[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DBRDBV4TQRC3JYWXX4BREE":3,"skill-same:01M6DBRDBV4TQRC3JYWXX4BREE":78},{"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":23,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":14,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":43,"install":44,"resources":57},"01M6DBRDBV4TQRC3JYWXX4BREE","recall-workflow-agent",null,"Use when you need the exact input PROMPT or final OUTPUT of a specific subagent from a past Workflow \u002F ultracode fan-out run - reviewing or recovering what one fanned-out agent (a lens, a verifier, the synthesizer) actually received or returned after the run scrolled off screen or the session compacted. Identify it by a distinctive phrase, the workflow name, or by listing the run's agents.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Frecall-workflow-agent\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32],"workflow","recall","recall workflow agent","run","subagent","prompt","session","use","verifier","2026-11-10 10:12:30",false,"---\nname: recall-workflow-agent\ndescription: Use when you need the exact input PROMPT or final OUTPUT of a specific subagent from a past Workflow \u002F ultracode fan-out run - reviewing or recovering what one fanned-out agent (a lens, a verifier, the synthesizer) actually received or returned after the run scrolled off screen or the session compacted. Identify it by a distinctive phrase, the workflow name, or by listing the run's agents.\n---\n\n# recall-workflow-agent\n\n## Overview\nEvery Workflow subagent's full transcript is written to disk as JSONL. To get one agent's prompt + output you locate its `agent-\u003Cid>.jsonl` and read it — you do NOT re-run the workflow or ask the user. **The reliable locator is a distinctive PHRASE, because the readable agent label is not stored on disk.**\n\n## Where it lives\n```\n~\u002F.claude\u002Fprojects\u002F\u003Cproject>\u002F\u003Csession>\u002Fsubagents\u002Fworkflows\u002F\u003Cwf_id>\u002F\n    journal.jsonl          # per agent: {type, key:\u003CHASH>, agentId, result:\u003Cthe OUTPUT>}\n    agent-\u003CagentId>.jsonl  # per agent: full transcript (prompt = its user msg; + turns)\n~\u002F.claude\u002Fprojects\u002F\u003COTHER-project>\u002F\u003Csession>\u002Fworkflows\u002Fscripts\u002F\u003Cname>-\u003Cwf_id>.js   # the script (holds the labels)\n```\n**Gotcha:** the transcript dir is under the SESSION's project, which is frequently a *different* `\u003Cproject>` than where the `.js` script lives. Never assume co-location — a phrase grep finds it wherever it is.\n\n## Do this — the bundled tool locates + extracts + saves the files\n```bash\nS=~\u002F.agents\u002Fskills\u002Frecall-workflow-agent\u002Fscripts\u002Fextract_workflow_agent.py\n# best: a phrase you SAW in the output, or a phrase from the prompt\u002Flens task:\npython \"$S\" --phrase \"Vaporware-filtered maturity table\" --slug decision --print\n# don't know a phrase? list every agent in the run (previewed by its report HEADLINE), then pick:\npython \"$S\" --workflow multiai-orchestration-research --list\n# scope a phrase to one run if it's ambiguous:\npython \"$S\" --workflow \u003Cname> --phrase \"\u003Cdistinctive>\"\n```\nPrompt is saved to `\u003Cslug>-PROMPT.md`, output to `\u003Cslug>-OUTPUT.md`. The tool takes the output from the journal `result` (the authoritative captured return), so it's clean even for tool-using agents.\n\n## Identify by (most→least reliable)\n1. A phrase from the **OUTPUT you saw** in the UI — unique, best.\n2. **`--list`** the workflow's agents (each previewed by its report headline) → pick.\n3. A phrase from the **PROMPT \u002F lens task** — works, but read the synthesizer gotcha below.\n\n## Common mistakes\n- **Grepping the agent label** (`synthesize:multiai-decision`): it is NOT in the transcript or journal — journal stores a hashed `key`; labels live only in the `.js` script. Grep a content phrase instead.\n- **Searching only under the script's project dir** → 0 files. Grep across ALL of `~\u002F.claude\u002Fprojects` (the tool does).