[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M794D6FS4XHPEX3794FN4VVT":3,"skill-same:01M794D6FS4XHPEX3794FN4VVT":61},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":14,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":46,"install":47,"resources":60},"01M794D6FS4XHPEX3794FN4VVT","agenttrace-session-audit",null,"Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.","Elogic360\u002Fsoftware-factory","Elogic360","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F156535519?v=4","01M74NH3F0RB13PWB7JZDE2V80","elogic360","01M74NH3FPC65082MDZVMFHDM3",0,"MIT","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fintegrations\u002Fantigravity-skills\u002Fskills\u002Fagenttrace-session-audit\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: agenttrace-session-audit\ndescription: \"Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.\"\ncategory: development\nrisk: safe\nsource: community\nsource_repo: luoyuctl\u002Fagenttrace\nsource_type: community\ndate_added: \"2026-05-10\"\nauthor: luoyuctl\ntags: [ai-coding, observability, cost-tracking, session-analysis]\ntools: [claude, cursor, gemini, codex-cli]\nlicense: \"MIT\"\nlicense_source: \"https:\u002F\u002Fgithub.com\u002Fluoyuctl\u002Fagenttrace\u002Fblob\u002Fmaster\u002FLICENSE\"\n---\n\n# agenttrace Session Audit\n\n## Overview\n\nUse this skill to inspect local AI coding-agent sessions with\n[agenttrace](https:\u002F\u002Fgithub.com\u002Fluoyuctl\u002Fagenttrace). It focuses on the process\nbehind a run: token and cost spikes, tool failures, retry loops, latency gaps,\nanomalies, health scores, and session-to-session diffs.\n\nagenttrace is local-first and reads session logs from tools such as Claude Code,\nCodex CLI, Gemini CLI, Aider, Cursor exports, OpenCode, Qwen Code, Kimi, and\ngeneric JSON or JSONL traces.\n\n## When to Use This Skill\n\n- Use when a user asks why an AI coding run was slow, expensive, shallow, or unreliable.\n- Use when reviewing local agent logs before retrying a failed or suspicious task.\n- Use when building a lightweight CI health gate for AI-assisted coding sessions.\n- Use when comparing two attempts and looking for changed tool paths, retries, or cost patterns.\n\n## How It Works\n\n### Step 1: Discover Available Sessions\n\nPrefer an installed `agenttrace` binary when it is available on `PATH`. If the\ncurrent repository is `luoyuctl\u002Fagenttrace`, use `go run .\u002Fcmd\u002Fagenttrace`\ninstead.\n\n```bash\nagenttrace --doctor\nagenttrace --overview\n```\n\nIf no sessions are detected, report the directories checked by `--doctor` and\nask for the exported session file or log directory.\n\n### Step 2: Produce a Human-Readable Audit\n\nUse Markdown when the user wants a concise report they can inspect or share.\n\n```bash\nagenttrace --overview -f markdown -o agenttrace-overview.md\n```\n\nIn the report, lead with the highest-risk sessions and explain why they matter:\ncritical anomalies, repeated tool failures, token or cost waste, long latency\ngaps, low health scores, and suspiciously shallow sessions.\n\n### Step 3: Inspect One Session or Directory\n\nUse the latest session for a quick check, or pass an explicit export path when\nthe user provides one.\n\n```bash\nagenttrace --latest\nagenttrace --latest -f json\nagenttrace path\u002Fto\u002Fsession-or-export.json\nagenttrace --overview -d path\u002Fto\u002Fsession-dir\n```\n\n### Step 4: Compare Attempts When Semantics Matter\n\nToken and latency metrics can look healthy even when an agent confidently takes\nthe wrong implementation path. When the risk is semantic drift, pair the trace\naudit with a diff against a previous or known-good attempt.\n\nLook for:\n\n- changed files or commands that diverge from the intended task\n- missing tests or verification steps compared with the reference attempt\n- repeated edits around the same files without a clear reason\n- lower cost that came from skipping necessary exploration\n\n### Step 5: Add Automation Gates\n\nFor CI or repeatable team workflows, use JSON output or health thresholds.\n\n```bash\nagenttrace --overview -f json -o agenttrace-overview.json\nagenttrace --overview --fail-under-health 80 --fail-on-critical --max-tool-fail-rate 15\n```\n\nTune thresholds to the project. A strict gate is useful for critical workflows;\na reporting-only command is better while the team is learning its baseline.\n\n## Examples\n\n### Quick Local Review\n\n```bash\nagenttrace --overview\nagenttrace --latest\n```\n\nUse this after a long coding-agent run to decide whether the next prompt should\nsplit the task, avoid a failing tool path, add missing tests, or reset context.\n\n### CI Health Check\n\n```bash\nagenttrace --overview --fail-under-health 80 --fail-on-critical\n```\n\nUse this when agent session logs are available in CI and the team wants a simple\nguard against critical anomalies or unhealthy runs.\n\n## Best Practices\n\n- Start with `--doctor` when session discovery is uncertain.\n- Report missing fields plainly; do not invent cost, model, latency, or health data.\n- Treat prompts, code, and session contents as private local data.\n- Prefer JSON output for automation and Markdown output for human review.\n- Use trace metrics for process failures and diff\u002Freference review for semantic drift.