[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JJDXJEEGWDJH531TH50Z2X":3,"skill-same:01M6JJDXJEEGWDJH531TH50Z2X":84},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":27,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":65,"ownerHtmlUrl":66,"ownerBlogUrl":67,"ownerDescription":68,"updatedAt":69,"install":70,"resources":83},"01M6JJDXJEEGWDJH531TH50Z2X","x-research",null,"Searches X\u002FTwitter for real-time perspectives, dev discussions, product feedback, breaking news, and expert opinions using the X API v2. Provides search with engagement sorting, user profiles, thread fetching, watchlists, and result caching. Use when: (1) user says \"x research\", \"search x for\", \"search twitter for\", \"what are people saying about\", \"what's twitter saying\", \"check x for\", \"x search\", (2) user needs recent X discourse on a topic (library releases, API changes, product launches, industry events), (3) user wants to find what devs\u002Fexperts\u002Fcommunity thinks about a topic. NOT for: posting tweets or account management.","zernie\u002Fvigiles","zernie","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1765991?v=4","01M6JJ2Z99JQ4JRCMD45T5VJXZ","01M6JJ2ZDM1H6B9QVRBZFAN009",15,"MIT","https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Ftrailofbits-skills-curated@6d05be4\u002Fplugins\u002Fx-research\u002Fskills\u002Fx-research\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"x research","research","releases","experts","sorting","people","caching","time","search x","community","expert","write","read","perspectives","x api","Bash","api","search","product","api changes","2026-11-10 10:12:30",false,"---\nname: x-research\ndescription: >\n  Searches X\u002FTwitter for real-time perspectives, dev discussions, product feedback,\n  breaking news, and expert opinions using the X API v2. Provides search with\n  engagement sorting, user profiles, thread fetching, watchlists, and result caching.\n  Use when: (1) user says \"x research\", \"search x for\", \"search twitter for\",\n  \"what are people saying about\", \"what's twitter saying\", \"check x for\", \"x search\",\n  (2) user needs recent X discourse on a topic (library releases, API changes, product\n  launches, industry events), (3) user wants to find what devs\u002Fexperts\u002Fcommunity thinks\n  about a topic. NOT for: posting tweets or account management.\nallowed-tools:\n  - Bash\n  - Read\n  - Grep\n  - Glob\n  - Write\n  - WebFetch\n---\n\n# X Research\n\nAgentic research over X\u002FTwitter. Decompose research questions into targeted searches,\niteratively refine, follow threads, deep-dive linked content, and synthesize sourced\nbriefings.\n\nFor X API details (endpoints, operators, response format): read\n`{baseDir}\u002Fskills\u002Fx-research\u002Freferences\u002Fx-api.md`.\n\n## Prerequisites\n\n- **X API Bearer Token** -- set `X_BEARER_TOKEN` (or `XAI_API_KEY`) env var\n- **Python 3.11+** and **uv** (`pip install uv` or https:\u002F\u002Fdocs.astral.sh\u002Fuv\u002F)\n\n## CLI Tool\n\nAll commands use `uv run` for automatic dependency management:\n\n### Search\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"\u003Cquery>\" [options]\n```\n\n**Options:**\n- `--sort likes|impressions|retweets|recent` -- sort order (default: likes)\n- `--since 1h|3h|12h|1d|7d` -- time filter (default: last 7 days)\n- `--min-likes N` -- filter by minimum likes\n- `--min-impressions N` -- filter by minimum impressions\n- `--pages N` -- pages to fetch, 1-5 (default: 1, 100 tweets\u002Fpage)\n- `--limit N` -- max results to display (default: 15)\n- `--quick` -- quick mode: 1 page, max 10 results, auto noise filter, 1hr cache\n- `--from-user \u003Cusername>` -- shorthand for `from:username` in query\n- `--quality` -- filter low-engagement tweets (min 10 likes, post-hoc)\n- `--no-replies` -- exclude replies\n- `--save` -- save results to `~\u002Fx-research-output\u002F`\n- `--json` -- raw JSON output\n- `--markdown` -- markdown output for research docs\n\nAuto-adds `-is:retweet` unless query already includes it. All searches display estimated API cost.\n\n**Examples:**\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"claude code\" --sort likes --limit 10\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"from:anthropic\" --sort recent\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"(cursor OR windsurf) AI editor\" --pages 2 --save\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"AI agents\" --quick\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"AI agents\" --quality --quick\n```\n\n### Profile\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py profile \u003Cusername> [--count N] [--replies] [--json]\n```\n\nFetches recent tweets from a specific user (excludes replies by default).