4.2K skills · sorted by relevance
Agent Skillを書く時の心得。SKILL.mdに何を書き、何を書かないかの判断基準を定める。AIが読んで従う文書に共通の心得は、kuden:common-rules-for-agent-instructionsにある。 Agent Skillを作成する時、編集する時、レビュー指摘の採否を決める時に読み込む。
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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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Create cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill, audit this skill, is this skill safe, vet a skill before installing, what does this skill access. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, spec validation, and security auditing of third-party skills before install.
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Author in-repo SKILL.md: frontmatter, validator, structure.
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Discovers and invokes agent skills. Use when starting a session or when you need to discover which skill applies to the current task. This is the meta-skill that governs how all other skills are discovered and invoked.
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別AIのCLI呼び出しを支援する。Codex以外のCLIを使う依頼で使う。 Codexへの相談・実装委譲は codex Skill を使う。Skill運用の移行は agent-skill-migrate、配置監査は overlay-audit を使う。
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起動ディレクトリと dotfiles root の Skill・Agent 配置を監査する。一般的なSkill運用の移行は agent-skill-migrate を使う。
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Review code using shared OpenClaw instructions and a Python helper, while also following the target repository’s review requirements. This skill points to a common installation that supplies the actual review procedure.
Review and refactor React and Next.js code to address slow loading and interactions. Prioritized guidance covers sequential data requests, downloaded JavaScript size, server processing, repeated rendering, and browser work, with examples for choosing relevant improvements.
Create or revise skills shipped within the Hermes Agent repository. The guidance covers file placement, required metadata, clear procedures, platform checks, tests, and documentation updates so contributions follow the repository’s authoring standards.
Add logs, metrics, traces that follow requests across services, and actionable alerts to production features. This skill guides signal design, request identification, response instructions, and checks that the collected data can explain failures.
Design APIs and interfaces between modules with clear inputs, outputs, naming, and errors. The skill guides compatible extensions, input validation, and retry handling so callers can use interfaces consistently without accidentally repeating state-changing operations.
Prepare a production release with checks for code quality, security, performance, accessibility, and infrastructure. Plan monitoring, gradual feature exposure, and rollback steps, then use release metrics and reliability limits to guide rollout decisions.
Clarify an unclear request through one question at a time, with the agent sharing its best guess for each answer. Produce a user-confirmed statement covering the desired outcome, audience, timing, success criteria, constraints, and exclusions.
Turn an unclear feature idea into a structured specification with goals, boundaries, and testable success criteria. Split broader requests into independent modules when needed, then move through planning, task breakdown, and implementation with human review.
Organize the information an AI coding agent needs for each task. This skill guides project rules, relevant file selection, conversation summaries, and session handoffs so the agent can follow existing conventions and retain key decisions.
Set up automated checks and deployment workflows with GitHub Actions. Covers tests, type checks, builds, security audits, preview deployments, staged releases, and rollback plans, with guidance for diagnosing failures and reducing pipeline runtime.
Challenge important design and implementation decisions with a separate reviewer that does not inherit the working session's context. Identify hidden assumptions and failure cases, then assess each finding against the artifact and its requirements.
Turn clear requirements into small implementation tasks with acceptance criteria, verification steps, dependencies, and estimated scope. Produce a written plan and ordered task list, with checkpoints and opportunities for parallel work identified before coding begins.
Organize code changes into focused commits and manageable branches, prepare pull requests, and maintain release versions and changelogs. The skill also covers separate working directories for parallel work and checks before committing.
Turn a rough idea into a concrete concept through guided discussion. Explore alternatives, compare user value and feasibility, and produce a Markdown brief covering the recommended direction, assumptions to test, minimum scope, and explicit exclusions.
Plan the retirement of old APIs, features, and systems, then move their users to replacements in stages. The skill also guides database schema changes through additive steps that keep old and new application code compatible during rollout.
Document why software decisions were made, which alternatives were considered, and what trade-offs remain. Create architecture decision records, README guidance, API documentation, and changelogs that help future contributors and AI agents understand the project.
Define project quality standards in CONSTRAINTS.md, connect measurable targets to checks, and inspect changes for weakened safeguards. It helps teams choose thresholds and catch skipped tests, suppressed warnings, unfinished code, or lowered standards during agent development.