[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6BXRQY93FS103VB2Y1TC5WD":3,"skill-same:01M6BXRQY93FS103VB2Y1TC5WD":79},{"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":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":6,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6BXRQY93FS103VB2Y1TC5WD","speckit-plan",null,"Execute the implementation planning workflow using the plan template to generate design artifacts.","gram-data\u002Ftree-sitter-gram","gram-data","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F64245548?v=4","01M6BXQXB5HJV0M61XTAABYKTE","Gram Data","01M6BXQXFKDCKKB0KVXC7V5TM8",6,"MIT","https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fspeckit-plan\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"speckit","plan","speckit plan","phase","design","for","true","kit","with","spec-kit","false","project","structure","planning workflow","generate design","template","templates","specify","artifacts","planning","generate","project structure","workflow","github","spec","directory","2026-11-10 10:12:30",false,"---\nname: harness-engineering\ndescription: 'Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.'\n---\n\n# Harness Engineering\n\nHarness engineering turns repeated coding-agent mistakes into durable\nrepository artifacts:\n\n```text\nHarness = Instructions + Constraints + Feedback + Memory + Evaluation + Governance\n```\n\nUse this skill when the user asks to:\n\n- make a repository more reliable for GitHub Copilot or other coding agents\n- add durable agent instructions, repository rules, or guardrails\n- prevent repeated AI coding-agent mistakes\n- record known failure paths and the checks that prevent recurrence\n- add lightweight drift checks for project rules\n- review, refresh, or update an existing agent harness\n\nDo not use this skill for ordinary feature implementation unless the user asks\nto improve the repository's agent operating environment.\n\n## Core Principles\n\n- Treat the target repository as the source of truth.\n- Inspect before editing. Preserve the existing stack, package manager, CI,\n  docs, naming, and architecture.\n- Add the smallest useful harness. Prefer updating existing files over adding\n  duplicate guidance.\n- Make important rules enforceable where practical through tests, linters,\n  type checks, CI, pre-commit hooks, or drift scripts.\n- Use manual review points only when automation would be brittle or misleading.\n- Record high-risk failures that should not recur, and name the check or review\n  point that catches recurrence.\n- Do not copy generic templates blindly. Adapt every artifact to real evidence\n  in the target repository.\n\n## Discovery\n\nBefore proposing or making harness changes, inspect the repository for existing\nrules and evidence.\n\nRead these files and folders when they exist:\n\n- `README.md`\n- `AGENTS.md`\n- `.github\u002Fcopilot-instructions.md`\n- `.github\u002Finstructions\u002F`\n- `.github\u002Fworkflows\u002F`\n- `CONTRIBUTING.md`\n- package manifests such as `package.json`, `pyproject.toml`, `go.mod`,\n  `Cargo.toml`, `pom.xml`, or `build.gradle`\n- existing docs under `docs\u002F`\n- existing scripts under `scripts\u002F`\n- existing tests and CI checks\n\nThen summarize:\n\n- stack, package manager, and entry points\n- existing development and verification commands\n- current agent instructions or repository conventions\n- known failures, incidents, flaky paths, or repeated review comments\n- gaps where project rules are not enforced\n\n## Adoption Workflow\n\nFollow this sequence:\n\n1. Choose the harness surface that fits the target repository.\n2. Write target-specific agent instructions.\n3. Add enforceable checks for high-value rules.\n4. Record failure memory for high-risk or recurring failures.\n5. Add drift checks for guidance that can silently become stale.\n6. Report the adoption with evidence, assumptions, and follow-up.\n\n### 1. Choose the Harness Surface\n\nPick only the surfaces that fit the target repository:\n\n| Need | Preferred artifact |\n| --- | --- |\n| Always-on agent behavior | `AGENTS.md` or `.github\u002Fcopilot-instructions.md` |\n| File-scoped guidance | `.github\u002Finstructions\u002F*.instructions.md` |\n| Recurring project checks | `scripts\u002Fcheck_*.py`, shell scripts, or package scripts |\n| CI enforcement | existing workflow files or a small new workflow |\n| Known failures | `docs\u002Ffailures\u002F*.md` |\n| Architecture or process decisions | `docs\u002Fdecisions\u002F*.md` |\n| Adoption evidence | `docs\u002Fharness\u002Fadoption-report.md` or similar |\n\nIf the repository already has an equivalent location, update it instead of\ncreating a parallel system.\n\n### 2. Write Agent Instructions\n\nAgent instructions should be concrete and operational. Include:\n\n- project purpose and major ownership boundaries\n- setup, test, lint, build, and verification commands\n- package manager and dependency rules\n- safe editing rules, generated file rules, and forbidden paths\n- testing expectations for changed code\n- PR and commit conventions if the repo has them\n- how to record new failures or decisions\n\nAvoid broad personality guidance, generic best practices, and rules that cannot\nbe checked or reviewed.