\n- **Fanning out the Grep TOOL across `~\u002F.claude\u002Fprojects`:** on Windows it can die with `ENAMETOOLONG` (giant argv over thousands of files). Use the bundled tool (Python glob + line scan) or `rg` via Bash — not the Grep tool.\n- **A lens phrase matching two agents:** a SYNTHESIZER's prompt embeds every upstream agent's report verbatim, so a lens name\u002Ftag hits the lens agent AND the synthesizer. Use a phrase from that lens's OWN narration\u002Foutput, or `--list` and pick by preview. The tool reports every match so you can narrow.\n- **Decoy — your OWN current session's subagents:** Agent-tool subagents live at `subagents\u002Fagent-*.jsonl` (NO `workflows\u002F\u003Cwf_id>\u002F` layer). If your task text quotes the workflow name\u002Fphrase they self-match. The tool's glob requires `...\u002Fsubagents\u002Fworkflows\u002F\u003Cwf_id>\u002Fagent-*.jsonl`, so it excludes them — a manual grep won't.\n- **Treating joined assistant turns as \"the output\":** tool-using agents interleave narration between tool batches. The real output is journal `result` (final return) — what the tool uses.\n- **content is a string OR a list of `{type:text}` blocks** — handle both (the tool does).\n```\n","# recall-workflow-agent\n\n## Overview\nEvery Workflow subagent's full transcript is written to disk as JSONL. To get one agent's prompt + output you locate its `agent-\u003Cid>.jsonl` and read it — you do NOT re-run the workflow or ask the user. **The reliable locator is a distinctive PHRASE, because the readable agent label is not stored on disk.**\n\n## Where it lives\n```\n~\u002F.claude\u002Fprojects\u002F\u003Cproject>\u002F\u003Csession>\u002Fsubagents\u002Fworkflows\u002F\u003Cwf_id>\u002F\n    journal.jsonl          # per agent: {type, key:\u003CHASH>, agentId, result:\u003Cthe OUTPUT>}\n    agent-\u003CagentId>.jsonl  # per agent: full transcript (prompt = its user msg; + turns)\n~\u002F.claude\u002Fprojects\u002F\u003COTHER-project>\u002F\u003Csession>\u002Fworkflows\u002Fscripts\u002F\u003Cname>-\u003Cwf_id>.js   # the script (holds the labels)\n```\n**Gotcha:** the transcript dir is under the SESSION's project, which is frequently a *different* `\u003Cproject>` than where the `.js` script lives. Never assume co-location — a phrase grep finds it wherever it is.\n\n## Do this — the bundled tool locates + extracts + saves the files\n```bash\nS=~\u002F.agents\u002Fskills\u002Frecall-workflow-agent\u002Fscripts\u002Fextract_workflow_agent.py\n# best: a phrase you SAW in the output, or a phrase from the prompt\u002Flens task:\npython \"$S\" --phrase \"Vaporware-filtered maturity table\" --slug decision --print\n# don't know a phrase? list every agent in the run (previewed by its report HEADLINE), then pick:\npython \"$S\" --workflow multiai-orchestration-research --list\n# scope a phrase to one run if it's ambiguous:\npython \"$S\" --workflow \u003Cname> --phrase \"\u003Cdistinctive>\"\n```\nPrompt is saved to `\u003Cslug>-PROMPT.md`, output to `\u003Cslug>-OUTPUT.md`. The tool takes the output from the journal `result` (the authoritative captured return), so it's clean even for tool-using agents.\n\n## Identify by (most→least reliable)\n1. A phrase from the **OUTPUT you saw** in the UI — unique, best.\n2. **`--list`** the workflow's agents (each previewed by its report headline) → pick.\n3. A phrase from the **PROMPT \u002F lens task** — works, but read the synthesizer gotcha below.\n\n## Common mistakes\n- **Grepping the agent label** (`synthesize:multiai-decision`): it is NOT in the transcript or journal — journal stores a hashed `key`; labels live only in the `.js` script. Grep a content phrase instead.\n- **Searching only under the script's project dir** → 0 files. Grep across ALL of `~\u002F.claude\u002Fprojects` (the tool does).\n- **Fanning out the Grep TOOL across `~\u002F.claude\u002Fprojects`:** on Windows it can die with `ENAMETOOLONG` (giant argv over thousands of files). Use the bundled tool (Python glob + line scan) or `rg` via Bash — not the Grep tool.