[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M70MJYSC2VXVWB2FBA81D645":3,"skill-same:01M70MJYSC2VXVWB2FBA81D645":92},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M70MJYSC2VXVWB2FBA81D645","harness-eval",null,"This skill should be used when the user asks to \"test the harness\", \"run integration tests\", \"validate features with real API\", \"test with real model calls\", \"run agent loop tests\", \"verify end-to-end\", or needs to verify OpenHarness features on a real codebase with actual LLM calls.","shengdabai\u002FTony-Claude-Code-Skills","shengdabai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F116417419?v=4","01M70F6D2WY5PQV5A92BKYJQEW","Tony Sheng","01M70F6D3GAAAYZ5H4CC1G8VXM",3,"MIT","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fharness-eval\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"harness","harness eval","eval","validate","codebase","integration","verify","test","with","tests","calls","integration tests","features","run","api","when","run integration tests","loop","end","model","2026-11-10 10:12:30",false,"---\nname: harness-eval\ndescription: This skill should be used when the user asks to \"test the harness\", \"run integration tests\", \"validate features with real API\", \"test with real model calls\", \"run agent loop tests\", \"verify end-to-end\", or needs to verify OpenHarness features on a real codebase with actual LLM calls.\nversion: 0.2.0\n---\n\n# Harness Eval — End-to-End Feature Validation\n\nValidate OpenHarness features by running real agent loops against an unfamiliar codebase with actual LLM API calls. Every test exercises the full stack: API client → model → tool calls → execution → result.\n\n## Core Principles\n\n1. **Test on an unfamiliar project** — never test on OpenHarness itself (the agent modifies its own code). Clone a real project as the workspace.\n2. **Use real API calls** — no mocks. Configure a real LLM endpoint.\n3. **Multi-turn conversations** — always test 2+ turns where the model needs prior context.\n4. **Combine features** — test hooks+skills+agent loop together, not in isolation.\n5. **Verify tool execution** — inspect tool call lists and output files, not just model text.\n\n## Workflow\n\n### 1. Prepare Workspace\n\nClone an unfamiliar project (do not use OpenHarness):\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAutoAgent \u002Ftmp\u002Feval-workspace\n```\n\n### 2. Configure Environment\n\n```bash\nexport ANTHROPIC_API_KEY=sk-xxx\nexport ANTHROPIC_BASE_URL=https:\u002F\u002Fapi.moonshot.cn\u002Fanthropic  # or any provider\nexport ANTHROPIC_MODEL=kimi-k2.5\n```\n\nFor long-running real evals, do not artificially lower `max_turns`. Use the product default (`200`) unless the user explicitly wants a tighter bound.\n\n### 3. Prepare Real Sandbox Runtime When Relevant\n\nIf the task is validating sandbox behavior, install and verify the actual runtime before running agent loops:\n\n```bash\nnpm install -g @anthropic-ai\u002Fsandbox-runtime\nsudo apt-get update\nsudo apt-get install -y bubblewrap ripgrep\nwhich srt\nwhich bwrap\nwhich rg\nsrt --version\n```\n\nThen run a minimal smoke check through OpenHarness, not just raw `srt`, so you verify the real adapter path:\n\n```python\nfrom pathlib import Path\nfrom openharness.config.settings import Settings, SandboxSettings, save_settings\nfrom openharness.tools.bash_tool import BashTool\n\ncfg = Path(\"\u002Ftmp\u002Fopenharness-sandbox-settings.json\")\nsave_settings(Settings(sandbox=SandboxSettings(enabled=True, fail_if_unavailable=True)), cfg)\n# Point config loader at this file, then run BashTool on a tiny command such as `pwd`.\n```\n\nIf sandbox dependencies are missing, treat that as an environment\u002Fsetup failure, not a feature regression.\n\n### 4. Design Tests\n\nEach test follows this pattern:\n\n```python\nengine = make_engine(system_prompt=\"...\", cwd=UNFAMILIAR_PROJECT)\nevs1 = [ev async for ev in engine.submit_message(\"Read X, analyze Y\")]\nr1 = collect(evs1)  # text, tools, turns, tokens\nevs2 = [ev async for ev in engine.submit_message(\"Based on what you found...