[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M698PW0KGGD3TEA98XY2Y7YC":3,"skill-same:01M698PW0KGGD3TEA98XY2Y7YC":267},{"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":17,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":42,"repositoryHtmlUrl":43,"repositoryHomepageUrl":44,"ownerHtmlUrl":45,"ownerBlogUrl":44,"ownerDescription":46,"updatedAt":47,"install":48,"resources":61},"01M698PW0KGGD3TEA98XY2Y7YC","claw-score",null,"Audit or refresh OpenClaw maturity scorecard docs from root taxonomy, maturity scores, and QA evidence artifacts without using maintainer discrawl data or committed inventory reports.","openclaw\u002Fopenclaw","openclaw","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252820863?v=4","01M698P3FSAZ6B4R928ZF19KPT","01M698PDMDRVH56MV30D253NFP",394352,"MIT","https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fopenclaw\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fclaw-score\u002FSKILL.md",[],[18,19,20,9,21,22,23,24,25,26,27,28,29,30,31,32,33],"claw score","claw","maturity","scorecard","root","reports","evidence","refresh","taxonomy","inventory","data","audit","qa","artifacts","docs","discrawl","2026-11-10 10:12:30",false,"---\nname: claw-score\ndescription: Audit or refresh OpenClaw maturity scorecard docs from root taxonomy, maturity scores, and QA evidence artifacts without using maintainer discrawl data or committed inventory reports.\n---\n\n# claw-score\n\nUse this skill when working on the OpenClaw maturity scorecard in this repo.\nThis is the openclaw-local version of the maintainer `claw-score` workflow:\nit keeps the taxonomy and scorecard concepts, but excludes discrawl and the old\ncommitted `inventory\u002F` report tree.\n\n## Authority\n\nThis skill owns the operational workflow for:\n\n- `taxonomy.yaml`\n- `qa\u002Fmaturity-scores.yaml`\n- `docs\u002Fconcepts\u002Fqa-e2e-automation.md`\n- `qa\u002Fscenarios\u002Findex.yaml`\n\nKeep person-specific, maintainer-private, Discord archive, and discrawl facts\nout of this repo. If a score needs private evidence, use the redacted\n`qa-evidence.json` artifact shape generated by OpenClaw QA workflows.\n\n## Source Model\n\n- `taxonomy.yaml` is the hand-edited source of truth for surfaces, levels,\n  QA profiles, categories, feature coverage IDs, docs refs, LTS overrides, and\n  completeness-instruction paths.\n- Each feature has exactly one `coverageIds` entry. Keep that evidence ID\n  unique to the feature; broader many-to-many evidence mapping is not part of\n  the current taxonomy schema.\n- Coverage IDs use dotted `namespace.behavior` form, with lowercase\n  alphanumeric\u002Fdash segments. Profile, surface, and category IDs may remain\n  dashed or dotted.\n- Keep categories and feature names unique, product-shaped, and broader than raw\n  coverage IDs. Do not promote generic IDs into standalone feature names.\n- Avoid duplicate coverage-ID bundles under different feature names in one\n  category.\n- `qa\u002Fmaturity-scores.yaml` is the committed aggregate source for Quality,\n  Completeness, and LTS review state.\n- `extensions\u002Fqa-lab\u002Fsrc\u002Fscorecard-taxonomy.ts` exports\n  `readValidatedQaMaturityScoreSources`; use it to validate score output.\n- Generated public docs are `docs\u002Fmaturity\u002Fscorecard.md` and\n  `docs\u002Fmaturity\u002Ftaxonomy.md`; both come from `pnpm maturity:render`. Do not\n  hand-edit generated Markdown to change score results.\n- `qa-evidence.json` artifacts provide per-run QA scorecard evidence. Release\n  profile artifacts are the source of truth for Coverage. They can enrich\n  generated artifact docs, but they are not committed as inventory.\n\n## Commands\n\nRun from the openclaw repo root.\n\nValidate taxonomy YAML structure and the maturity score schema after source\nedits:\n\n```bash\nnode --import tsx --input-type=module \u003C\u003C'NODE'\nimport fs from \"node:fs\";\nimport YAML from \"yaml\";\nimport { readValidatedQaMaturityScoreSources } from \".\u002Fextensions\u002Fqa-lab\u002Fsrc\u002Fscorecard-taxonomy.ts\";\n\nfor (const file of [\"taxonomy.yaml\", \"qa\u002Fscenarios\u002Findex.yaml\"]) {\n  YAML.parse(fs.readFileSync(file, \"utf8\"));\n}\nreadValidatedQaMaturityScoreSources();\nNODE\n```\n\nCheck docs when touching docs prose:\n\n```bash\npnpm check:docs\n```\n\nRun focused QA\u002Fprofile checks when changing coverage IDs or profile membership:\n\n```bash\npnpm openclaw qa coverage --json\n```\n\n## Full Generation Runs\n\nFor a direct full scorecard run that publishes the generated-doc pull request,\nuse floating `main` resolution by default:\n\n```bash\ngh workflow run maturity-scorecard.yml \\\n  --repo openclaw\u002Fopenclaw \\\n  --ref main \\\n  -f ref=main \\\n  -f expected_sha='' \\\n  -f publish_pull_request=true \\\n  -f allow_failures=true\n```\n\nDo not resolve `main` locally and pass that commit as both `ref` and\n`expected_sha` for an ordinary manual generation run. OpenClaw's `main` moves\nquickly, so the caller-selected commit can become stale before validation. The\nworkflow then correctly rejects publication when the pull request base contains\nnewer maturity inputs, and QA never starts.