\n\n### Thread\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py thread \u003Ctweet_id> [--pages N]\n```\n\nFetches full conversation thread by root tweet ID.\n\n### Single Tweet\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py tweet \u003Ctweet_id> [--json]\n```\n\n### Watchlist\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist                        # Show all\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist add \u003Cuser> [note]      # Add account\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist remove \u003Cuser>           # Remove\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist check                   # Check recent\n```\n\nWatchlist stored in `{baseDir}\u002Fskills\u002Fx-research\u002Fdata\u002Fwatchlist.json`.\n\n### Cache\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py cache clear\n```\n\n15-minute TTL. Avoids re-fetching identical queries.\n\n## Research Loop (Agentic)\n\nWhen doing deep research (not just a quick search), follow this loop:\n\n### 1. Decompose the Question into Queries\n\nTurn the research question into 3-5 keyword queries using X search operators:\n\n- **Core query**: Direct keywords for the topic\n- **Expert voices**: `from:` specific known experts\n- **Pain points**: Keywords like `(broken OR bug OR issue OR migration)`\n- **Positive signal**: Keywords like `(shipped OR love OR fast OR benchmark)`\n- **Links**: `url:github.com` or `url:` specific domains\n- **Noise reduction**: `-is:retweet` (auto-added), add `-is:reply` if needed\n- **Spam filter**: Add `-airdrop -giveaway -whitelist` for crypto-adjacent topics\n\n### 2. Search and Extract\n\nRun each query via CLI. After each, assess:\n- Signal or noise? Adjust operators.\n- Key voices worth searching `from:` specifically?\n- Threads worth following via `thread` command?\n- Linked resources worth deep-diving with `WebFetch`?\n\n### 3. Follow Threads\n\nWhen a tweet has high engagement or is a thread starter:\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py thread \u003Ctweet_id>\n```\n\n### 4. Deep-Dive Linked Content\n\nWhen tweets link to GitHub repos, blog posts, or docs, fetch with `WebFetch`. Prioritize links that:\n- Multiple tweets reference\n- Come from high-engagement tweets\n- Point to technical resources directly relevant to the question\n\n### 5. Synthesize\n\nGroup findings by theme, not by query:\n\n```\n### [Theme\u002FFinding Title]\n\n[1-2 sentence summary]\n\n- @username: \"[key quote]\" (NL, NI) [Tweet](url)\n- @username2: \"[another perspective]\" (NL, NI) [Tweet](url)\n\nResources shared:\n- [Resource title](url) -- [what it is]\n```\n\n### 6. Save\n\nUse `--save` flag or save manually.\n\n## Refinement Heuristics\n\n- **Too much noise?** Add `-is:reply`, use `--sort likes`, narrow keywords\n- **Too few results?** Broaden with `OR`, remove restrictive operators\n- **Spam flooding results?** Add `-$ -airdrop -giveaway -whitelist`\n- **Expert takes only?** Use `from:` or `--min-likes 50`\n- **Substance over hot takes?** Search with `has:links`\n\n## When to Use\n\n- Researching what developers\u002Fexperts\u002Fcommunity thinks about a topic\n- Getting real-time perspectives on breaking news or product launches\n- Finding technical discussions about libraries, frameworks, or APIs\n- Monitoring what key accounts are posting about\n- Gathering sourced evidence for competitive analysis or market research\n- Quick pulse check on a topic before deeper investigation\n\n## When NOT to Use\n\n- Posting tweets, replying, or managing an X account (read-only tool)\n- Historical research beyond 7 days (uses recent search endpoint only)\n- Searching non-X platforms (use web search tools instead)\n- Tasks where web search provides better results (X is best for real-time\n  opinions, discussions, and breaking news -- not reference docs)\n\n## Cost Awareness\n\nX API uses pay-per-use pricing ($0.005\u002Fpost read, $0.01\u002Fuser lookup). Quick mode\nkeeps costs under ~$0.50\u002Fsearch. Always check the cost display after each search.\nCache prevents duplicate charges. See `references\u002Fx-api.md` for full pricing.\n\n## File Structure\n\n```\nskills\u002Fx-research\u002F\n  SKILL.md           (this file)\n  scripts\u002F\n    x_search.py      (CLI entry point, run with uv)\n    x_api.py         (X API wrapper)\n    x_cache.py       (file-based cache, 15min TTL)\n    x_format.py      (terminal + markdown formatters)\n  data\u002F\n    watchlist.json   (accounts to monitor)\n    cache\u002F           (auto-managed)\n  references\u002F\n    x-api.md         (X API endpoint reference)\n```\n","# X Research\n\nAgentic research over X\u002FTwitter. Decompose research questions into targeted searches,\niteratively refine, follow threads, deep-dive linked content, and synthesize sourced\nbriefings.