[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QMGS3MPMRQW6G0DR136F02":3,"skill-same:01M6QMGS3MPMRQW6G0DR136F02":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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6QMGS3MPMRQW6G0DR136F02","process-mapper",null,"Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean \u002F Six Sigma \u002F Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fprocess-mapper\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"mapper","process mapper","process","lean","value","for","process map","time","tactical","reliability","value-stream","six-sigma","gemini","cycle-time","bottleneck","bpmn","list","system","procurement","severity","2026-11-10 10:12:30",false,"---\nname: process-mapper\ndescription: Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean \u002F Six Sigma \u002F Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.\nversion: 2.8.0\nauthor: claude-code-skills\nlicense: MIT\ntags: [bizops, process, bpmn, bottleneck, cycle-time, lean, six-sigma, value-stream]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# process-mapper\n\nBPMN-style business process documentation, bottleneck detection, and cycle-time analysis for internal-operations leaders.\n\n## Purpose\n\nInternal-operations work suffers from three recurring failure modes:\n\n1. **Implicit process** — the steps exist only in tribal knowledge, so handoffs drop and onboarding takes weeks.\n2. **Invisible waiting** — most of the elapsed time on any business process is queue \u002F wait \u002F approval time, not actual work; teams optimize the wrong stage.\n3. **Local optimization** — Goldratt's Theory of Constraints is ignored; resources are added to non-constraint stages, gaining nothing.\n\nThis skill produces a documented process map, identifies where work waits, and points the constraint out by name with deterministic logic — not LLM intuition.\n\n## When to use\n\n- Documenting a new business process (procurement intake, vendor onboarding, employee onboarding, incident handoff, expense reimbursement, customer onboarding, claims adjudication).\n- An existing process is \"too slow\" but nobody can name the bottleneck.\n- Cycle time is being measured but value-add ratio is not — so the team can't tell whether the process is healthy or waste-heavy.\n- Cross-functional handoffs are dropping work and root cause is unclear.\n\n## Workflow\n\nFive-step deterministic flow:\n\n1. **Intake.** Capture the process as a JSON file with one entry per stage: `name`, `owner`, `type` (`value-add` | `wait` | `rework`), `duration_minutes_p50`, `duration_minutes_p90`. Use `assets\u002Fprocess_template.md` and its JSON skeleton.\n2. **Map stages.** Run `process_documenter.py` to produce an ASCII swim-lane diagram + a normalized JSON artifact. The swim-lane separates lanes by owner so cross-functional handoffs become visible.\n3. **Measure cycle time.** Run `cycle_time_analyzer.py` to compute total P50, total P90, value-add ratio (VA%), and a Little's-Law throughput estimate. Verdict: VA% > 25% = HEALTHY, 10–25% = TYPICAL, \u003C 10% = WASTE-HEAVY.\n4. **Detect bottlenecks.** Run `bottleneck_detector.py` with the appropriate `--profile` (saas \u002F services \u002F manufacturing \u002F healthcare). Output is a ranked list with severity (CRITICAL \u002F HIGH \u002F MEDIUM), root-cause hypothesis, and one recommended action per finding.\n5. **Recommend.** Pair the bottleneck list with the cycle-time verdict; recommend a single constraint-focused intervention per Goldratt's \"subordinate everything to the constraint\" rule. Don't recommend optimization of a non-constraint stage.\n\n## Scripts\n\n**`scripts\u002Fprocess_documenter.py`** — Reads a process JSON, validates it, and emits a text-based BPMN-style swim-lane diagram in Markdown (lanes by owner, stages annotated with type + duration). Also outputs a normalized JSON artifact for downstream tools. Stdlib only. `--sample` prints a 6-stage procurement-intake example.\n\n**`scripts\u002Fbottleneck_detector.py`** — Applies three deterministic detection rules: (a) stage P50 > 2× mean of value-add stages, (b) wait-state % > 40% of total cycle, (c) rework % > 15%. Thresholds adjust by `--profile` because SaaS, services, manufacturing, and healthcare have different \"normal\" wait ratios. Output is a ranked list with severity, hypothesis, action.\n\n**`scripts\u002Fcycle_time_analyzer.py`** — Computes total P50 and P90 cycle time, value-add ratio (VA%), wait %, rework %, and a Little's-Law throughput estimate (WIP \u002F cycle time). Per Lean canon: VA% > 25% = HEALTHY, 10–25% = TYPICAL (most non-manufacturing processes land here), \u003C 10% = WASTE-HEAVY.\n\n## Quick example\n\n```bash\n# Renders a BPMN-style swim-lane diagram + normalized JSON for the built-in 6-stage procurement-intake example\ncd business-operations\u002Fskills\u002Fprocess-mapper && python3 scripts\u002Fprocess_documenter.py --sample\n```\n\n## References\n\n- `references\u002Flean_six_sigma_canon.md` — TIMWOOD wastes, value-stream mapping, Theory of Constraints, Kanban WIP, Little's Law. Cites Womack & Jones, Rother & Shook, Goldratt, Ohno, Liker, Pyzdek, Anderson.