\")]\nr2 = collect(evs2)\nassert \"grep\" in r1[\"tools\"]  # verify tools ran\n```\n\nFor detailed code templates and the `make_engine`\u002F`collect` helpers, consult `references\u002Ftest-patterns.md`.\n\n### 5. Prefer Long-Horizon, Real Agent Loops\n\nFor meaningful end-to-end validation, prefer unfamiliar-repo tasks that force multiple turns, context reuse, and mixed tool usage.\n\nRecommended pattern:\n\n- Use a real external workspace such as `AutoAgent`\n- Use real provider credentials and the actual target model\n- Keep `max_turns=200`\n- Use per-prompt timeouts large enough for real exploration, such as `240-600s`\n- Require at least 2 turns per scenario\n- Verify both text quality and tool traces\n- Keep polling long-running sessions until they finish; do not abandon a run after the first long pause\n\nRecommended long-horizon scenarios:\n\n- `architecture_multiturn`\n  - Turn 1: map architecture, shell\u002Fsubprocess surfaces, and test entrypoints\n  - Turn 2: identify top risks and propose refactors\n  - Turn 3: condense into onboarding or remediation actions\n  - Success: `bash`, `glob`, `grep`, `read_file` all appear; no timeout; no `MaxTurnsExceeded`\n\n- `hook_block_and_recover`\n  - Force the model to try `bash`\n  - Block it with a real pre-tool hook\n  - Verify the model adapts with `glob`\u002F`grep`\u002F`read_file`\n\n- `sandbox_multiturn`\n  - Enable real sandbox settings with `fail_if_unavailable=true`\n  - First prompt must start with exactly one shell command such as `pwd && ls -la`\n  - Second prompt must explicitly reuse the prior shell findings\n  - Success: `bash` executes via sandbox, non-shell tools continue the task, and the agent recovers from incidental repo errors\n\nWhen a scenario fails, classify it before changing code:\n\n- `MaxTurnsExceeded`: likely eval harness misconfiguration if `max_turns` was manually lowered\n- `timeout`: task is too broad or per-prompt timeout is too small\n- sandbox unavailable: environment missing `srt`, `bwrap`, or `rg`\n- tool error with task still completed: feature may still be healthy; inspect recovery behavior\n\n### 6. Run Tests\n\n```bash\npython tests\u002Ftest_merged_prs_on_autoagent.py   # PR feature tests\npython tests\u002Ftest_real_large_tasks.py           # large multi-step tasks\npython tests\u002Ftest_hooks_skills_plugins_real.py  # hooks\u002Fskills\u002Fplugins\npython -m pytest tests\u002F -q -k \"not autoagent\"  # unit tests (no API)\n```\n\nFor ad hoc long-horizon validation, it is acceptable to run a temporary Python driver script as long as it:\n\n- uses real OpenHarness engine\u002Ftool objects\n- targets an unfamiliar repository\n- prints per-scenario JSON summaries\n- records tools, errors, turns, and token usage\n- stays attached until completion\n\n### 7. Interpret Results\n\n| Result | Meaning | Action |\n|--------|---------|--------|\n| PASS with tool calls | Feature works end-to-end | Done |\n| PASS without tool calls | Model answered from knowledge | Rewrite prompt to force tool use |\n| FAIL with exception | Code bug | Read traceback |\n| FAIL with wrong output | Model behavior issue | Check system prompt and tool schemas |\n| Timeout | Task too complex | Increase `max_turns` or simplify prompt |\n\nFor long-running real evals, refine the timeout guidance:\n\n- First check whether `max_turns` was manually set too low\n- If `max_turns=200` and the run still fails, the next suspect is wall-clock timeout, not turn count\n- Distinguish environment failures from product failures\n  - Example: missing dependency in the unfamiliar target repo is not automatically an OpenHarness regression\n  - Example: missing `srt`\u002F`bwrap`\u002F`rg` is an eval environment issue\n\n## Feature Coverage Checklist\n\n- [ ] Engine: multi-turn memory, tool chaining, parallel tools, error recovery, auto-compaction\n- [ ] Swarm: InProcessBackend lifecycle, concurrent teammates, coordinator+notifications\n- [ ] Hooks: pre_tool_use blocking → model adapts, post_tool_use firing\n- [ ] Skills: skill tool invocation → model follows instructions\n- [ ] Plugins: plugin-provided skill loaded and used in agent loop\n- [ ] Memory: YAML frontmatter parsing, body