\n\nFor X API details (endpoints, operators, response format): read\n`{baseDir}\u002Fskills\u002Fx-research\u002Freferences\u002Fx-api.md`.\n\n## Prerequisites\n\n- **X API Bearer Token** -- set `X_BEARER_TOKEN` (or `XAI_API_KEY`) env var\n- **Python 3.11+** and **uv** (`pip install uv` or https:\u002F\u002Fdocs.astral.sh\u002Fuv\u002F)\n\n## CLI Tool\n\nAll commands use `uv run` for automatic dependency management:\n\n### Search\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"\u003Cquery>\" [options]\n```\n\n**Options:**\n- `--sort likes|impressions|retweets|recent` -- sort order (default: likes)\n- `--since 1h|3h|12h|1d|7d` -- time filter (default: last 7 days)\n- `--min-likes N` -- filter by minimum likes\n- `--min-impressions N` -- filter by minimum impressions\n- `--pages N` -- pages to fetch, 1-5 (default: 1, 100 tweets\u002Fpage)\n- `--limit N` -- max results to display (default: 15)\n- `--quick` -- quick mode: 1 page, max 10 results, auto noise filter, 1hr cache\n- `--from-user \u003Cusername>` -- shorthand for `from:username` in query\n- `--quality` -- filter low-engagement tweets (min 10 likes, post-hoc)\n- `--no-replies` -- exclude replies\n- `--save` -- save results to `~\u002Fx-research-output\u002F`\n- `--json` -- raw JSON output\n- `--markdown` -- markdown output for research docs\n\nAuto-adds `-is:retweet` unless query already includes it. All searches display estimated API cost.\n\n**Examples:**\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"claude code\" --sort likes --limit 10\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"from:anthropic\" --sort recent\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"(cursor OR windsurf) AI editor\" --pages 2 --save\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"AI agents\" --quick\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py search \"AI agents\" --quality --quick\n```\n\n### Profile\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py profile \u003Cusername> [--count N] [--replies] [--json]\n```\n\nFetches recent tweets from a specific user (excludes replies by default).\n\n### Thread\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py thread \u003Ctweet_id> [--pages N]\n```\n\nFetches full conversation thread by root tweet ID.\n\n### Single Tweet\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py tweet \u003Ctweet_id> [--json]\n```\n\n### Watchlist\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist                        # Show all\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist add \u003Cuser> [note]      # Add account\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist remove \u003Cuser>           # Remove\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py watchlist check                   # Check recent\n```\n\nWatchlist stored in `{baseDir}\u002Fskills\u002Fx-research\u002Fdata\u002Fwatchlist.json`.\n\n### Cache\n\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py cache clear\n```\n\n15-minute TTL. Avoids re-fetching identical queries.\n\n## Research Loop (Agentic)\n\nWhen doing deep research (not just a quick search), follow this loop:\n\n### 1. Decompose the Question into Queries\n\nTurn the research question into 3-5 keyword queries using X search operators:\n\n- **Core query**: Direct keywords for the topic\n- **Expert voices**: `from:` specific known experts\n- **Pain points**: Keywords like `(broken OR bug OR issue OR migration)`\n- **Positive signal**: Keywords like `(shipped OR love OR fast OR benchmark)`\n- **Links**: `url:github.com` or `url:` specific domains\n- **Noise reduction**: `-is:retweet` (auto-added), add `-is:reply` if needed\n- **Spam filter**: Add `-airdrop -giveaway -whitelist` for crypto-adjacent topics\n\n### 2. Search and Extract\n\nRun each query via CLI. After each, assess:\n- Signal or noise? Adjust operators.\n- Key voices worth searching `from:` specifically?\n- Threads worth following via `thread` command?\n- Linked resources worth deep-diving with `WebFetch`?\n\n### 3. Follow Threads\n\nWhen a tweet has high engagement or is a thread starter:\n```bash\nuv run {baseDir}\u002Fskills\u002Fx-research\u002Fscripts\u002Fx_search.py thread \u003Ctweet_id>\n```\n\n### 4. Deep-Dive Linked Content\n\nWhen tweets link to GitHub repos, blog posts, or docs, fetch with `WebFetch`. Prioritize links that:\n- Multiple tweets reference\n- Come from high-engagement tweets\n- Point to technical resources directly relevant to the question\n\n### 5. Synthesize\n\nGroup findings by theme, not by query:\n\n```\n### [Theme\u002FFinding Title]\n\n[1-2 sentence summary]\n\n- @username: \"[key quote]\" (NL, NI) [Tweet](url)\n- @username2: \"[another perspective]\" (NL, NI) [Tweet](url)\n\nResources shared:\n- [Resource title](url) -- [what it is]\n```\n\n### 6. Save\n\nUse `--save` flag or save manually.