\n\nWith `ref=main` and a blank `expected_sha`, the workflow's\n`floating_default_branch` path fetches and freezes the current remote default\nbranch inside validation before handing an immutable revision to downstream\njobs. Use an explicit SHA only when the requested evidence must remain bound to\nthat exact revision, such as a release-candidate workflow call or an\nartifact-only historical reproduction. If that exact-revision run also requests\npublication and `main` has changed relevant inputs, expect validation to fail and\ndispatch again from floating `main` instead.\n\n## Scoring Workflow\n\nWhen asked to score or refresh a surface:\n\n1. Read the surface in `taxonomy.yaml`.\n2. Read the surface completeness rubric under\n   `.agents\u002Fskills\u002Fclaw-score\u002Freferences\u002Fcompleteness\u002F`.\n3. Gather public repo evidence from docs, source, tests, and QA scenario\n   metadata.\n4. Prefer existing release profile `qa-evidence.json` artifacts for executed\n   proof.\n5. Update `qa\u002Fmaturity-scores.yaml` only for Quality, Completeness, and LTS\n   review state backed by public or redacted artifact evidence.\n6. Run the schema validation command from this skill.\n7. Run `pnpm check:docs` if docs prose changed, and focused QA coverage checks\n   if coverage IDs or profile membership changed.\n\nFor subjective score changes, make the smallest defensible edit and leave the\nevidence path in the PR or task summary. Keep manual prose in current docs and\nkeep score data in `qa\u002Fmaturity-scores.yaml`.\n\n## Default Completeness Process\n\nCompleteness is scored against the intended operator-visible workflow for each\ncategory, not against test breadth or implementation quality. The completeness\nreference files under `references\u002Fcompleteness\u002F` define the category scope and\nany surface-specific variation from this default process.\n\nBy default, Completeness measures how fully OpenClaw exposes the intended\nsurface capability set to the user, operator, author, or maintainer persona for\nthat surface. Score whether each category delivers the full expected workflow,\nincluding setup, normal use, status or inspection, recovery, and important\nplatform, provider, channel, security, or lifecycle variants where they apply.\n\nTreat `Surface-Specific Scoring Questions` and `Surface-Specific Guidance` as\nhigher-priority instructions for that surface. The surface instructions may\nflesh out, narrow, or intentionally conflict with the default ideas here; when\nthey do, follow the surface instructions and make the score rationale reflect\nthat surface-specific instruction. If a reference file does not include\nsurface-specific questions or guidance, apply this default process to the\nsurface's `Category Scope`.\n\nFor each category, ask:\n\n- Can the intended user or operator complete the category workflow end to end?\n- Are the taxonomy features present as supported capabilities rather than\n  isolated implementation fragments?\n- Are the important lifecycle stages represented: setup, normal operation,\n  status\u002Finspection, recovery, and upgrade or removal where relevant?\n- Are the important environment, provider, platform, channel, or security\n  branches present for this surface?\n- Do the known gaps leave major user-visible capability branches missing?\n\nDefault guidance:\n\n- Favor higher Completeness when the category supports the full\n  operator-visible workflow described by taxonomy and category evidence.\n- Lower Completeness when only the happy path exists, when important variants\n  are undocumented or unimplemented, or when recovery\u002Fstatus paths are missing.\n- Do not lower Completeness because tests are thin; that is Coverage.\n- Do not lower Completeness because implementation quality is fragile; that is\n  Quality.\n\nDefault Completeness bands:\n\n- `Clawesome` (95-100): complete across expected workflows, variants, and\n  recovery branches, with only minor polish gaps.