Claude Code Skills · page 121

Explain AgentOps workflows.

Run AGY headlessly via scheduled ticks or `agy -p`, capture agentapi JSONL evidence, and validate automated AGY loops or event streams.

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Use an explicitly selected AGY runtime for Triggers: "agy", "antigravity", "AGY evidence".

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Manage the PROGRAM.md/AUTODEV.md contract consumed by evolve/factory ticks. Use for loop rules, boundaries, or PROGRAM.md repair.

Track br issues and dependencies for Codex agents. Triggers: "beads", "br", "track issue", "create task", "find work", "ready issues".

Initialize explicitly requested, missing Triggers: "bootstrap AgentOps", "initialize AgentOps docs".

Separate goals from implementation.

Compile .agents knowledge wiki.

Collect independent perspectives for an Triggers: "council", "multi-judge review", "independent perspectives".

Hands-free epic execution for Codex using wave-based sub-agents and lead-side validation. Triggers: "crank", "run epic", "execute epic", "run all tasks", "hands-free execution", "crank it".

Validate product fit before discovery. Use when: framing a problem, checking product/market fit, or pressure-testing user value before writing a discovery packet or any code.

Create dense execution packets.

Run autonomous improvement loops.

Check knowledge flywheel health.

Mine transcripts into learnings.

Compatibility alias — renamed to fitness. Triggers: "goals" (deprecated).

Write compact caller-authored session evidence without choosing continuation. Triggers: "handoff", "write compact session handoff".

Execute one bounded RED to GREEN experiment from bead or caller intent; return derived subject identity and check facts. Triggers: "implement", "implement this bead", "run the experiment". Full plan-to-validation requests route to rpi.

Load relevant .agents context.

Install or run the seven-move operating-loop Workflow for AgentOps plugin users and multi-agent orchestration.

Shape or refine the existing bead or caller intent without a second planning artifact. Triggers: "plan", "discover and plan", "shape this goal".

Review completed work and learn. Use when: a task, PR arc, or session is finished and you want to extract learnings, or after ≥5 PRs (the scope checkpoint).

Stress-test plans before work. Use when: a plan is drafted but not yet executed and you want to surface failure modes, risks, and what would prove it wrong before committing.

Validate, commit, and push.

Record Brownian Ratchet gates.

Recover session context.

Probe docs and skills. Use when: adversarially probing a doc, skill, plan, or claim for weaknesses, gaps, or unstated assumptions before it ships.

Answer a bounded question with current cited evidence. Triggers: "research", "investigate this question", "find evidence". (Investigating a repository routes to codebase-recon.)

Coordinate one RPI traversal: one bounded Plan, Implement, and fresh Validate experiment, then report and stop. Triggers: "run rpi", "run one traversal", "execute this plan", orchestration or worker delegation that implements changes.

Review the bead or caller intent write scope for completeness and ambiguity. Triggers: "review write scope", "check scope boundaries", "scope this change".

Universal AgentOps init prompt for starting or onboarding a fresh agent session.

Audit SKILL.md files against the AgentOps template and readiness checks. Use for quality reviews or template compliance.

Create a metadata-complete AgentOps skill source package, regenerate its derived projections, and check or repair structural hygiene in skill packages. Triggers: "create a skill", "scaffold skill", "absorb external skill", "new skill", "heal skill", "repair skill hygiene", "audit skill structure", "check skill package".

Report observable AgentOps evidence without selecting work. Triggers: "status", "show AgentOps status".

Dispatch explicit disjoint packets exactly once through a caller-selected executor. Triggers: "swarm", "dispatch disjoint packets", "parallel explicit tasks".

Freshly judge exact subject content against bead or caller acceptance, optionally persist verdict.v2 for a declared consumer, and stop. Triggers: "validate", "independently validate", "vibe".

Switch a caller-selected coding-agent Triggers: "switch account", "rotate coding-agent account".

Use when operating ACFS flywheel health checks, init, and agent loop tooling from ~/acfs/bin/acfs.

Operate explicit orchestrator, implementer Triggers: "agent-native factory", "role-shaped agent panes", "persistent workers".

Guard outcome work against process overhead. Triggers: RPI pre-Plan guard; explicit "full anti-ceremony audit" requests.

