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Found 2,390 Skills
Configure the project's skill stack and supervision preferences. Reads the curated registry in `skillpacks/skill_dictionary.yaml`, asks a short preset-first set of questions about workflow, dependency tolerance, autonomy style, and resource policy, then writes or updates `.co-researcher/skills.yaml`. Trigger phrases: "customize my stack", "configure skillpacks", "set up my skills", "choose presets", "configure supervision and packs", "personalize this project".
Use Po Once's organization-scoped agent API to list connected accounts, upload media, create content, schedule or publish posts, inspect status, and delete eligible scheduled posts through a local helper script.
Assistant for ZenTao project management system via JS scripts. Use when the user asks about ZenTao, lists/creates/updates projects, products, users, tasks, bugs, or manages project workflow via natural language commands.
Expert guide for deploying, configuring, and optimizing Hermes AI agents with multi-platform support, MCP integration, and production best practices
Internal sub-skill for the job-hunt suite. Parses JD information from user-provided screenshots of any job platform (Boss Zhipin, Zhaopin, 51job, Liepin, etc.) and writes structured JD Markdown files to jd-pool. Do NOT invoke directly — use the job-hunt main skill instead.
Audit an AI agent skill for security risks before installing or trusting it. Runs a deterministic scanner (regex patterns, Python AST analysis, source-to-sink taint tracking, and YARA signatures) and then reasons about intent — catching prompt injection, credential exfiltration, persistence, memory poisoning, malicious code, supply-chain risks, and description-vs-behavior mismatch. Make sure to use this skill whenever the user wants to scan, audit, vet, review, or check the safety of a skill, plugin, SKILL.md, or agent tool — whether it is a local folder, a zip/.skill file, or a cloned repo — and whenever someone asks "is this skill safe to install?".
原始人スタイル subagent への委譲判断ガイド。`genshijin-investigator` (コード位置特定)、 `genshijin-builder` (1-2ファイル編集)、`genshijin-reviewer` (diff レビュー) を inline作業 or vanilla `Explore` の代わりにスポーンするタイミングを示す。subagent 出力は原始人圧縮 → 主コンテキストに戻る tool-result が約60%縮小 → 長セッション持続。 Trigger: 「subagent 委譲」「genshijin-crew 使用」「investigator/builder/reviewer 起動」「コンテキスト節約」「圧縮 agent 出力」。
Use when designing, reviewing, or refactoring a CLI that must serve AI agents alongside humans, or when converting an API or SDK into an agent-usable CLI interface.
Turn a vague idea or task into a confirmed Working Brief by interviewing the user one question at a time, each with a recommended answer, then (only when the user chooses) plan execution with cost-effective model routing. Use when the user invokes Ask Me, wants to clarify scope or requirements before work begins (เคลียร์โจทย์ วางขอบเขต ทำ brief), or asks to execute a WORKING-BRIEF.md.
Use when the user wants to configure Lore commit format in their project or globally — writes Lore rules to the agent's instruction file (AGENTS.md, CLAUDE.md, QWEN.md, or global agent config) so all agents automatically use structured git trailers in commit messages
Break down a one-sentence idea into a task plan that an AI agent can execute independently. Use this when the user says: "Help me write a goal for the agent", "Help me break down this goal in detail", "Write a task brief for the agent", "Write a goal prompt", "Let the agent run this project on its own", "Split the work among multiple agents for parallel execution". First conduct actual tests in the codebase, conduct online research if necessary, then ask a maximum of 5 questions in one go, and produce a task plan of ≤4000 characters that can be directly pasted into /goal to run, including actual test data, whitelist boundaries, anti-cheating acceptance criteria, and resumable progress. Automatically distinguish between execution-type and exploration-type (research/selection/solution-finding) tasks.
Take your AI agent to the next level with full LangWatch integration. Adds tracing, prompt versioning, evaluation experiments, and simulation tests in one go. Use when the user wants comprehensive observability, testing, and prompt management for their agent.