\n\n### 3. Add Enforceable Checks\n\nConvert high-value rules into checks. Good harness checks are:\n\n- narrow enough to avoid false positives\n- fast enough to run locally and in CI\n- named clearly so agents can run them before finishing\n- documented with the rule they protect\n\nExamples:\n\n```text\nRule: Do not edit generated API clients.\nCheck: script scans diffs for generated paths and fails with a clear message.\n\nRule: Every failure memory note names a regression check.\nCheck: script validates docs\u002Ffailures\u002F*.md for a \"Detection\" section.\n\nRule: Profile docs and templates must stay aligned.\nCheck: test compares profile README files to expected template files.\n```\n\n### 4. Record Failure Memory\n\nRecord failures when they are user-visible, high-risk, or likely to recur.\nUse a new file under `docs\u002Ffailures\u002F` unless an existing note already covers\nthe same root cause.\n\nRecommended structure:\n\n```markdown\n# Short Failure Title\n\n## Summary\n\nWhat failed, who saw it, and why it matters.\n\n## Root Cause\n\nThe technical or process cause. Avoid blame.\n\n## Prevention\n\nInstruction, test, drift check, CI gate, fixture, or manual review point that\nprevents or detects recurrence.\n\n## Evidence\n\nLinks to issue, PR, test, log, command output, or file paths.\n```\n\nIf no automated check is practical, record the manual review point and why\nautomation would be unsafe or misleading.\n\n### 5. Add Drift Checks\n\nUse drift checks for guidance that can silently become stale. Common examples:\n\n- docs mention commands that no longer exist\n- profile snippets and generated examples diverge\n- failure notes omit regression checks\n- decision records are missing for structural changes\n- CI references stale scripts or package commands\n\nPrefer small scripts using the repository's existing language. If the repo has\nno scripting convention, Python with only the standard library is a portable\ndefault.\n\n### 6. Report the Adoption\n\nFinish substantial harness work with an adoption report that includes:\n\n- files changed\n- rules added or updated\n- checks added or reused\n- commands run and results\n- assumptions and manual follow-up\n- failure memory created or intentionally skipped\n- how effectiveness will be measured\n\n## Review Workflow\n\nWhen asked to review a harness change, take an opposing perspective. Look for:\n\n- generic rules copied without evidence from the target repository\n- duplicate or conflicting instruction files\n- broad checks that are likely to fail on valid changes\n- unenforced high-risk rules\n- missing failure memory for repeated mistakes or runtime failures\n- generated docs not refreshed after source changes\n- CI gates that do not run the relevant checks\n- target repository conventions being overwritten by harness defaults\n\nReport findings first, ordered by severity, with file and line references when\navailable. Do not modify files during a review unless the user explicitly asks\nfor fixes.\n\n## Output Contract\n\nBefore finishing harness adoption work, verify:\n\n- the target repository was inspected before edits\n- new guidance is specific to the target repository\n- changed checks can be run locally or have a documented manual substitute\n- failure memory was recorded when required, or the final response explains why\n  it was skipped\n- generated docs or indexes are refreshed\n- the final report names every command run and its result\n\n## Optional Reference\n\nThe prompt-first workflow in\n`https:\u002F\u002Fgithub.com\u002Fbaskduf\u002Fharness-starter-kit` is a reference implementation\nof these ideas. Use it as reference material only when the user asks for it or\nwhen the repository already includes it. The target repository remains the\nsource of truth.\n","# Harness Engineering\n\nHarness engineering turns repeated coding-agent mistakes into durable\nrepository artifacts:\n\n```text\nHarness = Instructions + Constraints + Feedback + Memory + Evaluation + Governance\n```\n\nUse this skill when the user asks to:\n\n- make a repository more reliable for GitHub Copilot or other coding agents\n- add durable agent instructions, repository rules, or guardrails\n- prevent repeated AI coding-agent mistakes\n- record known failure paths and the checks that prevent recurrence\n- add lightweight drift checks for project rules\n- review, refresh, or update an existing agent harness\n\nDo not use this skill for ordinary feature implementation unless the user asks\nto improve the repository's agent operating environment.\n\n## Core Principles\n\n- Treat the target repository as the source of truth.\n- Inspect before editing. Preserve the existing stack, package manager, CI,\n  docs, naming, and architecture.\n- Add the smallest useful harness. Prefer updating existing files over adding\n  duplicate guidance.\n- Make important rules enforceable where practical through tests, linters,\n  type checks, CI, pre-commit hooks, or drift scripts.\n- Use manual review points only when automation would be brittle or misleading.\n- Record high-risk failures that should not recur, and name the check or review\n  point that catches recurrence.