\n- `Stable` (80-95): the expected workflow set is broadly present, with only\n  bounded missing branches.\n- `Beta` (70-80): the main workflow exists, but meaningful branches or recovery\n  paths are still absent.\n- `Alpha` (50-70): only a partial capability set is present; users can complete\n  some core tasks but not the full expected workflow.\n- `Experimental` (0-50): the category exposes only fragments of the intended\n  capability.\n\n## Decision Context\n\nRecord an optional `decision` beside `score` and `label` for surface and category\nQuality\u002FCompleteness, or beside `supported` for category LTS. In `taxonomy.yaml`,\nuse optional `level_decision` beside the canonical surface `level`.\n\nEach record contains `value`, `rationale`, `reviewer`, `evidence_refs`, and\n`revalidate_when`. Use an integer from 0–100 for Quality\u002FCompleteness, a boolean\nfor LTS, and a declared taxonomy level ID for `level_decision`. Supply nonempty\ntext fields and at least one evidence reference. Name the actual reviewer and\nthe condition that should trigger another review.\n\nLeave unavailable history absent: it is unknown, not an invitation to invent\nreviewers, rationale, or evidence. A record does not overwrite the current score,\nsupport flag, or canonical level. If its value differs, retain both; generated\ndocs show a non-gating mismatch, including under strict input validation.\n\nDo not attach decisions to Coverage, computed rollups, surface LTS summaries, or\nthe copied level in score aggregates. Decision context does not change coverage\nidentity, score calculations, support commitments, or release gates.\n\n## Score Semantics\n\n- Coverage: deterministic release validation coverage derived from the release\n  profile `qa-evidence.json.scorecard` feature fulfillment data.\n- Quality: reliability, maintainability, operator safety, and regression\n  confidence for the category.\n- Completeness: how much of the intended operator-visible workflow exists for\n  the category. Use the default completeness process plus any surface-specific\n  variation before changing this score.\n- LTS: derived from Quality, release-evidence Coverage, and\n  `human_lts_override`; do not hand-edit generated Markdown to change LTS\n  status.\n\nBands:\n\n- `Clawesome`: 95-100\n- `Stable`: 80-95\n- `Beta`: 70-80\n- `Alpha`: 50-70\n- `Experimental`: 0-50\n\n## Artifacts\n\nDo not add the maintainer repo's `docs\u002Fkevinslin\u002Fmaturity-scorecard\u002Finventory\u002F`\ntree to openclaw. Evidence-enriched scorecard outputs belong in short-lived\nartifacts, not committed generated docs, unless this repo adds an explicit\nrenderer\u002Fcheck workflow first.\n","# claw-score\n\nUse this skill when working on the OpenClaw maturity scorecard in this repo.\nThis is the openclaw-local version of the maintainer `claw-score` workflow:\nit keeps the taxonomy and scorecard concepts, but excludes discrawl and the old\ncommitted `inventory\u002F` report tree.\n\n## Authority\n\nThis skill owns the operational workflow for:\n\n- `taxonomy.yaml`\n- `qa\u002Fmaturity-scores.yaml`\n- `docs\u002Fconcepts\u002Fqa-e2e-automation.md`\n- `qa\u002Fscenarios\u002Findex.yaml`\n\nKeep person-specific, maintainer-private, Discord archive, and discrawl facts\nout of this repo. If a score needs private evidence, use the redacted\n`qa-evidence.json` artifact shape generated by OpenClaw QA workflows.\n\n## Source Model\n\n- `taxonomy.yaml` is the hand-edited source of truth for surfaces, levels,\n  QA profiles, categories, feature coverage IDs, docs refs, LTS overrides, and\n  completeness-instruction paths.\n- Each feature has exactly one `coverageIds` entry. Keep that evidence ID\n  unique to the feature; broader many-to-many evidence mapping is not part of\n  the current taxonomy schema.\n- Coverage IDs use dotted `namespace.behavior` form, with lowercase\n  alphanumeric\u002Fdash segments. Profile, surface, and category IDs may remain\n  dashed or dotted.\n- Keep categories and feature names unique, product-shaped, and broader than raw\n  coverage IDs. Do not promote generic IDs into standalone feature names.\n- Avoid duplicate coverage-ID bundles under different feature names in one\n  category.\n- `qa\u002Fmaturity-scores.yaml` is the committed aggregate source for Quality,\n  Completeness, and LTS review state.\n- `extensions\u002Fqa-lab\u002Fsrc\u002Fscorecard-taxonomy.ts` exports\n  `readValidatedQaMaturityScoreSources`; use it to validate score output.