\n- `references\u002Fbpmn_essentials.md` — Pools, lanes, gateways, events, message flows, common notation mistakes. Cites the OMG BPMN 2.0 spec, Silver, Allweyer, Freund\u002FRücker, OASIS, ISO\u002FIEC 19510:2013.\n- `references\u002Fbottleneck_anti_patterns.md` — Seven specific anti-patterns drawn from Goldratt, Kim et al., Spear, DORA, Deming, and process-mining research.\n\n## Assumptions\n\n1. The user can provide stage-level cycle-time data (even rough P50 \u002F P90 estimates). If they cannot, the first step is to instrument the process — not to map it.\n2. \"Process\" here means a repeatable business workflow with discrete stages, not a one-off project.\n3. The user has authority to act on bottlenecks (or can route findings to someone who does). Without that, the output is academic.\n4. Stage `type` is honest: a \"value-add\" stage labeled as such by the user really does change the work product from the customer's perspective. Mis-labelling waiting as value-add is the most common data-quality failure.\n\n## Anti-patterns\n\n- **Mapping every process at once.** Pick one. Goldratt: the constraint is a single point.\n- **Optimizing the non-constraint.** If stage 4 is the bottleneck, speeding up stage 2 just builds inventory in front of stage 4. Subordinate everything to the constraint.\n- **Mistaking total cycle time for processing time.** They are almost never the same; VA% reveals the gap.\n- **Adding people to a wait-bound process.** Wait time is not solved by more headcount; it's solved by removing the handoff or batch.\n- **Treating rework as a separate problem.** Rework loops belong in the process map. Hiding them understates true cycle time.\n\n## Distinct from\n\n- **business-growth skills** — external sales motion, lead-funnel conversion, customer-success retention. Process-mapper is *internal* operations.\n- **engineering\u002Fslo-architect** — system-reliability SLOs \u002F error budgets \u002F burn-rate alerts. Process-mapper is *business-process* cycle time, not system uptime.\n- **c-level-advisor (COO \u002F CEO)** — strategic prioritization of which processes to fix. Process-mapper is the tactical instrument used after that prioritization decision.\n- **project-management skills** — Jira \u002F Confluence ticket workflow tooling. Process-mapper is process *design*, not ticket *tracking*.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nBefore invoking the tools, the orchestrator (or `\u002Fcs:grill-bizops`) walks the user through these questions **one at a time, with a recommended answer + canon citation**. Never bundled.\n\n1. **\"Do you have measured cycle times for the top-3 longest stages, or only estimates?\"**\n   Recommended: insist on measured data.\n   Canon: Goldratt 1984 (*The Goal*) — optimizing estimated bottlenecks reliably attacks the wrong constraint.\n\n2. **\"Are you mapping the *current* process (as-is) or the *intended* process (to-be)?\"**\n   Recommended: map as-is first. To-be after bottleneck is identified.\n   Canon: Rother & Shook 1999 (*Learning to See*) — value-stream mapping starts with the current state, always.\n\n3. **\"Where do handoffs occur between teams, and how long does each handoff wait?\"**\n   Recommended: log every handoff with median wait time.\n   Canon: Reinertsen 2009 (*Principles of Product Development Flow*) — wait time at handoffs is the largest invisible cost.\n\n4. **\"What's your batch size at each stage?\"**\n   Recommended: drive batch size toward 1 wherever possible.\n   Canon: Anderson 2010 (*Kanban*) — batch size correlates 1:1 with cycle time variance.\n\n5. **\"What's the rework rate per stage?\"**\n   Recommended: surface it explicitly; rework loops belong in the map.\n   Canon: Pyzdek (*Six Sigma Handbook*) — hidden rework drives 30-50% of total cycle time in service processes.\n\nWalk depth-first. Don't open question 4 before 1-3 are answered. After all 5 are locked, invoke `process_documenter.py` → `bottleneck_detector.py` → `cycle_time_analyzer.py` in sequence.\n","# process-mapper\n\nBPMN-style business process documentation, bottleneck detection, and cycle-time analysis for internal-operations leaders.\n\n## Purpose\n\nInternal-operations work suffers from three recurring failure modes:\n\n1. **Implicit process** — the steps exist only in tribal knowledge, so handoffs drop and onboarding takes weeks.\n2. **Invisible waiting** — most of the elapsed time on any business process is queue \u002F wait \u002F approval time, not actual work; teams optimize the wrong stage.\n3. **Local optimization** — Goldratt's Theory of Constraints is ignored; resources are added to non-constraint stages, gaining nothing.