\n\n## Limitations\n\n- agenttrace can only analyze logs that are present locally or provided as exports.\n- Some agents do not expose enough fields to infer cost, model, cache use, or latency.\n- Healthy trace metrics do not prove the final code is correct; still run tests and review diffs.\n- CI gates should start as advisory until the team understands normal baseline behavior.\n\n## Security & Safety Notes\n\n- Do not upload private session logs to external services unless the user explicitly approves it.\n- Do not overwrite user reports unless they requested that exact output path.\n- Avoid printing secrets found in prompts, tool output, environment variables, or logs.\n\n## Common Pitfalls\n\n- **Problem:** No sessions are found.\n  **Solution:** Run `agenttrace --doctor`, then point agenttrace at the exported file or log directory.\n\n- **Problem:** A run looks cheap and fast but produced the wrong refactor.\n  **Solution:** Compare the session against a prior attempt or known-good diff; cost metrics alone will miss semantic drift.\n\n- **Problem:** CI fails too often after adding a health gate.\n  **Solution:** Start with JSON or Markdown reporting, inspect normal baselines, then tighten thresholds gradually.\n\n## Related Skills\n\n- `@langfuse` - Use for production LLM application tracing and evaluation.\n- `@observability-engineer` - Use for broader service monitoring, SLOs, and incident workflows.\n","# agenttrace Session Audit\n\n## Overview\n\nUse this skill to inspect local AI coding-agent sessions with\n[agenttrace](https:\u002F\u002Fgithub.com\u002Fluoyuctl\u002Fagenttrace). It focuses on the process\nbehind a run: token and cost spikes, tool failures, retry loops, latency gaps,\nanomalies, health scores, and session-to-session diffs.\n\nagenttrace is local-first and reads session logs from tools such as Claude Code,\nCodex CLI, Gemini CLI, Aider, Cursor exports, OpenCode, Qwen Code, Kimi, and\ngeneric JSON or JSONL traces.\n\n## When to Use This Skill\n\n- Use when a user asks why an AI coding run was slow, expensive, shallow, or unreliable.\n- Use when reviewing local agent logs before retrying a failed or suspicious task.\n- Use when building a lightweight CI health gate for AI-assisted coding sessions.\n- Use when comparing two attempts and looking for changed tool paths, retries, or cost patterns.\n\n## How It Works\n\n### Step 1: Discover Available Sessions\n\nPrefer an installed `agenttrace` binary when it is available on `PATH`. If the\ncurrent repository is `luoyuctl\u002Fagenttrace`, use `go run .\u002Fcmd\u002Fagenttrace`\ninstead.\n\n```bash\nagenttrace --doctor\nagenttrace --overview\n```\n\nIf no sessions are detected, report the directories checked by `--doctor` and\nask for the exported session file or log directory.\n\n### Step 2: Produce a Human-Readable Audit\n\nUse Markdown when the user wants a concise report they can inspect or share.\n\n```bash\nagenttrace --overview -f markdown -o agenttrace-overview.md\n```\n\nIn the report, lead with the highest-risk sessions and explain why they matter:\ncritical anomalies, repeated tool failures, token or cost waste, long latency\ngaps, low health scores, and suspiciously shallow sessions.\n\n### Step 3: Inspect One Session or Directory\n\nUse the latest session for a quick check, or pass an explicit export path when\nthe user provides one.\n\n```bash\nagenttrace --latest\nagenttrace --latest -f json\nagenttrace path\u002Fto\u002Fsession-or-export.json\nagenttrace --overview -d path\u002Fto\u002Fsession-dir\n```\n\n### Step 4: Compare Attempts When Semantics Matter\n\nToken and latency metrics can look healthy even when an agent confidently takes\nthe wrong implementation path. When the risk is semantic drift, pair the trace\naudit with a diff against a previous or known-good attempt.\n\nLook for:\n\n- changed files or commands that diverge from the intended task\n- missing tests or verification steps compared with the reference attempt\n- repeated edits around the same files without a clear reason\n- lower cost that came from skipping necessary exploration\n\n### Step 5: Add Automation Gates\n\nFor CI or repeatable team workflows, use JSON output or health thresholds.\n\n```bash\nagenttrace --overview -f json -o agenttrace-overview.json\nagenttrace --overview --fail-under-health 80 --fail-on-critical --max-tool-fail-rate 15\n```\n\nTune thresholds to the project. A strict gate is useful for critical workflows;\na reporting-only command is better while the team is learning its baseline.\n\n## Examples\n\n### Quick Local Review\n\n```bash\nagenttrace --overview\nagenttrace --latest\n```\n\nUse this after a long coding-agent run to decide whether the next prompt should\nsplit the task, avoid a failing tool path, add missing tests, or reset context.\n\n### CI Health Check\n\n```bash\nagenttrace --overview --fail-under-health 80 --fail-on-critical\n```\n\nUse this when agent session logs are available in CI and the team wants a simple\nguard against critical anomalies or unhealthy runs.\n\n## Best Practices\n\n- Start with `--doctor` when session discovery is uncertain.\n- Report missing fields plainly; do not invent cost, model, latency, or health data.\n- Treat prompts, code, and session contents as private local data.\n- Prefer JSON output for automation and Markdown output for human review.