content search, context injection\n- [ ] Session: save → load → resume with context preserved\n- [ ] Providers: Anthropic client, OpenAI client (with reasoning_content), multi-turn\n- [ ] Cost: token accumulation across turns\n\n## Common Pitfalls\n\n- Testing on OpenHarness itself — agent modifies its own running code\n- Using mocks — misses serialization and API compatibility bugs\n- Single-turn only — misses context accumulation and compaction bugs\n- Artificially lowering `max_turns` during real evals — can create false failures that do not reflect product defaults\n- Not checking tool call list — model may claim tool use without calling it\n- Hardcoding paths — use `WORKSPACE` variable, skip in CI with `pytest.mark.skipif`\n- Declaring sandbox “tested” after only checking raw `srt` — verify the OpenHarness adapter path too\n- Abandoning long tasks too early — some real tasks pause for minutes before the next event arrives\n\n## Additional Resources\n\n### Reference Files\n\n- **`references\u002Ftest-patterns.md`** — Complete code templates for `make_engine`, `collect`, and each feature category\n- **`references\u002Ffeature-matrix.md`** — Detailed test cases for every OpenHarness module\n\n### Existing Test Files\n\nWorking test suites in the repo:\n- `tests\u002Ftest_merged_prs_on_autoagent.py` — PR feature validation\n- `tests\u002Ftest_real_large_tasks.py` — Large multi-step tasks\n- `tests\u002Ftest_hooks_skills_plugins_real.py` — Hooks\u002Fskills\u002Fplugins in agent loops\n- `tests\u002Ftest_untested_features.py` — Module-level integration tests\n","# Harness Eval — End-to-End Feature Validation\n\nValidate OpenHarness features by running real agent loops against an unfamiliar codebase with actual LLM API calls. Every test exercises the full stack: API client → model → tool calls → execution → result.\n\n## Core Principles\n\n1. **Test on an unfamiliar project** — never test on OpenHarness itself (the agent modifies its own code). Clone a real project as the workspace.\n2. **Use real API calls** — no mocks. Configure a real LLM endpoint.\n3. **Multi-turn conversations** — always test 2+ turns where the model needs prior context.\n4. **Combine features** — test hooks+skills+agent loop together, not in isolation.\n5. **Verify tool execution** — inspect tool call lists and output files, not just model text.\n\n## Workflow\n\n### 1. Prepare Workspace\n\nClone an unfamiliar project (do not use OpenHarness):\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FHKUDS\u002FAutoAgent \u002Ftmp\u002Feval-workspace\n```\n\n### 2. Configure Environment\n\n```bash\nexport ANTHROPIC_API_KEY=sk-xxx\nexport ANTHROPIC_BASE_URL=https:\u002F\u002Fapi.moonshot.cn\u002Fanthropic  # or any provider\nexport ANTHROPIC_MODEL=kimi-k2.5\n```\n\nFor long-running real evals, do not artificially lower `max_turns`. Use the product default (`200`) unless the user explicitly wants a tighter bound.\n\n### 3. Prepare Real Sandbox Runtime When Relevant\n\nIf the task is validating sandbox behavior, install and verify the actual runtime before running agent loops:\n\n```bash\nnpm install -g @anthropic-ai\u002Fsandbox-runtime\nsudo apt-get update\nsudo apt-get install -y bubblewrap ripgrep\nwhich srt\nwhich bwrap\nwhich rg\nsrt --version\n```\n\nThen run a minimal smoke check through OpenHarness, not just raw `srt`, so you verify the real adapter path:\n\n```python\nfrom pathlib import Path\nfrom openharness.config.settings import Settings, SandboxSettings, save_settings\nfrom openharness.tools.bash_tool import BashTool\n\ncfg = Path(\"\u002Ftmp\u002Fopenharness-sandbox-settings.json\")\nsave_settings(Settings(sandbox=SandboxSettings(enabled=True, fail_if_unavailable=True)), cfg)\n# Point config loader at this file, then run BashTool on a tiny command such as `pwd`.\n```\n\nIf sandbox dependencies are missing, treat that as an environment\u002Fsetup failure, not a feature regression.\n\n### 4. Design Tests\n\nEach test follows this pattern:\n\n```python\nengine = make_engine(system_prompt=\"...\", cwd=UNFAMILIAR_PROJECT)\nevs1 = [ev async for ev in engine.submit_message(\"Read X, analyze Y\")]\nr1 = collect(evs1)  # text, tools, turns, tokens\nevs2 = [ev async for ev in engine.submit_message(\"Based on what you found...