Front door for agent automation: choose Triggers: "build automation", "which orchestration shape", "should this use NTM".

Reconstruct a repository as cited Triggers: "codebase recon", "trace this codebase", "repository audit", "refresh the prior recon".

Run one caller-supplied Codex command Triggers: "run Codex headless", "capture Codex evidence".

Convert AgentOps skill formats. Triggers: "converter", "convert agentops skill formats.", "converter skill".

Compile or lint a persistent Mayor-style Triggers: "craft a goal prompt", "mayor goal", "goal-runner prompt", "lint this goal", "is this goal safe". (Shaping one experiment''s intent routes to plan.)

Generate and validate repo docs, READMEs Triggers: "doc", "generate and validate repo docs", "doc skill".

Load the AgentOps language and Triggers: "define this domain term", "check the bounded context".

Measure declared project fitness goals Triggers: "fitness", "check project fitness", "measure goals".

Name the skills reserved for people to invoke — where the runtime honors disable-model-invocation — and when to reach for each. Triggers: "human-only skills", "which skills must I run myself".

Generate evidenced opportunities or challenge an idea with sealed perspectives. Triggers: "idea genie", "what should we build", "challenge this idea", "compare proposals".

Optionally analyze collections of durable verdicts for recurring evidence after the critical path. Triggers: "learn from verdicts", "mine validation history".

meta_skill (ms) — the skill-search/load engine over both corpora (agentops + jsm). Find a skill for a task, search skills, or load runnable skill guidance. Triggers: "ms", "meta_skill", "skill search", "find a skill for", "load skill guidance".

Classify a pending decision as reversible or irreversible before it is acted on, and route irreversible ones to the caller instead of auto-deciding. Triggers: "is this a one-way door", "can we undo this", "should I just decide this", "the models disagree with me", before any auto-decided approval gate.

Distill repeated, evidence-backed expertise into a proposed skill, check, reference, or workflow artifact. Triggers: "operationalize this", "turn this expertise into a reusable capability".

Test repeated implementation shapes against independent exemplars and a holdout before routing an earned abstraction. Triggers: "mine a recurring code pattern", "is this abstraction earned", "extract invariants from implementations".

Optionally test a retrospective causal question against durable verdict evidence. Triggers: "postmortem", "causal retrospective", "test a retrospective hypothesis".

Optionally challenge a frozen plan with one fresh independent judge before implementation. Triggers: "premortem", "challenge this plan", "what could make this plan fail".

Create or refine PRODUCT.md while separating evidence, aspiration, users, value, and non-goals. Triggers: "product", "create PRODUCT.md", "product boundary".

Compare a claimed state with observable repository evidence and report concrete gaps. Requires a claim or expected state to test. Triggers: "reality check", "is this claim actually done", "compare claim to repo".

Execute one behavior-preserving structural transformation and report evidence. Triggers: "refactor this", "simplify without changing behavior".

Reverse-engineer an authorized repo, binary, or product into a verifiable feature inventory and adoption map. Triggers: "reverse-engineer X", "tear down Y", "what should we steal from Z", "evaluate competitor/upstream", "should we fork/adopt/build-native".

Pick the one AgentOps skill that owns a request, or answer that none does. Triggers: "which skill covers this", "route this", "is there a skill for X", "what should I use here", or any request whose owning skill is not obvious.

Stamp a bounded project, component, or CI scaffold and verify the generated result once. Triggers: "scaffold", "create project component or boilerplate".

Retired — its runtime-neutrality contract moved to docs/contracts/runtime-neutrality.md. Triggers: none — not routable.

Author and tier behavioral probes for a skill, including seeded-defect probes that escape ceiling saturation. Triggers: "measure this skill", "the probe came back INERT", "the control arm aces it", "harden this scenario", "is this skill actually doing anything".

Load only the standards relevant to a caller-supplied change, then report concrete findings. Triggers: "check standards", "which standards apply".

Mine caller-supplied usage history for repeated toil and emit ranked evidence. Triggers: "mine toil", "find repeated operational work".

Operate the Agentic Coding Flywheel as a caller-selected software factory; keep its runtime state out of AgentOps verdicts. Triggers: "using flywheel", "agent flywheel".

