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Found 203 Skills
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it. Use for /arena, 'arena this', 'throw it in the arena', or when one attempt at a non-trivial artifact would lock in the wrong shape.
Analyze candidate algorithms for time/space complexity, scalability limits, and resource-budget fit (CPU, memory, I/O, concurrency). Use when feasibility depends on input growth or latency/memory constraints and quantitative bounds are required before implementation; do not use for persistence schema or deployment topology decisions.
This skill should be used when the user needs to create a personalized, compelling cover letter from a resume and job description. Use when writing job application letters, addressing specific role requirements, handling career change narratives, or structuring persuasive arguments for candidacy.
Comprehensive evaluation of potential stock investments combining valuation analysis, fundamental research, technical assessment, and clear buy/hold/sell recommendations. Use when the user asks about buying a stock, evaluating investment opportunities, analyzing watchlist candidates, or requests stock recommendations. Provides specific entry prices, position sizing, and conviction ratings.
Buffett-style stock screener — "What would Buffett buy now?" Generates 3–5 candidate stocks from a market / sector / preference query via a two-layer model: hard quant filter (ROE 5y ≥15%, debt/asset ≤50%, FCF positive 3y, listed ≥5y, gross margin ≥30%) → qualitative moat scoring (moat 35% / capital allocation 20% / earnings predictability 20% / valuation 15% / runway 10%). Longbridge CLI first, MCP fallback, WebSearch for gaps only. Output: candidate cards with moat-type tag, quantitative highlights, verdict (🟢 likely buy / 🟡 wait for price / 🔴 not at this price), deep-dive CTA to `longbridge-buffett-moat-analyzer`. Mandatory holding-period education + data-source appendix. Disqualifies airlines, pre-revenue biotech, ST, listing<5y. Triggers: "巴菲特会买什么", "巴菲特选股", "巴菲特风格的股票", "护城河选股", "宽护城河股票", "价值投资选股", "10年不动的股票", "定价权强的公司", "巴菲特會買什麼", "巴菲特選股", "護城河選股", "寬護城河股票", "Buffett screener", "what would Buffett buy", "wide-moat screener", "quality compounder screen", "Berkshire-style screen", "pricing-power screen".
Generate ultra-compact commit messages. Follows the Conventional Commits format with subject ≤50 characters, prioritizing "why" over "what". Supports both Japanese and English. Trigger with "Make a commit message", "/commit", or "/genshijin-commit". Auto-trigger candidate when staging changes.
Final pass for rhythm, word choice, consistency, and a candid assessment of the finished piece
Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end.
Fill out job application form fields with context-aware, tailored answers drawn from the candidate's CV and the job description
Ultra-compressed PR review comments. One issue per line: Location • Problem • Fix. Remove preambles, prioritize signals. Supports Japanese. Trigger with "PR review please", "code review", "/review", "/genshijin-review". Candidate for automatic activation during pull request reviews.
Manage local Codex session transcripts, including listing candidate sessions, exporting full or selected sessions to organized Markdown, inspecting archived sessions, and summarizing tool-call history. Use when the user asks to scan, parse, archive, inspect, recover, summarize, manage, or convert Codex sessions, `~/.codex/sessions` data, `~/.codex/archived_sessions` data, `.jsonl` transcripts, tool-call history, or hard-to-read Codex conversation logs.
Analyze code complexity, cyclomatic complexity, maintainability index, and code churn using metrics tools. Use when assessing code quality, identifying refactoring candidates, or monitoring technical debt.