\")]\nr2 = collect(evs2)\nassert \"grep\" in r1[\"tools\"]  # verify tools ran\n```\n\nFor detailed code templates and the `make_engine`\u002F`collect` helpers, consult `references\u002Ftest-patterns.md`.\n\n### 5. Prefer Long-Horizon, Real Agent Loops\n\nFor meaningful end-to-end validation, prefer unfamiliar-repo tasks that force multiple turns, context reuse, and mixed tool usage.\n\nRecommended pattern:\n\n- Use a real external workspace such as `AutoAgent`\n- Use real provider credentials and the actual target model\n- Keep `max_turns=200`\n- Use per-prompt timeouts large enough for real exploration, such as `240-600s`\n- Require at least 2 turns per scenario\n- Verify both text quality and tool traces\n- Keep polling long-running sessions until they finish; do not abandon a run after the first long pause\n\nRecommended long-horizon scenarios:\n\n- `architecture_multiturn`\n  - Turn 1: map architecture, shell\u002Fsubprocess surfaces, and test entrypoints\n  - Turn 2: identify top risks and propose refactors\n  - Turn 3: condense into onboarding or remediation actions\n  - Success: `bash`, `glob`, `grep`, `read_file` all appear; no timeout; no `MaxTurnsExceeded`\n\n- `hook_block_and_recover`\n  - Force the model to try `bash`\n  - Block it with a real pre-tool hook\n  - Verify the model adapts with `glob`\u002F`grep`\u002F`read_file`\n\n- `sandbox_multiturn`\n  - Enable real sandbox settings with `fail_if_unavailable=true`\n  - First prompt must start with exactly one shell command such as `pwd && ls -la`\n  - Second prompt must explicitly reuse the prior shell findings\n  - Success: `bash` executes via sandbox, non-shell tools continue the task, and the agent recovers from incidental repo errors\n\nWhen a scenario fails, classify it before changing code:\n\n- `MaxTurnsExceeded`: likely eval harness misconfiguration if `max_turns` was manually lowered\n- `timeout`: task is too broad or per-prompt timeout is too small\n- sandbox unavailable: environment missing `srt`, `bwrap`, or `rg`\n- tool error with task still completed: feature may still be healthy; inspect recovery behavior\n\n### 6. Run Tests\n\n```bash\npython tests\u002Ftest_merged_prs_on_autoagent.py   # PR feature tests\npython tests\u002Ftest_real_large_tasks.py           # large multi-step tasks\npython tests\u002Ftest_hooks_skills_plugins_real.py  # hooks\u002Fskills\u002Fplugins\npython -m pytest tests\u002F -q -k \"not autoagent\"  # unit tests (no API)\n```\n\nFor ad hoc long-horizon validation, it is acceptable to run a temporary Python driver script as long as it:\n\n- uses real OpenHarness engine\u002Ftool objects\n- targets an unfamiliar repository\n- prints per-scenario JSON summaries\n- records tools, errors, turns, and token usage\n- stays attached until completion\n\n### 7. Interpret Results\n\n| Result | Meaning | Action |\n|--------|---------|--------|\n| PASS with tool calls | Feature works end-to-end | Done |\n| PASS without tool calls | Model answered from knowledge | Rewrite prompt to force tool use |\n| FAIL with exception | Code bug | Read traceback |\n| FAIL with wrong output | Model behavior issue | Check system prompt and tool schemas |\n| Timeout | Task too complex | Increase `max_turns` or simplify prompt |\n\nFor long-running real evals, refine the timeout guidance:\n\n- First check whether `max_turns` was manually set too low\n- If `max_turns=200` and the run still fails, the next suspect is wall-clock timeout, not turn count\n- Distinguish environment failures from product failures\n  - Example: missing dependency in the unfamiliar target repo is not automatically an OpenHarness regression\n  - Example: missing `srt`\u002F`bwrap`\u002F`rg` is an eval environment issue\n\n## Feature Coverage Checklist\n\n- [ ] Engine: multi-turn memory, tool chaining, parallel tools, error recovery, auto-compaction\n- [ ] Swarm: InProcessBackend lifecycle, concurrent teammates, coordinator+notifications\n- [ ] Hooks: pre_tool_use blocking → model adapts, post_tool_use firing\n- [ ] Skills: skill