Operate a caller-selected Gas City 1.4 with upstream registry packs and native run-centered surfaces while keeping GC runtime state out of AgentOps verdicts. Triggers: "using gc", "gas city", "drive the mayor", "dispatch through gc".

Scaffold an explicit one-shot workflow adapter without lifecycle authority. Triggers: "build a workflow adapter", "scaffold a one-shot workflow".

SEO·AEO·GEO·LLMO·NEO(네이버) 다섯 레인을 진단하고 직접 구현하는 스킬. 사이트를 검색엔진·답변엔진·생성 AI·네이버 AI 브리핑이 인용하는 1차 소스로 만든다. "SEO 해줘", "AI에 인용되게 해줘", "네이버 노출 늘려줘", "llms.txt 만들어줘" 류 요청에 사용. Use for "audit my site's SEO", "get my site cited by ChatGPT/Perplexity/AI Overviews", "improve search visibility", "create llms.txt", "answer engine / generative engine optimization", and any AI-search-visibility request.

Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

Ask the application user concise structured questions when missing information blocks a safe AI4J agent action.

Use this skill when helping users build applications with AI4J in their own Java or Spring Boot projects, including first chat, streaming, tool/function calls, MCP, RAG, memory, Agent runtime, Coding Agent CLI embedding, FlowGram integration, provider configuration, dependency selection, and troubleshooting. It guides beginner-friendly app scaffolding, secure environment-variable configuration, smallest useful AI4J module selection, runnable examples, and verification steps. For AI4J repository maintenance, follow the repository AGENTS.md and Harness Anything task workflow instead of this user-facing app builder skill.

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Use the Claude Code CLI to consult Claude and delegate coding tasks for prototyping, debugging, and code review. Supports multi-turn sessions via SESSION_ID. Optimized for low-token, file/line-based handoff.

Use the Gemini CLI to consult Gemini and delegate coding tasks for prototyping, debugging, and code review. Supports multi-turn sessions via SESSION_ID. Optimized for low-token, file/line-based handoff.

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求职工具包。把「找岗位 → 签 offer」拆成六个独立子 skill：⓪ Job Hunt 批量发现岗位，① Job Description 解码具体 JD，② Resume 定向简历，③ BQ 准备行为面试，④ Offer Compare 对比多份 offer，⑤ Salary Negotiation 谈 package。任何求职相关请求（找工作、该不该投、改简历、准备面试、选 offer、谈薪）都从这里进，再路由到对应子 skill。关键词：求职, offer, career, job hunt, LinkedIn jobs, JD, resume, CV, behavioral interview, BQ, STAR, offer compare, salary negotiation, 薪资谈判, 选 offer。

行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。

Job Description 解码器 + Offer 策略系统。把任何 JD 翻译成「这家公司到底在招什么人 / 我的匹配度 / Gap 在哪 / 为什么投/不投 / 面试会问什么 / 下一步怎么走」。只有 3 步：贴 JD → 给简历 → 自动生成并打开一份 HTML Offer Strategy Report。是整个求职链路的入口，向下衔接 Resume Skill 和 BQ Skill。关键词：job description, JD, 职位描述, 匹配度, ATS, resume tailor, 简历定制, 招聘经理, 投不投, should I apply, 面试预测, offer strategy。

批量发现、整理和持续维护求职岗位清单。根据用户的简历、目标方向或种子 JD，搜索 LinkedIn 与公开网页，去重并建立尽量完整的候选池，用证据区分已核验事实和方向性推断，最后生成可搜索、可筛选的单文件 HTML job-hunt-skill 报告。用户说‘帮我批量找岗位’、‘整理职位清单’、‘使用 job-hunt-skill’、‘根据简历搜工作’、‘把这些 LinkedIn 职位做成表格’、‘持续跟踪岗位’时必须使用。只发现和分析岗位；绝不自动申请、处理登录凭据或绕过访问限制。

LinkedIn 岗位发现与匹配排序。根据用户给的一份种子 JD 和简历，提取目标岗位画像，自动生成多组 LinkedIn Jobs 搜索，采集、去重并按证据给岗位分层，最终输出可直接投递的 shortlist。用户说‘帮我找工作’、‘在 LinkedIn 搜适合我的岗位’、‘找相似职位’、‘根据简历推荐岗位’、‘job search’、‘find jobs like this’时必须使用。只搜索和推荐，不自动投递、不代替用户登录、不绕过验证码。