\n- Do not copy generic templates blindly. Adapt every artifact to real evidence\n  in the target repository.\n\n## Discovery\n\nBefore proposing or making harness changes, inspect the repository for existing\nrules and evidence.\n\nRead these files and folders when they exist:\n\n- `README.md`\n- `AGENTS.md`\n- `.github\u002Fcopilot-instructions.md`\n- `.github\u002Finstructions\u002F`\n- `.github\u002Fworkflows\u002F`\n- `CONTRIBUTING.md`\n- package manifests such as `package.json`, `pyproject.toml`, `go.mod`,\n  `Cargo.toml`, `pom.xml`, or `build.gradle`\n- existing docs under `docs\u002F`\n- existing scripts under `scripts\u002F`\n- existing tests and CI checks\n\nThen summarize:\n\n- stack, package manager, and entry points\n- existing development and verification commands\n- current agent instructions or repository conventions\n- known failures, incidents, flaky paths, or repeated review comments\n- gaps where project rules are not enforced\n\n## Adoption Workflow\n\nFollow this sequence:\n\n1. Choose the harness surface that fits the target repository.\n2. Write target-specific agent instructions.\n3. Add enforceable checks for high-value rules.\n4. Record failure memory for high-risk or recurring failures.\n5. Add drift checks for guidance that can silently become stale.\n6. Report the adoption with evidence, assumptions, and follow-up.\n\n### 1. Choose the Harness Surface\n\nPick only the surfaces that fit the target repository:\n\n| Need | Preferred artifact |\n| --- | --- |\n| Always-on agent behavior | `AGENTS.md` or `.github\u002Fcopilot-instructions.md` |\n| File-scoped guidance | `.github\u002Finstructions\u002F*.instructions.md` |\n| Recurring project checks | `scripts\u002Fcheck_*.py`, shell scripts, or package scripts |\n| CI enforcement | existing workflow files or a small new workflow |\n| Known failures | `docs\u002Ffailures\u002F*.md` |\n| Architecture or process decisions | `docs\u002Fdecisions\u002F*.md` |\n| Adoption evidence | `docs\u002Fharness\u002Fadoption-report.md` or similar |\n\nIf the repository already has an equivalent location, update it instead of\ncreating a parallel system.\n\n### 2. Write Agent Instructions\n\nAgent instructions should be concrete and operational. Include:\n\n- project purpose and major ownership boundaries\n- setup, test, lint, build, and verification commands\n- package manager and dependency rules\n- safe editing rules, generated file rules, and forbidden paths\n- testing expectations for changed code\n- PR and commit conventions if the repo has them\n- how to record new failures or decisions\n\nAvoid broad personality guidance, generic best practices, and rules that cannot\nbe checked or reviewed.\n\n### 3. Add Enforceable Checks\n\nConvert high-value rules into checks. Good harness checks are:\n\n- narrow enough to avoid false positives\n- fast enough to run locally and in CI\n- named clearly so agents can run them before finishing\n- documented with the rule they protect\n\nExamples:\n\n```text\nRule: Do not edit generated API clients.\nCheck: script scans diffs for generated paths and fails with a clear message.\n\nRule: Every failure memory note names a regression check.\nCheck: script validates docs\u002Ffailures\u002F*.md for a \"Detection\" section.\n\nRule: Profile docs and templates must stay aligned.\nCheck: test compares profile README files to expected template files.\n```\n\n### 4. Record Failure Memory\n\nRecord failures when they are user-visible, high-risk, or likely to recur.\nUse a new file under `docs\u002Ffailures\u002F` unless an existing note already covers\nthe same root cause.\n\nRecommended structure:\n\n```markdown\n# Short Failure Title\n\n## Summary\n\nWhat failed, who saw it, and why it matters.\n\n## Root Cause\n\nThe technical or process cause. Avoid blame.\n\n## Prevention\n\nInstruction, test, drift check, CI gate, fixture, or manual review point that\nprevents or detects recurrence.\n\n## Evidence\n\nLinks to issue, PR, test, log, command output, or file paths.\n```\n\nIf no automated check is practical, record the manual review point and why\nautomation would be unsafe or misleading.\n\n### 5. Add Drift Checks\n\nUse drift checks for guidance that can silently become stale. Common examples:\n\n- docs mention commands that no longer exist\n- profile snippets and generated examples diverge\n- failure notes omit regression checks\n- decision records are missing for structural changes\n- CI references stale scripts or package commands\n\nPrefer small scripts using the repository's existing language. If the repo has\nno scripting convention, Python with only the standard library is a portable\ndefault.