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Found 15 Skills
Analyze competitor repositories with an evidence-based approach. Use this when tracking competitors, creating competitor profiles, or generating competitive analysis. CRITICAL - all analysis must be based on actual cloned code, never assumptions. Triggers include "analyze competitor", "add competitor", "competitive analysis", or "竞品分析".
Execute a complete, deterministic, read-only repository audit and produce a single `improvements.md` action plan with traceable findings (file + lines), severity, category, impact, and high-level fixes. Use when users ask for full code audits, security/performance/architecture reviews, file-by-file analysis, or technical debt mapping without modifying project files.
Systematic OSS release preparation checklist. Use when preparing a repository for open-source publishing, making a project public, or ensuring a repo meets OSS standards. Triggers: "prepare for OSS", "ready to publish", "make this public", "OSS checklist", "scan repo for publish", "open source this", "/oss-release-prep"
Analyze code repository logging coverage to ensure all function branches have LOGE/LOGI logs and identify high-frequency log risks. Supports multiple programming languages (C++, Java, Python, JavaScript, etc.)
Apply Traceknot's ISTQB-aligned, evidence-bound QA process to repository changes across OMP, Codex, GajaeCode, Claude Code, and OpenCode. Use for implementation verification, bug fixes, release checks, repository audits, defect confirmation, and residual-risk decisions without treating an agent's own completion claim as proof.
Check for security risks in Skills/code repositories. When the user wants to check if a skill, GitHub repository, npm package, or local code is safe to download or use. This includes detecting malicious code, malware, key stealing, environment variable modification, suspicious network behavior, and evaluating repository reputation (stars, forks, contributors, age). Use this skill whenever the user mentions checking skills for security risks, scanning repositories for malware, verifying code safety, checking npm packages for threats, or asking if a download is safe.
Scan the current repo and score its feedback loop maturity for AI-assisted development
Read-only audit of a repository for machine-written 'AI slop' across code, comments, tests, docs, configs and CI, against a verified catalog: glyph pockets, stale and false comments, change-narration, drift-bait numbers, template stamps, impossible defensiveness, copy-paste drift, negative-parallelism prose. Every suspect is verified against the code, absence is proven per category, and the ranked findings hand off to awesome-code-cleanup, which owns every edit. Use when asked to find AI slop or machine-written markers, or 'проверь код на следы ИИ'. Do not use to fix what it finds, for prose line-editing (awesome-document-style), or for public-claims drift (awesome-claims-audit).
Stack-aware review for local diffs, pull requests, and repository-wide audits. Routes review across shared policy plus language packs for TypeScript frontend, TypeScript backend/Bun, Go, Rust, and Python. Use after implementation, before merge, or when auditing an existing codebase.
Prepare and publish a research code repository for public release alongside a paper (arXiv, conference, GitHub). Use when the user wants to open-source code, create a GitHub release, package a code submission, make code public, or prepare a reproducibility release.
Whole-codebase deep audit. Partitions the repo into 25-file components, runs 9 perspectives by 3 models (OpenAI, Gemini, Claude) on every component via opencode run with Serena navigation, sweeps for gaps, and runs one ocr scan coverage pass. Then collates and deduplicates the findings and verifies every Critical and High issue against a clean export of the reviewed commit with isolated claude -p judges. The run directory feeds the review-tickets skill.
[user] 저장소를 읽기 전용으로 감사하고, 다른 실행자나 `tk-prep`이 재사용할 수 있는 우선순위가 있는 근거 기반 `AUD-*` `finding`을 작성합니다.