Offer 对比决策器。用户同时拿到两份（或多份）offer 纠结怎么选时，扮演 Senior Career Decision Advisor：结构化对比 comp / 成长 / AI 敞口 / 公司强度 / 团队风险 / 晋升速度 / 生活方式，识别 front-loaded vs long-term upside、resume value、switch-out 难度、hidden risks，最后给一条**明确、有立场、不中性**的推荐（并附「你是 X 类人选 A，你是 Y 类人选 B」的分叉判定）。三步：贴两份 offer → 补 priorities & 现状 → 自动生成并打开一份 HTML Offer Decision Report。是 offer-toolkit 里 JD Skill / Resume Skill / BQ Skill 拿到结果之后的下一环。关键词：offer compare, 选 offer, 两个 offer, offer decision, TC compare, 该去哪家, which offer, 4-year TC, RSU, sign-on, front-loaded, long-term upside, career decision。

简历生成与美化。两种入口：把已有简历(PDF/Word/文本)解析、诊断、套用模板美化；或者还没有简历时，通过 LinkedIn 导入或一问一答的对话帮你从零建出一份。输出单页打印优化的 HTML(浏览器里 Cmd+P 直接存成 PDF)，提供 4 套模板：Classic/ATS 友好、Modern 侧栏、Elegant 衬线、Tech 紧凑。关键词：resume, CV, 简历, 美化简历, 做简历, 简历模板, resume template, LinkedIn 简历, 求职简历, ATS, 改简历, polish resume。

薪资谈判教练。模拟前 FAANG 招聘官 + 薪酬策略师，把「拿到 offer → 签字」这一步拆成 6 个动作：Offer 诊断（Base / RSU / Sign-on / Bonus 每项标 LOW/MED/HIGH leverage）→ 谈判杠杆图（我的力量 / 招聘官的力量 / 弱点）→ 策略排序（Base → RSU → Sign-on → Bonus，按 context 调整）→ 脚本生成（电话 / 邮件 / 招聘官回话模拟）→ Counter 模拟（"这是最好的了" / "预算固定" / "会考虑其他候选人" 三种硬回答）→ 最终建议（要什么 / 别推什么 / 何时停）。三步：贴 offer → 说 context（有没有竞争 offer / deadline / 级别 / 风险偏好 / 最看重什么）→ 自动生成并打开一份 HTML Negotiation Playbook。关键词：salary negotiation, offer negotiation, comp negotiation, RSU, sign-on, base salary, counter offer, competing offer, TC, total comp, 薪资谈判, offer 谈判, 谈薪, 反 offer, 加薪, 谈 package, levels.fyi, 招聘官话术, recruiter script。

Analyzes, generates, and enhances CLAUDE.md files for any project type using best practices, modular architecture support, and tech stack customization. Use when setting up new projects, improving existing CLAUDE.md files, or establishing AI-assisted development standards.

Re-detect this project's tech stack from package.json / requirements.txt / pyproject.toml / go.mod / Cargo.toml and diff it against the Tech Stack section of every CLAUDE.md. Read-only — returns added / removed / renamed dependencies, never edits.

Audit every CLAUDE.md in this project for drift against the last week of git history. Flags sections that reference deleted files, renamed paths, or removed dependencies. Read-only — returns a punch list, never edits.

Verify every @path chain import and every markdown link inside every CLAUDE.md in this project resolves to an existing file. Read-only — returns broken links with file:line refs, never edits.

Behavioral guardrails for LLM-assisted coding. Use when writing, reviewing, or refactoring code in any project to avoid overcomplication, keep changes surgical, surface assumptions early, and execute against verifiable success criteria.

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Audit websites for SEO, technical, content, security, JS rendering, and AI readiness using SEOmator CLI. Returns LLM-optimized reports with health scores across 287 rules and 20 categories, and can diff two audits to show what a deploy changed. Use when analyzing websites, debugging SEO issues, checking site health, or comparing a site before and after a change.

Implement SAFe methodology in Jira. Use when creating Epics, Features, Stories with proper hierarchy, acceptance criteria, and parent-child linking.

Orchestrate Jira workflows end-to-end. Use when building stories with approvals, transitioning items through lifecycle states, or syncing task completion with Jira.