\n\n### 6. Report the Adoption\n\nFinish substantial harness work with an adoption report that includes:\n\n- files changed\n- rules added or updated\n- checks added or reused\n- commands run and results\n- assumptions and manual follow-up\n- failure memory created or intentionally skipped\n- how effectiveness will be measured\n\n## Review Workflow\n\nWhen asked to review a harness change, take an opposing perspective. Look for:\n\n- generic rules copied without evidence from the target repository\n- duplicate or conflicting instruction files\n- broad checks that are likely to fail on valid changes\n- unenforced high-risk rules\n- missing failure memory for repeated mistakes or runtime failures\n- generated docs not refreshed after source changes\n- CI gates that do not run the relevant checks\n- target repository conventions being overwritten by harness defaults\n\nReport findings first, ordered by severity, with file and line references when\navailable. Do not modify files during a review unless the user explicitly asks\nfor fixes.\n\n## Output Contract\n\nBefore finishing harness adoption work, verify:\n\n- the target repository was inspected before edits\n- new guidance is specific to the target repository\n- changed checks can be run locally or have a documented manual substitute\n- failure memory was recorded when required, or the final response explains why\n  it was skipped\n- generated docs or indexes are refreshed\n- the final report names every command run and its result\n\n## Optional Reference\n\nThe prompt-first workflow in\n`https:\u002F\u002Fgithub.com\u002Fbaskduf\u002Fharness-starter-kit` is a reference implementation\nof these ideas. Use it as reference material only when the user asks for it or\nwhen the repository already includes it. The target repository remains the\nsource of truth.\n",{"name":55,"description":56},"harness-engineering","Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.","main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd",".claude\u002Fskills\u002Fspeckit-plan",2,"https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram","https:\u002F\u002Fgithub.com\u002Fgram-data","Data format for working with connected data","2026-11-09 20:28:36",{"source":8,"skillName":55,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add gram-data\u002Ftree-sitter-gram --skill harness-engineering",{"tool":71,"command":72},"bunx","bunx skills add gram-data\u002Ftree-sitter-gram --skill harness-engineering",{"tool":74,"command":75},"pnpm","pnpm dlx skills add gram-data\u002Ftree-sitter-gram --skill harness-engineering","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fspeckit-plan\u002FSKILL.md",true,[],[80,86,119,134,142,156],{"skillNo":81,"name":82,"nameZh":6,"description":6,"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":50},"01M6BXSRDDQZQY85T7HA1PNP0E","gram","https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002Ftools\u002Fgram\u002Fskills\u002Fgram\u002FSKILL.md",[],[82],{"skillNo":87,"name":82,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":96,"publishedAt":50},"01M6BXSNS6HJRW4Q2M86NXWMSC","Work with gram notation files (.gram). Use this skill when the user is creating, reading, editing, or validating gram notation — a text format for Pattern\u003CSubject> structures. Gram encodes nodes, annotations, relationships, and general list\u002Ftree patterns in two interchangeable syntactic styles: square-bracket pattern notation and parenthesis path notation. Trigger on .gram files, graph data tasks, or any task involving Pattern\u003CSubject> serialization.","https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002Fskills\u002Fgram\u002FSKILL.md",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[82,97,47,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,29,118],"cli","tasks","graph","https","serialization","data","verify","format","text","annotations","work","list","install","files","patterns","square","pattern","cargo","reading","tree","path","when",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":50},"01M6BXSKPF9TCQ6W10R1FK057D","speckit-taskstoissues","Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.","https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fspeckit-taskstoissues\u002FSKILL.md",[],[126,24,28,49,48,47,45,98,127,42,128,41,40,129,130,36,35,131,132,33,32,31,29,133],"speckit taskstoissues","github issues","issues","convert","dependency","feature","actionable","Existing",{"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":140,"publishedAt":50},"01M6BXSHFER1S6YBP3QYNFBRC1","speckit-tasks","Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.","https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fspeckit-tasks\u002FSKILL.md",[],[98,141,24,28,49,48,47,45,44,42,41,40,130,36,35,131,132,33,32,31,29],"speckit tasks",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":50},"01M6BXSFGB5PGR2JYZ6H0N2N8B","speckit-specify","Create or update the feature specification from a natural language feature description.","https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fspeckit-specify\u002FSKILL.md",[],[41,149,24,49,48,47,45,150,151,40,152,153,36,35,154,131,33,32,155,31],"speckit specify","create","update","specification","natural language","commands","feature specification",{"skillNo":157,"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":158,"categories":159,"keywords":161,"publishedAt":50},"01M6BXSCZ235X1JQ06GB78Q68H","https:\u002F\u002Fgithub.com\u002Fgram-data\u002Ftree-sitter-gram\u002Fblob\u002Fmain\u002F.cursor\u002Fskills\u002Fspeckit-plan\u002FSKILL.md",[160],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[25,26,24,28,49,48,47,46,45,44,43,42,41,40,39,38,37,36,35,33,32,31]]