\n\nThis skill produces a documented process map, identifies where work waits, and points the constraint out by name with deterministic logic — not LLM intuition.\n\n## When to use\n\n- Documenting a new business process (procurement intake, vendor onboarding, employee onboarding, incident handoff, expense reimbursement, customer onboarding, claims adjudication).\n- An existing process is \"too slow\" but nobody can name the bottleneck.\n- Cycle time is being measured but value-add ratio is not — so the team can't tell whether the process is healthy or waste-heavy.\n- Cross-functional handoffs are dropping work and root cause is unclear.\n\n## Workflow\n\nFive-step deterministic flow:\n\n1. **Intake.** Capture the process as a JSON file with one entry per stage: `name`, `owner`, `type` (`value-add` | `wait` | `rework`), `duration_minutes_p50`, `duration_minutes_p90`. Use `assets\u002Fprocess_template.md` and its JSON skeleton.\n2. **Map stages.** Run `process_documenter.py` to produce an ASCII swim-lane diagram + a normalized JSON artifact. The swim-lane separates lanes by owner so cross-functional handoffs become visible.\n3. **Measure cycle time.** Run `cycle_time_analyzer.py` to compute total P50, total P90, value-add ratio (VA%), and a Little's-Law throughput estimate. Verdict: VA% > 25% = HEALTHY, 10–25% = TYPICAL, \u003C 10% = WASTE-HEAVY.\n4. **Detect bottlenecks.** Run `bottleneck_detector.py` with the appropriate `--profile` (saas \u002F services \u002F manufacturing \u002F healthcare). Output is a ranked list with severity (CRITICAL \u002F HIGH \u002F MEDIUM), root-cause hypothesis, and one recommended action per finding.\n5. **Recommend.** Pair the bottleneck list with the cycle-time verdict; recommend a single constraint-focused intervention per Goldratt's \"subordinate everything to the constraint\" rule. Don't recommend optimization of a non-constraint stage.\n\n## Scripts\n\n**`scripts\u002Fprocess_documenter.py`** — Reads a process JSON, validates it, and emits a text-based BPMN-style swim-lane diagram in Markdown (lanes by owner, stages annotated with type + duration). Also outputs a normalized JSON artifact for downstream tools. Stdlib only. `--sample` prints a 6-stage procurement-intake example.\n\n**`scripts\u002Fbottleneck_detector.py`** — Applies three deterministic detection rules: (a) stage P50 > 2× mean of value-add stages, (b) wait-state % > 40% of total cycle, (c) rework % > 15%. Thresholds adjust by `--profile` because SaaS, services, manufacturing, and healthcare have different \"normal\" wait ratios. Output is a ranked list with severity, hypothesis, action.\n\n**`scripts\u002Fcycle_time_analyzer.py`** — Computes total P50 and P90 cycle time, value-add ratio (VA%), wait %, rework %, and a Little's-Law throughput estimate (WIP \u002F cycle time). Per Lean canon: VA% > 25% = HEALTHY, 10–25% = TYPICAL (most non-manufacturing processes land here), \u003C 10% = WASTE-HEAVY.\n\n## Quick example\n\n```bash\n# Renders a BPMN-style swim-lane diagram + normalized JSON for the built-in 6-stage procurement-intake example\ncd business-operations\u002Fskills\u002Fprocess-mapper && python3 scripts\u002Fprocess_documenter.py --sample\n```\n\n## References\n\n- `references\u002Flean_six_sigma_canon.md` — TIMWOOD wastes, value-stream mapping, Theory of Constraints, Kanban WIP, Little's Law. Cites Womack & Jones, Rother & Shook, Goldratt, Ohno, Liker, Pyzdek, Anderson.\n- `references\u002Fbpmn_essentials.md` — Pools, lanes, gateways, events, message flows, common notation mistakes. Cites the OMG BPMN 2.0 spec, Silver, Allweyer, Freund\u002FRücker, OASIS, ISO\u002FIEC 19510:2013.\n- `references\u002Fbottleneck_anti_patterns.md` — Seven specific anti-patterns drawn from Goldratt, Kim et al., Spear, DORA, Deming, and process-mining research.\n\n## Assumptions\n\n1. The user can provide stage-level cycle-time data (even rough P50 \u002F P90 estimates). If they cannot, the first step is to instrument the process — not to map it.\n2. \"Process\" here means a repeatable business workflow with discrete stages, not a one-off project.\n3. The user has authority to act on bottlenecks (or can route findings to someone who does). Without that, the output is academic.\n4. Stage `type` is honest: a \"value-add\" stage labeled as such by the user really does change the work product from the customer's perspective. Mis-labelling waiting as value-add is the most common data-quality failure.\n\n## Anti-patterns\n\n- **Mapping every process at once.** Pick one. Goldratt: the constraint is a single point.\n- **Optimizing the non-constraint.** If stage 4 is the bottleneck, speeding up stage 2 just builds inventory in front of stage 4. Subordinate everything to the constraint.\n- **Mistaking total cycle time for processing time.** They are almost never the same; VA% reveals the gap.\n- **Adding people to a wait-bound process.** Wait time is not solved by more headcount; it's solved by removing the handoff or batch.