tool invocation → model follows instructions\n- [ ] Plugins: plugin-provided skill loaded and used in agent loop\n- [ ] Memory: YAML frontmatter parsing, body content search, context injection\n- [ ] Session: save → load → resume with context preserved\n- [ ] Providers: Anthropic client, OpenAI client (with reasoning_content), multi-turn\n- [ ] Cost: token accumulation across turns\n\n## Common Pitfalls\n\n- Testing on OpenHarness itself — agent modifies its own running code\n- Using mocks — misses serialization and API compatibility bugs\n- Single-turn only — misses context accumulation and compaction bugs\n- Artificially lowering `max_turns` during real evals — can create false failures that do not reflect product defaults\n- Not checking tool call list — model may claim tool use without calling it\n- Hardcoding paths — use `WORKSPACE` variable, skip in CI with `pytest.mark.skipif`\n- Declaring sandbox “tested” after only checking raw `srt` — verify the OpenHarness adapter path too\n- Abandoning long tasks too early — some real tasks pause for minutes before the next event arrives\n\n## Additional Resources\n\n### Reference Files\n\n- **`references\u002Ftest-patterns.md`** — Complete code templates for `make_engine`, `collect`, and each feature category\n- **`references\u002Ffeature-matrix.md`** — Detailed test cases for every OpenHarness module\n\n### Existing Test Files\n\nWorking test suites in the repo:\n- `tests\u002Ftest_merged_prs_on_autoagent.py` — PR feature validation\n- `tests\u002Ftest_real_large_tasks.py` — Large multi-step tasks\n- `tests\u002Ftest_hooks_skills_plugins_real.py` — Hooks\u002Fskills\u002Fplugins in agent loops\n- `tests\u002Ftest_untested_features.py` — Module-level integration tests\n",{"name":5,"version":57,"description":7},"0.2.0","main","7850740bd837adb32f0a109ab2af82482196c6f2","skills\u002Fharness-eval",1,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills","https:\u002F\u002Fzturnsgo.com","https:\u002F\u002Fgithub.com\u002Fshengdabai","https:\u002F\u002Fzturnsgo.com\u002F","AI × education creator — producer, not consumer. Taught 6,000+ learners; shipping learning tools & daily essays with Claude Code, practicing @xiaolai's way 🥋","2026-11-01 21:28:23",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add shengdabai\u002FTony-Claude-Code-Skills --skill harness-eval",{"tool":74,"command":75},"bunx","bunx skills add shengdabai\u002FTony-Claude-Code-Skills --skill harness-eval",{"tool":77,"command":78},"pnpm","pnpm dlx skills add shengdabai\u002FTony-Claude-Code-Skills --skill harness-eval","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fharness-eval\u002FSKILL.md",true,[82,88],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Ffeature-matrix.md","REFERENCE","参考文档",2423,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fharness-eval\u002Freferences\u002Ffeature-matrix.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Ftest-patterns.md",5959,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fharness-eval\u002Freferences\u002Ftest-patterns.md",[93,110,137,163,185,205],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":102,"publishedAt":52},"01M70P17DAD4C1C47NPH6A96EM","xiaolai-methodology","以李笑来(Li Xiaolai)的思考方式、方法论与表达风格,为用户做全方位 1-on-1 教练指导,覆盖创业、赚钱、投资、学习、自学、读书写作、用 AI、以及人生心态与自我管理。当用户提到 \"李笑来\"、\"笑来\"、\"笑来老师\"、\"扮演李笑来\"、\"找笑来聊聊\"、\"笑来怎么看\"、\"xiaolai\"、\"把时间当作朋友\"、\"通往财富自由之路\"、\"定投改变命运\"、\"韭菜的自我修养\"、\"自学是门手艺\"、\"让时间陪你慢慢变富\"、\"七年就是一辈子\"、\"元认知\"、\"活在未来\"、\"复利\u002F长期主义\"、\"践行\"、\"痴迷改进\"、\"个人商业模式\u002F出售时间\"、\"睡后收入\"、\"定投\"、\"财富自由\"、\"操作系统升级\"、\"最少必要知识\"、\"北极星提示词\"、\"用 AI 该怎么学\u002F思考\" 等任一触发词,或当用户带着关于创业\u002F赚钱\u002F投资\u002F学习\u002F职业\u002F重大人生决策的困惑、希望被人用笑来式的「逻辑 + 大白话 + 类比 + 重新定义 + 践行导向」来点醒和指导时,自动进入李笑来教练模式。","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fxiaolai-methodology\u002FSKILL.md",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",[103,104,105,106,107,108,109],"methodology","xiaolai","xiaolai methodology","朋友","投资","writing","收入",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":118,"publishedAt":52},"01M70P13NB9YP7ACJR1S861CDS","zread","Produce and consume a wiki-style knowledge base for a code repository via the `zread` CLI and its on-disk output under `.