\n\n## Refinement Heuristics\n\n- **Too much noise?** Add `-is:reply`, use `--sort likes`, narrow keywords\n- **Too few results?** Broaden with `OR`, remove restrictive operators\n- **Spam flooding results?** Add `-$ -airdrop -giveaway -whitelist`\n- **Expert takes only?** Use `from:` or `--min-likes 50`\n- **Substance over hot takes?** Search with `has:links`\n\n## When to Use\n\n- Researching what developers\u002Fexperts\u002Fcommunity thinks about a topic\n- Getting real-time perspectives on breaking news or product launches\n- Finding technical discussions about libraries, frameworks, or APIs\n- Monitoring what key accounts are posting about\n- Gathering sourced evidence for competitive analysis or market research\n- Quick pulse check on a topic before deeper investigation\n\n## When NOT to Use\n\n- Posting tweets, replying, or managing an X account (read-only tool)\n- Historical research beyond 7 days (uses recent search endpoint only)\n- Searching non-X platforms (use web search tools instead)\n- Tasks where web search provides better results (X is best for real-time\n  opinions, discussions, and breaking news -- not reference docs)\n\n## Cost Awareness\n\nX API uses pay-per-use pricing ($0.005\u002Fpost read, $0.01\u002Fuser lookup). Quick mode\nkeeps costs under ~$0.50\u002Fsearch. Always check the cost display after each search.\nCache prevents duplicate charges. See `references\u002Fx-api.md` for full pricing.\n\n## File Structure\n\n```\nskills\u002Fx-research\u002F\n  SKILL.md           (this file)\n  scripts\u002F\n    x_search.py      (CLI entry point, run with uv)\n    x_api.py         (X API wrapper)\n    x_cache.py       (file-based cache, 15min TTL)\n    x_format.py      (terminal + markdown formatters)\n  data\u002F\n    watchlist.json   (accounts to monitor)\n    cache\u002F           (auto-managed)\n  references\u002F\n    x-api.md         (X API endpoint reference)\n```\n",{"name":5,"description":53,"allowed-tools":54},"Searches X\u002FTwitter for real-time perspectives, dev discussions, product feedback, breaking news, and expert opinions using the X API v2. Provides search with engagement sorting, user profiles, thread fetching, watchlists, and result caching. Use when: (1) user says \"x research\", \"search x for\", \"search twitter for\", \"what are people saying about\", \"what's twitter saying\", \"check x for\", \"x search\", (2) user needs recent X discourse on a topic (library releases, API changes, product launches, industry events), (3) user wants to find what devs\u002Fexperts\u002Fcommunity thinks about a topic. NOT for: posting tweets or account management.\n",[43,55,56,57,58,59],"Read","Grep","Glob","Write","WebFetch","main","1a1de50f62f5c1093a46455488dcf6e8157c6182","test\u002Fdogfood\u002Ftrailofbits-skills-curated@6d05be4\u002Fplugins\u002Fx-research\u002Fskills\u002Fx-research",4,"https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles","https:\u002F\u002Fvigiles.sh","https:\u002F\u002Fgithub.com\u002Fzernie","https:\u002F\u002Fzernie.com?utm_source=github","Senior Fullstack Software Developer.\r\n\r\nRuby on Rails, React.JS\u002FRedux, TypeScript, Next.js.","2026-11-02 22:19:44",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add zernie\u002Fvigiles --skill x-research",{"tool":76,"command":77},"bunx","bunx skills add zernie\u002Fvigiles --skill x-research",{"tool":79,"command":80},"pnpm","pnpm dlx skills add zernie\u002Fvigiles --skill x-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Ftrailofbits-skills-curated@6d05be4\u002Fplugins\u002Fx-research\u002Fskills\u002Fx-research\u002FSKILL.md",true,[],[85,113,134,164,188,208],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":94,"publishedAt":48},"01M6JJE6A64FF49TKDQ6F4JD0Q","wcag-audit-patterns","Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixing WCAG violations, or implementing accessible design patterns.","https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Fwshobson-accessibility@cf6059d\u002Fskills\u002Fwcag-audit-patterns\u002FSKILL.md",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量",[95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112],"audit","WCAG","wcag audit patterns","patterns","wcag audit","testing","design","accessibility","verification","remediation","use","design patterns","guidance","automated testing","with","manual","implementing","for",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":120,"publishedAt":48},"01M6JJE24VZ3K0PRFHRJA4SNWG","screen-reader-testing","Test