\n- Generated public docs are `docs\u002Fmaturity\u002Fscorecard.md` and\n  `docs\u002Fmaturity\u002Ftaxonomy.md`; both come from `pnpm maturity:render`. Do not\n  hand-edit generated Markdown to change score results.\n- `qa-evidence.json` artifacts provide per-run QA scorecard evidence. Release\n  profile artifacts are the source of truth for Coverage. They can enrich\n  generated artifact docs, but they are not committed as inventory.\n\n## Commands\n\nRun from the openclaw repo root.\n\nValidate taxonomy YAML structure and the maturity score schema after source\nedits:\n\n```bash\nnode --import tsx --input-type=module \u003C\u003C'NODE'\nimport fs from \"node:fs\";\nimport YAML from \"yaml\";\nimport { readValidatedQaMaturityScoreSources } from \".\u002Fextensions\u002Fqa-lab\u002Fsrc\u002Fscorecard-taxonomy.ts\";\n\nfor (const file of [\"taxonomy.yaml\", \"qa\u002Fscenarios\u002Findex.yaml\"]) {\n  YAML.parse(fs.readFileSync(file, \"utf8\"));\n}\nreadValidatedQaMaturityScoreSources();\nNODE\n```\n\nCheck docs when touching docs prose:\n\n```bash\npnpm check:docs\n```\n\nRun focused QA\u002Fprofile checks when changing coverage IDs or profile membership:\n\n```bash\npnpm openclaw qa coverage --json\n```\n\n## Full Generation Runs\n\nFor a direct full scorecard run that publishes the generated-doc pull request,\nuse floating `main` resolution by default:\n\n```bash\ngh workflow run maturity-scorecard.yml \\\n  --repo openclaw\u002Fopenclaw \\\n  --ref main \\\n  -f ref=main \\\n  -f expected_sha='' \\\n  -f publish_pull_request=true \\\n  -f allow_failures=true\n```\n\nDo not resolve `main` locally and pass that commit as both `ref` and\n`expected_sha` for an ordinary manual generation run. OpenClaw's `main` moves\nquickly, so the caller-selected commit can become stale before validation. The\nworkflow then correctly rejects publication when the pull request base contains\nnewer maturity inputs, and QA never starts.\n\nWith `ref=main` and a blank `expected_sha`, the workflow's\n`floating_default_branch` path fetches and freezes the current remote default\nbranch inside validation before handing an immutable revision to downstream\njobs. Use an explicit SHA only when the requested evidence must remain bound to\nthat exact revision, such as a release-candidate workflow call or an\nartifact-only historical reproduction. If that exact-revision run also requests\npublication and `main` has changed relevant inputs, expect validation to fail and\ndispatch again from floating `main` instead.\n\n## Scoring Workflow\n\nWhen asked to score or refresh a surface:\n\n1. Read the surface in `taxonomy.yaml`.\n2. Read the surface completeness rubric under\n   `.agents\u002Fskills\u002Fclaw-score\u002Freferences\u002Fcompleteness\u002F`.\n3. Gather public repo evidence from docs, source, tests, and QA scenario\n   metadata.\n4. Prefer existing release profile `qa-evidence.json` artifacts for executed\n   proof.\n5. Update `qa\u002Fmaturity-scores.yaml` only for Quality, Completeness, and LTS\n   review state backed by public or redacted artifact evidence.\n6. Run the schema validation command from this skill.\n7. Run `pnpm check:docs` if docs prose changed, and focused QA coverage checks\n   if coverage IDs or profile membership changed.\n\nFor subjective score changes, make the smallest defensible edit and leave the\nevidence path in the PR or task summary. Keep manual prose in current docs and\nkeep score data in `qa\u002Fmaturity-scores.yaml`.\n\n## Default Completeness Process\n\nCompleteness is scored against the intended operator-visible workflow for each\ncategory, not against test breadth or implementation quality. The completeness\nreference files under `references\u002Fcompleteness\u002F` define the category scope and\nany surface-specific variation from this default process.\n\nBy default, Completeness measures how fully OpenClaw exposes the intended\nsurface capability set to the user, operator, author, or maintainer persona for\nthat surface. Score whether each category delivers the full expected workflow,\nincluding setup, normal use, status or inspection, recovery, and important\nplatform, provider, channel, security, or lifecycle variants where they apply.\n\nTreat `Surface-Specific Scoring Questions` and `Surface-Specific Guidance` as\nhigher-priority instructions for that surface. The surface instructions may\nflesh out, narrow, or intentionally conflict with the default ideas here; when\nthey do, follow the surface instructions and make the score rationale reflect\nthat surface-specific instruction. If a reference file does not include\nsurface-specific questions or guidance, apply this default process to the\nsurface's `Category Scope`.