\u002F.zread\u002Fwiki\u002F`. Use this skill whenever the user wants to understand, onboard onto, explore, summarize, map, or get an overview of an unfamiliar codebase; asks for architecture docs, a project wiki, a repo walkthrough, module\u002Fpackage explanations, or \"what does this repo do\"; wants to generate, regenerate, resume, browse, or serve code documentation locally; or mentions zread \u002F zread.ai directly. Also use it proactively before diving into a large unknown repo — if `.\u002F.zread\u002Fwiki\u002Fcurrent` exists, read the generated pages instead of crawling source file-by-file; if it doesn't, consider offering to run `zread generate`. The trigger is the intent (understand a codebase through generated docs), not the literal word \"zread\".","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fzread\u002FSKILL.md",[116,117],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},[112,119,120,121,122,123,124,125,45,126,127,128,129,130,131,132,133,134,135,136],"knowledge","wiki","explore","walkthrough","project","get","knowledge base","generate","intent","cli","map","documentation","project wiki","disk","architecture","code","unknown","generated",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":147,"publishedAt":52},"01M70P10S4SWMZQ8BJAFBFDRW3","youtube","Launch the YouTube Creator Agent to auto-generate videos.\nUse when: user types \u002Fyoutube, wants to create YouTube videos,\ngenerate video content, or upload to YouTube.\nKeywords: youtube, video, 视频, 创作视频, 发布视频","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube\u002FSKILL.md",[143,144],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":145,"categoryName":146,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者",[139,148,149,150,151,152,153,154,126,155,156,157,47,158,159,160,161,162],"user","content","视频","creator","generate video","videos","launch","video content","create","upload","use","auto","types","Video","创作",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":170,"publishedAt":52},"01M70P0YE95D8BACZCGV8BK90K","youtube-script-sync","Sync, update, or optimize Tony's GetNote knowledge base `Youtube视频逐字稿` by turning each parent recording or first-person idea note into an idempotent child note containing a directly recordable YouTube long-video or Shorts script. Use when the user says “更新\u002F同步\u002F优化 Youtube视频逐字稿知识库”, or says “更新\u002F同步\u002F优化该知识库” while this knowledge base is the active context.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-script-sync\u002FSKILL.md",[169],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[171,172,139,173,174,161,175,176,177,178,179,180,181,182,125,183,47,158,119,184],"sync","script","youtube script sync","recording","shorts","first","base","person","update","active","child","optimize","idempotent","long",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":195,"publishedAt":52},"01M70P0WMV5XT0EEC0N445PW07","writing-skills","How to write SKILL.md files that trigger reliably and teach effectively. Use when creating, improving, or reviewing Claude Code skills.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fxiaolai\u002Fnlpm\u002Fskills\u002Fnlpm\u002Fwriting-skills\u002FSKILL.md",[191,192,193,194],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},{"categoryNo":145,"categoryName":146,"taxonomyType":29,"taxonomyTypeDesc":30},[196,108,197,198,199,200,134,47,158,201,202,203,204],"writing skills","how","teach","write skill","write","claude code skills","files","how to","trigger",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":213,"publishedAt":52},"01M70P0SJFR4YVJQBGMHKGSKXT","writing-rules","How to write .claude\u002Frules\u002F files that Claude actually follows. Use when creating, improving, or reviewing project rules.","https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fxiaolai\u002Fnlpm\u002Fskills\u002Fnlpm\u002Fwriting-rules\u002FSKILL.md",[211,212],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":145,"categoryName":146,"taxonomyType":29,"taxonomyTypeDesc":30},[214,108,215,123,216,202,47,197,200,158,203],"Rules","writing rules","project rules"]