[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6GSK7MX2DH0JFJDDCQAGQYG":3,"skill-same:01M6GSK7MX2DH0JFJDDCQAGQYG":70},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":14,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":63},"01M6GSK7MX2DH0JFJDDCQAGQYG","ruminate",null,"Mine past conversations for uncaptured patterns, corrections, and knowledge. Cross-references with existing brain content. Triggers on \"ruminate\", \"mine my history\", \"what have I been working on\", \"review past sessions\", \"extract learnings\".","japurcell\u002Fskills","japurcell","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F10947458?v=4","01M6GSJK1YAG95R6VHF47V30KY","Adam Purcell","01M6GSJK6817NVR5FVDMFFNJ6H",0,"MIT","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fruminate\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"with","Existing","for","python3","content","patterns","my","brain","past","references","review","learnings","knowledge","true","triggers","sessions","conversations","history","mine","2026-11-10 10:12:30",false,"---\nname: ruminate\ndescription: Mine past conversations for uncaptured patterns, corrections, and knowledge. Cross-references with existing brain content. Triggers on \"ruminate\", \"mine my history\", \"what have I been working on\", \"review past sessions\", \"extract learnings\".\ncompatibility: Requires python3\ndisable-model-invocation: true\n---\n\n# Ruminate\n\nMine conversation history for brain-worthy knowledge that was never captured. Complements `reflect` (current session) and `meditate` (brain vault audit) by looking at the full archive of past conversations across available providers.\n\n## Process\n\nTrack each step through the available planning tools or a short checklist.\n\n### 1. Read the brain\n\nCreate one temporary directory with Python's `tempfile.mkdtemp(prefix=\"ruminate-\")`. Retain its exact path for extraction, snapshot, findings, and cleanup. Build the brain snapshot inside it:\n\n`sh .agents\u002Fskills\u002Fmeditate\u002Fscripts\u002Fsnapshot.sh brain\u002F \u003Ctemporary-directory>\u002Fbrain-snapshot.md`\n\nReplace the placeholder with the retained temporary path. Pass the snapshot path to each analysis agent. This avoids loading the full brain into the ruminate orchestrator's context.\n\n### 2. Locate conversations\n\nFind available supported provider roots:\n\n1. Claude project directory:\n   `~\u002F.claude\u002Fprojects\u002F-\u003Ccwd-with-dashes-replacing-slashes>\u002F`\n2. Codex sessions root:\n   `~\u002F.codex\u002Fsessions\u002F`\n\nThe bundled extractor supports Claude and Codex JSONL formats only. Other provider histories require a separate supported extraction route; do not pass unsupported options to this script.\n\n### 3. Extract conversations\n\nRun the extraction script to parse both JSONL formats into readable text and split into batches:\n\n```python\nfrom pathlib import Path\nimport subprocess\nimport sys\n\nsubprocess.run([\n    sys.executable, \".agents\u002Fskills\u002Fruminate\u002Fscripts\u002Fextract-conversations.py\",\n    \"\u003Ctemporary-directory>\u002Fextracted\",\n    \"--claude-dir\", str(Path.home() \u002F \".claude\u002Fprojects\u002F\u003Cproject-slug>\"),\n    \"--codex-dir\", str(Path.home() \u002F \".codex\u002Fsessions\"),\n    \"--cwd\", str(Path.cwd()), \"--batches\", \"2\",\n], check=True)\n```\n\nReplace placeholders with actual temporary and project paths; omit unavailable provider options. Choose the batch count based on total extracted conversations (Claude + Codex): ~1 batch per 20 conversations, minimum 2, maximum 10.\n\nKeep `--cwd` for project-scoped extraction: it includes only conversations with matching working-directory metadata, skipping different or missing attribution even within a provider's project directory. Omitting `--cwd` leaves extraction unfiltered by project.\n\n### 4. Spawn analysis team\n\nAnalyze each batch with available general analysis subagents, following the model-routing skill when available and respecting harness concurrency limits. Use bounded local passes if delegation is unavailable.