\n\nFor each category, ask:\n\n- Can the intended user or operator complete the category workflow end to end?\n- Are the taxonomy features present as supported capabilities rather than\n  isolated implementation fragments?\n- Are the important lifecycle stages represented: setup, normal operation,\n  status\u002Finspection, recovery, and upgrade or removal where relevant?\n- Are the important environment, provider, platform, channel, or security\n  branches present for this surface?\n- Do the known gaps leave major user-visible capability branches missing?\n\nDefault guidance:\n\n- Favor higher Completeness when the category supports the full\n  operator-visible workflow described by taxonomy and category evidence.\n- Lower Completeness when only the happy path exists, when important variants\n  are undocumented or unimplemented, or when recovery\u002Fstatus paths are missing.\n- Do not lower Completeness because tests are thin; that is Coverage.\n- Do not lower Completeness because implementation quality is fragile; that is\n  Quality.\n\nDefault Completeness bands:\n\n- `Clawesome` (95-100): complete across expected workflows, variants, and\n  recovery branches, with only minor polish gaps.\n- `Stable` (80-95): the expected workflow set is broadly present, with only\n  bounded missing branches.\n- `Beta` (70-80): the main workflow exists, but meaningful branches or recovery\n  paths are still absent.\n- `Alpha` (50-70): only a partial capability set is present; users can complete\n  some core tasks but not the full expected workflow.\n- `Experimental` (0-50): the category exposes only fragments of the intended\n  capability.\n\n## Decision Context\n\nRecord an optional `decision` beside `score` and `label` for surface and category\nQuality\u002FCompleteness, or beside `supported` for category LTS. In `taxonomy.yaml`,\nuse optional `level_decision` beside the canonical surface `level`.\n\nEach record contains `value`, `rationale`, `reviewer`, `evidence_refs`, and\n`revalidate_when`. Use an integer from 0–100 for Quality\u002FCompleteness, a boolean\nfor LTS, and a declared taxonomy level ID for `level_decision`. Supply nonempty\ntext fields and at least one evidence reference. Name the actual reviewer and\nthe condition that should trigger another review.\n\nLeave unavailable history absent: it is unknown, not an invitation to invent\nreviewers, rationale, or evidence. A record does not overwrite the current score,\nsupport flag, or canonical level. If its value differs, retain both; generated\ndocs show a non-gating mismatch, including under strict input validation.\n\nDo not attach decisions to Coverage, computed rollups, surface LTS summaries, or\nthe copied level in score aggregates. Decision context does not change coverage\nidentity, score calculations, support commitments, or release gates.\n\n## Score Semantics\n\n- Coverage: deterministic release validation coverage derived from the release\n  profile `qa-evidence.json.scorecard` feature fulfillment data.\n- Quality: reliability, maintainability, operator safety, and regression\n  confidence for the category.\n- Completeness: how much of the intended operator-visible workflow exists for\n  the category. Use the default completeness process plus any surface-specific\n  variation before changing this score.\n- LTS: derived from Quality, release-evidence Coverage, and\n  `human_lts_override`; do not hand-edit generated Markdown to change LTS\n  status.\n\nBands:\n\n- `Clawesome`: 95-100\n- `Stable`: 80-95\n- `Beta`: 70-80\n- `Alpha`: 50-70\n- `Experimental`: 0-50\n\n## Artifacts\n\nDo not add the maintainer repo's `docs\u002Fkevinslin\u002Fmaturity-scorecard\u002Finventory\u002F`\ntree to openclaw. Evidence-enriched scorecard outputs belong in short-lived\nartifacts, not committed generated docs, unless this repo adds an explicit\nrenderer\u002Fcheck workflow first.\n",{"name":5,"description":7},"main","f1c93577085244aa96aa87b53a2df9b59d293bd7",".agents\u002Fskills\u002Fclaw-score",82861,"https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fopenclaw","https:\u002F\u002Fopenclaw.ai","https:\u002F\u002Fgithub.com\u002Fopenclaw","Your personal, open source AI assistant.","2026-11-12 03:01:25",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add openclaw\u002Fopenclaw --skill claw-score",{"tool":54,"command":55},"bunx","bunx skills add openclaw\u002Fopenclaw --skill claw-score",{"tool":57,"command":58},"pnpm","pnpm dlx skills add openclaw\u002Fopenclaw --skill 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