\n- Use trace metrics for process failures and diff\u002Freference review for semantic drift.\n\n## Limitations\n\n- agenttrace can only analyze logs that are present locally or provided as exports.\n- Some agents do not expose enough fields to infer cost, model, cache use, or latency.\n- Healthy trace metrics do not prove the final code is correct; still run tests and review diffs.\n- CI gates should start as advisory until the team understands normal baseline behavior.\n\n## Security & Safety Notes\n\n- Do not upload private session logs to external services unless the user explicitly approves it.\n- Do not overwrite user reports unless they requested that exact output path.\n- Avoid printing secrets found in prompts, tool output, environment variables, or logs.\n\n## Common Pitfalls\n\n- **Problem:** No sessions are found.\n  **Solution:** Run `agenttrace --doctor`, then point agenttrace at the exported file or log directory.\n\n- **Problem:** A run looks cheap and fast but produced the wrong refactor.\n  **Solution:** Compare the session against a prior attempt or known-good diff; cost metrics alone will miss semantic drift.\n\n- **Problem:** CI fails too often after adding a health gate.\n  **Solution:** Start with JSON or Markdown reporting, inspect normal baselines, then tighten thresholds gradually.\n\n## Related Skills\n\n- `@langfuse` - Use for production LLM application tracing and evaluation.\n- `@observability-engineer` - Use for broader service monitoring, SLOs, and incident workflows.\n",{"name":5,"risk":24,"tags":25,"tools":30,"author":35,"source":36,"license":15,"category":37,"date_added":38,"description":7,"source_repo":39,"source_type":36,"license_source":40},"safe",[26,27,28,29],"ai-coding","observability","cost-tracking","session-analysis",[31,32,33,34],"claude","cursor","gemini","codex-cli","luoyuctl","community","development","2026-05-10","luoyuctl\u002Fagenttrace","https:\u002F\u002Fgithub.com\u002Fluoyuctl\u002Fagenttrace\u002Fblob\u002Fmaster\u002FLICENSE","main","c475fc64e94e803b50d14a791dfee8853fbb2c5c","integrations\u002Fantigravity-skills\u002Fskills\u002Fagenttrace-session-audit","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory","https:\u002F\u002Fgithub.com\u002FElogic360","2026-11-08 22:21:22",{"source":8,"skillName":5,"commands":48,"prompt":58,"zipAvailable":59},[49,52,55],{"tool":50,"command":51},"npx","npx skills add Elogic360\u002Fsoftware-factory --skill agenttrace-session-audit",{"tool":53,"command":54},"bunx","bunx skills add Elogic360\u002Fsoftware-factory --skill agenttrace-session-audit",{"tool":56,"command":57},"pnpm","pnpm dlx skills add Elogic360\u002Fsoftware-factory --skill agenttrace-session-audit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fintegrations\u002Fantigravity-skills\u002Fskills\u002Fagenttrace-session-audit\u002FSKILL.md",true,[],[62,70,77,84,91,98],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":66,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":19},"01M79G206MT3J6S0GY3C45P2KV","vss-summarize-video","Use to summarize a recorded video via the LVS summarization microservice (HITL-gated) with a VLM fallback. Not for report generation or live RTSP captioning.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-summarize-video\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":66,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M79G1WN63PGRE8CKD47WEBQ5","vss-setup-video-analytics-api","Use to deploy the vss-video-analytics-api REST service standalone (config-source, data-log bind, Elasticsearch, optional Kafka). Not for full warehouse deploy.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-setup-video-analytics-api\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":66,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M79G1S9F7VY3EF6H53PRP5AN","vss-setup-behavior-analytics","Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-setup-behavior-analytics\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":66,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M79G1N1S1Y27PFZPYMNK1MTQ","vss-search-archive","Use this skill to run top-level VSS fusion search on archived video, or to ingest video files \u002F RTSP streams for search. Do NOT use for ad-hoc visual Q&A (use vss-ask-video), live captioning (use vss-deploy-dense-captioning), or video summarization and reports (use vss-summarize-video).","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-search-archive\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":66,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":19},"01M79G1H6WN0Q6C0VCAXCZX6B7","vss-query-analytics","Use this skill when reading video-analytics metrics, incidents, alerts, and sensor data via the VA-MCP server (port 9901). Not for live VLM or incident-range narrative reports.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-query-analytics\u002FSKILL.md",[],[],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":66,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":19},"01M79G1DYPNXQS7QHZ0AAF5FX5","vss-manage-video-io-storage","Use to call the VIOS REST API (sensor list, timelines, clip extraction, snapshots, add\u002Fdelete sensors and streams). Not for VLM inference or search.","https:\u002F\u002Fgithub.com\u002FElogic360\u002Fsoftware-factory\u002Fblob\u002Fmain\u002Fskills\u002Fnvidia\u002Fvss-manage-video-io-storage\u002FSKILL.md",[],[]]