\n- **A lens phrase matching two agents:** a SYNTHESIZER's prompt embeds every upstream agent's report verbatim, so a lens name\u002Ftag hits the lens agent AND the synthesizer. Use a phrase from that lens's OWN narration\u002Foutput, or `--list` and pick by preview. The tool reports every match so you can narrow.\n- **Decoy — your OWN current session's subagents:** Agent-tool subagents live at `subagents\u002Fagent-*.jsonl` (NO `workflows\u002F\u003Cwf_id>\u002F` layer). If your task text quotes the workflow name\u002Fphrase they self-match. The tool's glob requires `...\u002Fsubagents\u002Fworkflows\u002F\u003Cwf_id>\u002Fagent-*.jsonl`, so it excludes them — a manual grep won't.\n- **Treating joined assistant turns as \"the output\":** tool-using agents interleave narration between tool batches. The real output is journal `result` (final return) — what the tool uses.\n- **content is a string OR a list of `{type:text}` blocks** — handle both (the tool does).\n```\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Fskills\u002Frecall-workflow-agent","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill recall-workflow-agent",{"tool":50,"command":51},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill recall-workflow-agent",{"tool":53,"command":54},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill recall-workflow-agent","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Frecall-workflow-agent\u002FSKILL.md",true,[58,64,68,72],{"path":59,"resourceType":60,"resourceTypeDesc":61,"sizeBytes":62,"sourceHtmlUrl":63},"scripts\u002Fcapture.js","SCRIPT","脚本",7057,"https:\u002F\u002Fgithub.com\u002Flove-dabimas\u002Fdabimas-factor\u002Fblob\u002Fmain\u002Fskills\u002Fpersonal\u002Fdabifaku-manual\u002Fscripts\u002Fcapture.js",{"path":65,"resourceType":60,"resourceTypeDesc":61,"sizeBytes":66,"sourceHtmlUrl":67},"scripts\u002Fcapture2.js",6168,"https:\u002F\u002Fgithub.com\u002Flove-dabimas\u002Fdabimas-factor\u002Fblob\u002Fmain\u002Fskills\u002Fpersonal\u002Fdabifaku-manual\u002Fscripts\u002Fcapture2.js",{"path":69,"resourceType":60,"resourceTypeDesc":61,"sizeBytes":70,"sourceHtmlUrl":71},"scripts\u002Fextract_workflow_agent.py",8190,"https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Frecall-workflow-agent\u002Fscripts\u002Fextract_workflow_agent.py",{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"style-guide.md","REFERENCE","参考文档",3749,"https:\u002F\u002Fgithub.com\u002Flove-dabimas\u002Fdabimas-factor\u002Fblob\u002Fmain\u002Fskills\u002Fpersonal\u002Fdabifaku-manual\u002Fstyle-guide.md",[79,100,126,153,167,183],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":33},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[86,87,88,89,90,91,92,93,94,31,95,96,97,98,99],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"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":33},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[106],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[108,109,110,111,112,113,114,115,116,117,118,119,24,120,31,121,122,123,124,125],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","windows","free","molecular dynamics","local","do","MCP",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":138,"publishedAt":33},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[132,135],{"categoryNo":133,"categoryName":134,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[139,140,141,142,143,144,145,146,147,148,109,149,150,151,152],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"publishedAt":33},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[155,160,161,162,163,164,165,166],"setup","report","project","installation","project setup","issues","state",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":174,"publishedAt":33},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[173],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[169,175,91,176,177,178,31,179,180,181,182],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":184,"name":160,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":188,"publishedAt":33},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[160,162,123,189,164,190],"files","interactive"]