web applications with screen readers including VoiceOver, NVDA, and JAWS. Use when validating screen reader compatibility, debugging accessibility issues, or ensuring assistive technology support.","https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Fwshobson-accessibility@cf6059d\u002Fskills\u002Fscreen-reader-testing\u002FSKILL.md",[119],{"categoryNo":92,"categoryName":93,"taxonomyType":20,"taxonomyTypeDesc":21},[100,121,122,123,124,125,126,102,127,128,129,105,130,131,132,133,109],"reader","screen reader testing","screen","support","test","web","debugging","issues","applications","test web","voiceover","compatibility","technology",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":146,"publishedAt":48},"01M6JJDQQJE9RZWEN0E3FKB5KF","wooyun-legacy","Provides web vulnerability testing methodology distilled from 88,636 real-world cases from the WooYun vulnerability database (2010-2016). Use when performing penetration testing, security audits, code reviews for security flaws, or vulnerability research. Covers SQL injection, XSS, command execution, file upload, path traversal, unauthorized access, information disclosure, and business logic flaws.","https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Ftrailofbits-skills-curated@6d05be4\u002Fplugins\u002Fwooyun-legacy\u002Fskills\u002Fwooyun-legacy\u002FSKILL.md",[140,141,144,145],{"categoryNo":92,"categoryName":93,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":142,"categoryName":143,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},[147,148,100,149,150,151,152,153,154,155,43,156,157,40,158,159,160,161,162,163],"Legacy","wooyun legacy","security","sql","code","database","injection","xss","vulnerability","access","command","Performing Penetration Testing","world","penetration","information","reviews","path",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":173,"publishedAt":48},"01M6JJDJRQ8D4VEVYB2YS0ZG7G","skill-extractor","Extracts reusable skills from work sessions. Use when: (1) a non-obvious problem was solved worth preserving, (2) a pattern was discovered that would help future sessions, (3) a workaround or debugging technique needs documentation. Manual invocation only via \u002Fskill-extractor command - no automatic triggers or hooks.","https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Ftrailofbits-skills-curated@6d05be4\u002Fplugins\u002Fskill-extractor\u002Fskills\u002Fskill-extractor\u002FSKILL.md",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[174,175,176,127,177,178,179,157,40,39,105,180,181,182,183,184,185,186,110,187],"skill extractor","sessions","documentation","help","grep","work","only","hooks","reusable","pattern","triggers","problem","via","websearch",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":194,"publishedAt":48},"01M6JJDE8FBAP7YD2Y3ES050JQ","security-awareness","Teaches agents to recognize and avoid security threats during normal activity. Covers phishing detection, credential protection, domain verification, and social engineering defense. Use when building or operating agents that access email, credential vaults, web browsers, or sensitive data.","https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Ftrailofbits-skills-curated@6d05be4\u002Fplugins\u002Fsecurity-awareness\u002Fskills\u002Fsecurity-awareness\u002FSKILL.md",[],[149,195,196,126,103,197,198,156,178,40,199,105,200,201,202,203,204,205,206,207],"awareness","security awareness","email","phishing","webfetch","protection","social","social engineering","domain","vaults","operating","activity","phishing detection",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":219,"publishedAt":48},"01M6JJD9WN8E2GPBKNKB43ET77","scv-scan","Audits Solidity codebases for smart contract vulnerabilities using a four-phase workflow (cheatsheet loading, codebase sweep, deep validation, reporting) covering 36 vulnerability classes. Use when auditing Solidity contracts for security issues, performing smart contract vulnerability scans, or reviewing Solidity code for common exploit patterns.","https:\u002F\u002Fgithub.com\u002Fzernie\u002Fvigiles\u002Fblob\u002Fmain\u002Ftest\u002Fdogfood\u002Ftrailofbits-skills-curated@6d05be4\u002Fplugins\u002Fscv-scan\u002Fskills\u002Fscv-scan\u002FSKILL.md",[214,215,216],{"categoryNo":171,"categoryName":172,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},{"categoryNo":217,"categoryName":218,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000037","律师与法务",[220,221,222,149,223,151,128,224,225,155,226,43,227,40,39,228,98,229,230,231],"scan","scv","scv scan","workflow","reporting","solidity","cheatsheet","common","exploit","deep","loading","smart contract"]