\n\nEach agent's prompt should include:\n\n- The actual batch manifest path (`\u003Ctemporary-directory>\u002Fextracted\u002Fbatches\u002Fbatch_N.txt`)\n- The actual output path (`\u003Ctemporary-directory>\u002Ffindings_N.md`)\n- The list of topics **already captured** in the brain (compiled from step 1) - so agents skip known knowledge\n- A reminder that each extracted file includes provider\u002Fsource metadata headers (`[PROVIDER]`, `[CWD]`, `[SOURCE_FILE]`) and should be used as evidence context\n- Evidence rules: distinguish human messages from injected skill\u002Fsystem text, scripted probes, and evaluation prompts, even when stored with a `user` role. Only attributable human messages support user-preference findings.\n- Deduplication rules: group copied transcripts and guardian\u002Fapproval review logs by original conversation, using root\u002Fthread metadata and overlapping message IDs or transcript content. Separate files alone do not establish independent conversations; report unique-conversation counts and uncertain attribution.\n- Quote verification: check each retained quote against the full raw `[SOURCE_FILE]`, including speaker and surrounding context. Record the source line or event and original conversation identity. Extraction may truncate messages; inspect retained-transcript sections even when expected root-conversation markers are absent.\n- Instructions to extract from each conversation:\n  - **User corrections**: times the user corrected the assistant's approach, code, or understanding\n  - **Recurring preferences**: things the user explicitly asked for or pushed back on repeatedly\n  - **Technical learnings**: codebase-specific knowledge, gotchas, patterns discovered\n  - **Workflow patterns**: how the user prefers to work\n  - **Frustrations**: friction points, wasted effort, things that went wrong\n  - **Skills wished for**: capabilities the user expressed wanting\n\nAgents write structured findings to their output files.\n\n### 5. Synthesize\n\nAfter all agents complete, read all findings files. Cross-reference with existing brain content. Deduplicate across batches. Before presenting a proposed finding, recheck its cited raw evidence and unique-conversation count; an analysis agent's summary alone is not proof.\n\n**Filter by frequency and impact.** Most findings won't be worth adding. Apply these filters before presenting:\n\n- **Frequency**: Did this come up in multiple conversations, or was the user correcting the same mistake repeatedly? One-off corrections are usually not worth a brain entry - the brain should capture _patterns_, not incidents.\n- **Factual accuracy**: Is something in the brain now wrong? (e.g. a rule was disabled but the brain still documents it as active). These are always worth fixing regardless of frequency.\n- **Impact**: Would failing to capture this cause repeated wasted effort in future sessions? A gotcha that cost 5 minutes once is low-impact. A pattern that caused 3 rounds of corrections is high-impact.\n\n**Discard aggressively.** It's better to present 3 high-signal findings than 9 that include noise. If a finding only happened once and isn't a factual correction, skip it.\n\n### 6. Present and apply\n\nPresent findings to the user in a table with columns: finding, frequency\u002Fevidence, and proposed action. Be honest about which findings are one-offs vs. recurring patterns - let the user decide what's worth adding.\n\n**Route skill-specific learnings.** Check if any findings are about how a specific skill should work - its process, prompts, edge cases, or troubleshooting. Update the skill's SKILL.md or references\u002F directly. Read the skill first to avoid duplicating or contradicting existing content.\n\nApply only the changes the user approves. Follow brain writing conventions:\n\n- One topic per file, organized in directories\n- Use file-relative Markdown links to connect related notes\n- Maintain curated indexes through the brain skill and let hooks reconcile the root inventory\n- Default to updating existing notes over creating new ones\n\n### 7. Clean up\n\nAfter all analysis finishes, remove only the temporary directory this invocation created. Replace the placeholder with the exact retained path, never a provider history directory:\n\n```python\nimport shutil\nshutil.rmtree(\"\u003Ctemporary-directory>\")\n```\n\n## Guidelines\n\n- **Filter aggressively.** Most conversations will have low signal - automated tasks, trivial exchanges, already-captured knowledge. Only surface what's genuinely new and impactful.\n- **Prefer reduction.** If a finding is a special case of an existing brain principle, update the existing note rather than creating a new one.\n- **Quote the user.