacy-respecting usage telemetry and update checks. Provides version checking, usage logging, and usage reporting for the skills in this repo. Use only when asked to view skill usage stats, configure telemetry opt-in/out, wire a skill to log its own runs, or check for skill updates.

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Unified verification engine for Python data science projects. Covers environment checks, type checking, linting, tests, security scans, code review with DS anti-patterns, and notebook checks. Commands (/verify, /quality-gate) invoke different subsets of this skill.

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This is the MistEye security gate skill. It is triggered by pre-installation risk checks (including Skill/MCP dependency manifests), pre-access security checks for domains or URLs, IoC malicious detection, and supply-chain risk blocking, especially for common requests such as "take a look at this address/website." It must use `https://app-api.misteye.io/functions/v1/detect`; in dependency and external-link scenarios it has the highest priority. If the API returns `status="malicious"` or any `matches`, the action must be blocked and explicitly reported as "blocked". Only after the first installation is complete should the user be reminded that daily patrols can be enabled with OpenClaw or Hermes (default once per day).

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Security launch audit for a public web app BEFORE you ship or hand it to a client — "vibe coders are getting sued", systematized. An executable pipeline: classify (static / full-stack / AI) → run sections (secrets+supply-chain, DB, auth, infra+headers, legal, LLM/injection) orchestrating deterministic tools + manual checks → GO/NO-GO report. Adapted from Prajwal Tomar's playbook, extended with prompt-injection / supply-chain / secret-history / dependency-CVE layers. NOT the same as pre-push (that = clean push; this = is the product itself safe to launch). Triggers — "security checklist for the site", "audit before launch", "is the site safe", "check before handing to client".

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Autonomous goal loop — give it ONE goal plus how to verify it, the agent runs check→fix→check itself and comes back only with the finished result (code with audit, research with fact-check, content with critique, audit/backtest). Self-verification instead of constant supervision, with hard safety guards. Vary the loop with a verification rubric designed before it runs. Triggers — "work autonomously to the result", "don't come back until it's done", "run the loop yourself", "drive it to done".

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Verification checklist to prevent fake success. Detects test deletion/disabling, coverage regression, lint bypass, secret exposure. Triggered by "deception", "fake" ("가짜"), "fake success", "verification" ("검증"), "checklist" ("체크리스트"), "deletion detection" ("삭제 탐지"), "regression" keywords.

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Linting and code quality workflow for Software Metrics Machine. Covers ESLint 9.x flat config, Prettier formatting, lint-staged (auto-fix on commit), per-workspace configs, TypeScript strict mode, and all lint commands. USE FOR: lint, eslint, prettier, format, code style, linting, lint fix, auto-fix, check style, code quality, lint-staged, typecheck, type-check, tsc noEmit. DO NOT USE FOR: writing tests (use tdd skill), building (use build commands), commit hooks setup.

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Produces internal case viability and conflict check memos for personal injury litigation intake. Screens conflicts, assesses liability and damages, checks statute of limitations, and outputs an accept/decline recommendation. Use when evaluating new PI intake, running conflict checks, assessing case merit pre-filing, or preparing acceptance memos.

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Automatic quality control, linting, and static analysis after code modifications. Triggers on: lint, format, check, validate, types, static analysis. NOT for full build verification (see mk:verify); NOT for test coverage (see mk:testing).

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Check a prototype against its original design direction and requirements before shipping. Compares implementation to the mockup/spec/context, flags drift in layout, spacing, type, color, states, and behavior, and reports a pass/fix list. Use when the user says "design QA", "does it match the design", "check against the spec", "conformance check", or before sharing/handing off a prototype. Part of the Product Design set. Uses a browser tool for visual comparison.

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。

Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

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Generates comprehensive, context-aware code review checklists tailored to the specific codebase, programming language, and team standards. Analyzes PR diffs and suggests what reviewers should focus on.

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Design and implement systematic checklists that reduce errors by 30-50% in complex, high-stakes domains (medicine, aviation, construction, finance). NOT for simple tasks or when comprehensive instruction is needed.

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Passive domain reconnaissance using Python stdlib. Use this skill for subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys required. Triggers on requests like "find subdomains", "check ssl cert", "whois lookup", "is this domain available", "bulk check these domains".

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 code against a standard checklist covering security, correctness, performance, and readability. Use when asked to review code, check a PR, or audit changes.

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Investigate and fix failing CI. Takes a PR reference, one or more CI run/job URLs, or freeform text describing where the failures are, then enumerates failing workflows/jobs/steps, determines the root cause (often one shared cause behind several red checks), proposes a fix plan, applies code changes, commits, pushes, watches the new runs, and repeats until checks pass. Use when the user says "fix CI", "CI is red", "checks are failing", "the build is broken on my PR", "fix the failing jobs", or pastes a failing CI run URL. Do NOT use for authoring new workflows, or for read-only diagnosis with no fix intent — use gha-log-reader for that.

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Use when verifying a story or ticket implementation against its spec and acceptance criteria - per-AC verdicts with file:line evidence and proving tests, code reuse and design conformance, dead code, stale docs, comment discipline. Finds the spec from an explicit path, the ticket key in the branch name, or plan files - and asks rather than guessing. For defect-hunting code review use dotnet-techne-code-review instead. Keywords: story QA, acceptance criteria, spec conformance, AC coverage, definition of done, dead code check, reuse check, comment check, verify story, QA this branch.

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[Utilities] Use when you need to save analysis context to a checkpoint file for recovery (user-facing alias for memory-management Part 1 CHECKPOINT_CREATE).

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Use before pushing, force-pushing, marking ready for review, or claiming checks pass on a deepseek-harness branch, and immediately after gh stack sync publishes rewritten branches, to select the smallest tests and checks that cover the outgoing or just-published diff without reflexively running the full repository suite.

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Pre-commit i18n check. Scans changed files for untranslated strings, missing locale keys, and locale sync issues. Fast and focused — only checks what changed. Use before committing or as a review add-on.

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。