\n- **Treating rework as a separate problem.** Rework loops belong in the process map. Hiding them understates true cycle time.\n\n## Distinct from\n\n- **business-growth skills** — external sales motion, lead-funnel conversion, customer-success retention. Process-mapper is *internal* operations.\n- **engineering\u002Fslo-architect** — system-reliability SLOs \u002F error budgets \u002F burn-rate alerts. Process-mapper is *business-process* cycle time, not system uptime.\n- **c-level-advisor (COO \u002F CEO)** — strategic prioritization of which processes to fix. Process-mapper is the tactical instrument used after that prioritization decision.\n- **project-management skills** — Jira \u002F Confluence ticket workflow tooling. Process-mapper is process *design*, not ticket *tracking*.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nBefore invoking the tools, the orchestrator (or `\u002Fcs:grill-bizops`) walks the user through these questions **one at a time, with a recommended answer + canon citation**. Never bundled.\n\n1. **\"Do you have measured cycle times for the top-3 longest stages, or only estimates?\"**\n   Recommended: insist on measured data.\n   Canon: Goldratt 1984 (*The Goal*) — optimizing estimated bottlenecks reliably attacks the wrong constraint.\n\n2. **\"Are you mapping the *current* process (as-is) or the *intended* process (to-be)?\"**\n   Recommended: map as-is first. To-be after bottleneck is identified.\n   Canon: Rother & Shook 1999 (*Learning to See*) — value-stream mapping starts with the current state, always.\n\n3. **\"Where do handoffs occur between teams, and how long does each handoff wait?\"**\n   Recommended: log every handoff with median wait time.\n   Canon: Reinertsen 2009 (*Principles of Product Development Flow*) — wait time at handoffs is the largest invisible cost.\n\n4. **\"What's your batch size at each stage?\"**\n   Recommended: drive batch size toward 1 wherever possible.\n   Canon: Anderson 2010 (*Kanban*) — batch size correlates 1:1 with cycle time variance.\n\n5. **\"What's the rework rate per stage?\"**\n   Recommended: surface it explicitly; rework loops belong in the map.\n   Canon: Pyzdek (*Six Sigma Handbook*) — hidden rework drives 30-50% of total cycle time in service processes.\n\nWalk depth-first. Don't open question 4 before 1-3 are answered. After all 5 are locked, invoke `process_documenter.py` → `bottleneck_detector.py` → `cycle_time_analyzer.py` in sequence.\n",{"name":5,"tags":44,"author":46,"license":15,"version":47,"description":7,"compatible_tools":48},[45,21,34,33,32,22,30,29],"bizops","claude-code-skills","2.8.0",[49,50,51,52,53,54],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fprocess-mapper","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill process-mapper",{"tool":70,"command":71},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill process-mapper",{"tool":73,"command":74},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill process-mapper","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fprocess-mapper\u002FSKILL.md",true,[],[79,109,134,161,185,209],{"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":90,"publishedAt":39},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[85],{"categoryNo":86,"categoryName":87,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[91,92,93,94,95,96,97,98,99,35,100,101,102,103,104,105,24,106,107,108],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":39},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[116,117,118,119,95,120,121,122,123,124,125,126,127,128,129,130,131,132,133,24],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","use","platform","grow","posting","competitive",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":143,"publishedAt":39},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[140],{"categoryNo":141,"categoryName":142,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000023","Skill与智能体开发",[144,127,145,146,147,148,149,150,129,151,152,55,153,154,155,156,157,158,159,160],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":167,"publishedAt":39},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[168,169,170,31,171,172,173,174,175,129,176,101,177,178,179,180,181,182,183,184],"wrap up","up","wrap","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"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":192,"publishedAt":39},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[191],{"categoryNo":141,"categoryName":142,"taxonomyType":88,"taxonomyTypeDesc":89},[193,194,195,196,197,146,172,198,199,200,201,202,127,203,100,204,205,206,207,208],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":213,"categories":214,"keywords":215,"publishedAt":39},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[216,217,218,219,220,221,222,223,224,201,225,226,227,228,229,230,102,231,232,184],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]