** When a finding stems from a direct user correction, include the user's words and source file path - they carry the most signal about what matters.\n- **Shut down agents** when analysis is complete. Don't leave them idle.\n","# Ruminate\n\nMine conversation history for brain-worthy knowledge that was never captured. Complements `reflect` (current session) and `meditate` (brain vault audit) by looking at the full archive of past conversations across available providers.\n\n## Process\n\nTrack each step through the available planning tools or a short checklist.\n\n### 1. Read the brain\n\nCreate one temporary directory with Python's `tempfile.mkdtemp(prefix=\"ruminate-\")`. Retain its exact path for extraction, snapshot, findings, and cleanup. Build the brain snapshot inside it:\n\n`sh .agents\u002Fskills\u002Fmeditate\u002Fscripts\u002Fsnapshot.sh brain\u002F \u003Ctemporary-directory>\u002Fbrain-snapshot.md`\n\nReplace the placeholder with the retained temporary path. Pass the snapshot path to each analysis agent. This avoids loading the full brain into the ruminate orchestrator's context.\n\n### 2. Locate conversations\n\nFind available supported provider roots:\n\n1. Claude project directory:\n   `~\u002F.claude\u002Fprojects\u002F-\u003Ccwd-with-dashes-replacing-slashes>\u002F`\n2. Codex sessions root:\n   `~\u002F.codex\u002Fsessions\u002F`\n\nThe bundled extractor supports Claude and Codex JSONL formats only. Other provider histories require a separate supported extraction route; do not pass unsupported options to this script.\n\n### 3. Extract conversations\n\nRun the extraction script to parse both JSONL formats into readable text and split into batches:\n\n```python\nfrom pathlib import Path\nimport subprocess\nimport sys\n\nsubprocess.run([\n    sys.executable, \".agents\u002Fskills\u002Fruminate\u002Fscripts\u002Fextract-conversations.py\",\n    \"\u003Ctemporary-directory>\u002Fextracted\",\n    \"--claude-dir\", str(Path.home() \u002F \".claude\u002Fprojects\u002F\u003Cproject-slug>\"),\n    \"--codex-dir\", str(Path.home() \u002F \".codex\u002Fsessions\"),\n    \"--cwd\", str(Path.cwd()), \"--batches\", \"2\",\n], check=True)\n```\n\nReplace placeholders with actual temporary and project paths; omit unavailable provider options. Choose the batch count based on total extracted conversations (Claude + Codex): ~1 batch per 20 conversations, minimum 2, maximum 10.\n\nKeep `--cwd` for project-scoped extraction: it includes only conversations with matching working-directory metadata, skipping different or missing attribution even within a provider's project directory. Omitting `--cwd` leaves extraction unfiltered by project.\n\n### 4. Spawn analysis team\n\nAnalyze each batch with available general analysis subagents, following the model-routing skill when available and respecting harness concurrency limits. Use bounded local passes if delegation is unavailable.\n\nEach agent's prompt should include:\n\n- The actual batch manifest path (`\u003Ctemporary-directory>\u002Fextracted\u002Fbatches\u002Fbatch_N.txt`)\n- The actual output path (`\u003Ctemporary-directory>\u002Ffindings_N.md`)\n- The list of topics **already captured** in the brain (compiled from step 1) - so agents skip known knowledge\n- A reminder that each extracted file includes provider\u002Fsource metadata headers (`[PROVIDER]`, `[CWD]`, `[SOURCE_FILE]`) and should be used as evidence context\n- Evidence rules: distinguish human messages from injected skill\u002Fsystem text, scripted probes, and evaluation prompts, even when stored with a `user` role. Only attributable human messages support user-preference findings.\n- Deduplication rules: group copied transcripts and guardian\u002Fapproval review logs by original conversation, using root\u002Fthread metadata and overlapping message IDs or transcript content. Separate files alone do not establish independent conversations; report unique-conversation counts and uncertain attribution.\n- Quote verification: check each retained quote against the full raw `[SOURCE_FILE]`, including speaker and surrounding context. Record the source line or event and original conversation identity. Extraction may truncate messages; inspect retained-transcript sections even when expected root-conversation markers are absent.\n- Instructions to extract from each conversation:\n  - **User corrections**: times the user corrected the assistant's approach, code, or understanding\n  - **Recurring preferences**: things the user explicitly asked for or pushed back on repeatedly\n  - **Technical learnings**: codebase-specific knowledge, gotchas, patterns discovered\n  - **Workflow patterns**: how the user prefers to work\n  - **Frustrations**: friction points, wasted effort, things that went wrong\n  - **Skills wished for**: capabilities the user expressed wanting\n\nAgents write structured findings to their output files.\n\n### 5. Synthesize\n\nAfter all agents complete, read all findings files. Cross-reference with existing brain content. Deduplicate across batches. Before presenting a proposed finding, recheck its cited raw evidence and unique-conversation count; an analysis agent's summary alone is not proof.\n\n**Filter by frequency and impact.** Most findings won't be worth adding. Apply these filters before presenting:\n\n- **Frequency**: Did this come up in multiple conversations, or was the user correcting the same mistake repeatedly? One-off corrections are usually not worth a brain entry - the brain should capture _patterns_, not incidents.\n- **Factual accuracy**: Is something in the brain now wrong? (e.g. a rule was disabled but the brain still documents it as active). These are always worth fixing regardless of frequency.\n- **Impact**: Would failing to capture this cause repeated wasted effort in future sessions? A gotcha that cost 5 minutes once is low-impact. A pattern that caused 3 rounds of corrections is high-impact.\n\n**Discard aggressively.** It's better to present 3 high-signal findings than 9 that include noise. If a finding only happened once and isn't a factual correction, skip it.\n\n### 6. Present and apply\n\nPresent findings to the user in a table with columns: finding, frequency\u002Fevidence, and proposed action. Be honest about which findings are one-offs vs. recurring patterns - let the user decide what's worth adding.\n\n**Route skill-specific learnings.** Check if any findings are about how a specific skill should work - its process, prompts, edge cases, or troubleshooting. Update the skill's SKILL.md or references\u002F directly. Read the skill first to avoid duplicating or contradicting existing content.\n\nApply only the changes the user approves. Follow brain writing conventions:\n\n- One topic per file, organized in directories\n- Use file-relative Markdown links to connect related notes\n- Maintain curated indexes through the brain skill and let hooks reconcile the root inventory\n- Default to updating existing notes over creating new ones\n\n### 7. Clean up\n\nAfter all analysis finishes, remove only the temporary directory this invocation created. Replace the placeholder with the exact retained path, never a provider history directory:\n\n```python\nimport shutil\nshutil.rmtree(\"\u003Ctemporary-directory>\")\n```\n\n## Guidelines\n\n- **Filter aggressively.** Most conversations will have low signal - automated tasks, trivial exchanges, already-captured knowledge. Only surface what's genuinely new and impactful.\n- **Prefer reduction.** If a finding is a special case of an existing brain principle, update the existing note rather than creating a new one.\n- **Quote the user.** When a finding stems from a direct user correction, include the user's words and source file path - they carry the most signal about what matters.\n- **Shut down agents** when analysis is complete. Don't leave them idle.\n",{"name":5,"description":7,"compatibility":43,"disable-model-invocation":44},"Requires python3",true,"main","d9967dd74cef0efe0bc968ac4f387b38348122a2",".agents\u002Fskills\u002Fruminate","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills","https:\u002F\u002Fgithub.com\u002Fjapurcell","2026-11-11 08:59:34",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":44},[53,56,59],{"tool":54,"command":55},"npx","npx skills add japurcell\u002Fskills --skill ruminate",{"tool":57,"command":58},"bunx","bunx skills add japurcell\u002Fskills --skill ruminate",{"tool":60,"command":61},"pnpm","pnpm dlx skills add japurcell\u002Fskills --skill ruminate","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fruminate\u002FSKILL.md",[64],{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"scripts\u002Fextract-conversations.py","SCRIPT","脚本",23129,"https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fruminate\u002Fscripts\u002Fextract-conversations.py",[71,100,122,150,173,194],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":82,"publishedAt":38},"01M6PT0ZTYCN4DHK3SXWS79WSY","spec-create","Create or revise a portable behavioral SPEC for a feature or whole product. Use when specifying a new idea, documenting an existing system for reimplementation, or defining a change while preserving compatibility. Implementation planning is a separate task.","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdefault\u002Fspec-create\u002FSKILL.md",[77],{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[83,84,85,86,87,21,88,89,20,90,91,92,93,94,95,96,97,98,99],"create","spec","spec create","feature","planning","system","use","task","output","change","product","implementation","revise","compatibility","behavioral","idea","path",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":107,"publishedAt":38},"01M6GSVHQ4MY4R4SMS8RY6324Q","prd-ralph","Complete at most one eligible unfinished PRD task, verify it, log progress, and optionally create one scoped commit.","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdefault\u002Fprd-ralph\u002FSKILL.md",[106],{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},[108,109,110,83,111,112,113,114,115,116,90,117,118,119,120,121,99],"prd ralph","ralph","prd","progress","verify","at","complete","commit","it","log","scoped","false","file","id",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":131,"publishedAt":38},"01M6GSYMGHE2FEGEFKD926EJH8","fsdp-style-guide","Mandatory for all UI\u002FUX design, implementation, review, audit, or visual regression tasks. Always use for any work that affects user-facing appearance, behavior, accessibility, branding, layout, navigation, forms, dialogs, alerts, tables, or visual design.","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fforest-service\u002Ffsdp-style-guide\u002FSKILL.md",[128],{"categoryNo":129,"categoryName":130,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000032","设计师",[132,133,134,135,136,137,138,139,89,140,141,29,142,143,144,145,146,147,148,149,21],"fsdp style guide","style guide","FSDP","style","guide","visual regression","regression","design","visual","tasks","visual design","alerts","work","branding","layout","audit","ux","behavior",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":38},"01M6GSYHEQARNM7WN0RJA7K7T2","agency-priorities","Provide self-contained USDA strategic goals and Forest Service priorities for agency briefings, proposals, and work-plan alignment. Use when explaining agency direction or evaluating how a project contributes to USDA and Forest Service priorities.","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fforest-service\u002Fagency-priorities\u002FSKILL.md",[],[157,158,159,160,161,144,162,163,164,165,166,167,168,169,89,21,170,171,172],"agency priorities","priorities","agency","self","alignment","plan","goals","proposals","direction","how","project","strategic","service","when","briefings","self-contained",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":182,"publishedAt":38},"01M6GSY9VVN8RSJ5QYM540ZJ1D","accomplishment-report","Create a concise Forest Service executive accomplishment report when all work in an ExecPlan is completed, or update its existing report. Use at ExecPlan closeout and when asked to report completed work, agency alignment, and estimated AI-assisted versus human-only time and cost savings.","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fforest-service\u002Faccomplishment-report\u002FSKILL.md",[179],{"categoryNo":180,"categoryName":181,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000034","行政与办公人员",[183,184,161,185,83,144,186,187,188,189,113,159,190,169,89,191,192,193,20,170],"report","accomplishment report","cost","concise","human","time","update","only","closeout","savings","execplan",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":208,"publishedAt":38},"01M6GSY7J8ZMCE319P8Y6JZ432","writing-for-agents","Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.","https:\u002F\u002Fgithub.com\u002Fjapurcell\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdefault\u002Fwriting-for-agents\u002FSKILL.md",[200,205],{"categoryNo":201,"categoryName":202,"taxonomyType":203,"taxonomyTypeDesc":204},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":206,"categoryName":207,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000036","内容创作者",[209,210,21,89,211,212,170],"writing","writing for agents","documents","editing"]