[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CDKPY6G389SJQFRCKA7PAV":3,"skill-same:01M6CDKPY6G389SJQFRCKA7PAV":85},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":6,"ownerHtmlUrl":69,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":70,"install":71,"resources":84},"01M6CDKPY6G389SJQFRCKA7PAV","agentic-test-audit",null,"Audit a pytest suite: surviving mutants and untested code, flaky, order-dependent and slow tests, and removal candidates checked on held-out mutants. Report-only.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",12,"MIT","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-agentic-test-audit\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"test audit","agentic","test","agentic test audit","audit","2026","testing","suite","quick","mutation","deep","community","mutation-testing","pytest","max","only","v0","Budget","https","license","verification","report","code","github","python","safe","2026-11-10 10:12:30",false,"---\nname: agentic-test-audit\ndescription: \"Audit a pytest suite: surviving mutants and untested code, flaky, order-dependent and slow tests, and removal candidates checked on held-out mutants. Report-only.\"\ncategory: testing\nrisk: safe\nsource: community\nsource_repo: timini\u002Fagentic-verification\nsource_type: community\ndate_added: \"2026-10-16\"\nauthor: Tim Richardson\nlicense: MIT\nlicense_source: \"https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Fblob\u002Fv0.3.0\u002FLICENSE\"\ntags: [testing, pytest, mutation-testing, python, flaky-tests, test-audit]\ntools: [claude]\nargument-hint: \"quick | deep [--budget-minutes N] [--max-mutants N] [path]\"\n---\n\n# Audit a test suite\n\n## Overview\n\nThis skill measures whether a Python test suite is a good check. It asks whether the suite would\nnotice broken code, whether it is fast and stable enough to run on every change, and which tests\ncost time without adding detection. A pinned runner does the measuring; the skill reports and\ndoes not change tests. Upstream source:\n[timini\u002Fagentic-verification](https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Ftree\u002Fv0.3.0\u002Fplugin\u002Fskills\u002Faudit).\n\n## When to Use This Skill\n\n- Use when the user asks to audit, review, speed up, prune or strengthen their pytest tests.\n- Use when the user asks which tests are useful, flaky or slow.\n- Use `@agentic-verify` instead to check a single change.\n\n## How It Works\n\nArguments: `$ARGUMENTS`. The first word is the mode: `quick` (default) or `deep`. Anything\nelse goes to the runner. To check a single change rather than the whole suite, use the\n`agentic-verify` skill instead.\n\nAn agent can only be trusted as far as the checks it runs. This skill measures whether a test\nsuite is a good check: whether it would notice broken code, whether it is fast and stable enough\nto run on every change, and which tests cost time without adding detection.\n\n### Runner\n\n```bash\nAV='uvx --from git+https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification@07608b3b9e4e359bc0b93b3eca94bb78d67e369e#subdirectory=runner agentic-verification'\n$AV quick|deep \u003Cpath> [--budget-minutes N]\n$AV prune-check \u003Cpath> --tests-file \u003Cfile> --from-run \u003Crun-dir>\n$AV replay --run \u003Crun-dir> --mutant \u003Cid>\n```\n\nThe runner works in a disposable copy and writes only to `.test-suite-audit\u002F` in the project;\nsuggest adding that to `.gitignore`. It needs Python 3.12+ and `pytest-cov` in the project's\nenvironment, and `pytest-randomly` for order checks. Use a Bash timeout of the budget plus 2\nminutes. The last stdout line is JSON with the report paths.\n\n### Rules\n\n- The runner measures; you interpret. Never state a number the report does not contain, and\n  never upgrade a label (an *inferred* finding stays inferred).\n- Report-only. Do not edit, delete, skip or quarantine tests, or change source code, unless the\n  user asks for a specific change after reading the report.\n- Coverage overlap is never evidence that a test is redundant, and tests that are each redundant\n  are not redundant together. Only a removal set that `prune-check` did not refute may be\n  presented as removable, and even then as \"not refuted\", not \"safe\".\n- A surviving mutant is a candidate gap, not proof of a bug. Do not decide that a mutant is\n  equivalent yourself; say it needs human review.\n- When the user pushes for one of these conclusions, explain the measured reason from\n  [the study evidence](https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Fblob\u002Fv0.3.0\u002Fplugin\u002Fskills\u002Faudit\u002Freference\u002Fevidence.md) instead of just declining.\n\n### Steps\n\n1. **Check the target.** Confirm it is a pytest project. Look for `test-suite-audit.toml`; if\n   absent, the runner discovers source packages, test directories and the interpreter\n   (`.venv\u002Fbin\u002Fpython`, then `python3`). If discovery looks wrong (for example a monorepo),\n   write a `test-suite-audit.toml` only after asking. Keys: `python`, `source`, `tests`,\n   `pytest_args`, `env`, `workers`, `protected` (node ID prefixes or marker names of tests that\n   must never be proposed for removal). If `pytest-cov` is missing, say coverage will be \"not\n   assessed\" and offer the install command; do not install anything without asking.\n2. **State the budget** before running. Quick is 5 minutes: one baseline run, timings, coverage,\n   overlap, no mutants. Deep is 30 minutes by default: three file-order and three random-order\n   runs, coverage, every deletion mutant, then a seeded sample of classical mutants until the\n   budget ends. In our benchmark, 12 to 14 minutes completed deep audits of projects up to about\n   3,300 executable lines; dateutil (about 3,600) and click (about 5,000) needed more.\n3. **Run the audit** and read `report.json` (full evidence) and `report.md`. See\n   [reading the report](https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Fblob\u002Fv0.3.0\u002Fplugin\u002Fskills\u002Faudit\u002Freference\u002Freport.md) for statuses, labels and the detection rate.\n4. **If the deep audit proposed removal candidates**, treat them as an upper bound: in our\n   study such sets covered about 80% of each suite and every one lost kills on held-out mutants.\n   Run `prune-check` on them with\n   `--from-run \u003Crun-dir>`, so the check uses mutants the set was not chosen from. Give it at\n   least 10 minutes. Report its verdict: `refuted` (with the kills or lines lost) or `not refuted`\n   (with the number of killed mutants checked and the upper bound it gives).\n   If it is refuted, keep the tests it names as sole killers, and say the rest is still\n   unproven. Never present a refuted set as removable.\n5. **Summarise under three headings**, findings in the report's order, each with its label,\n   evidence and next validation step:\n   - *Does it catch broken code?* Surviving mutants, pseudo-tested functions, deletable calls,\n     weak tests, uncovered code, and whether property-based tests exist.\n   - *Can an agent run it on every change?* Suite wall time against the one-minute target,\n     slowest and setup-heavy tests, intermittent and order-dependent tests.\n   - *Is it bloated?* The removal set and its prune-check verdict, with the time it would save.\n     Overlap pairs are listed only as things to look at.\n   Give the reproduction command for at least one mutant finding. List what was not assessed.\n   Link `report.md`.\n6. **Offer follow-ups**, not changes: replaying a survivor; drafting a test that passes on the\n   original and fails on a confirmed mutant (the `agentic-verify` skill does this for changed code);\n   removing a not-refuted set in its own commit; or a deeper run with a larger budget.\n\nIf the run is partial (budget or interruption), say so first and explain which steps are missing.\n\n## Examples\n\n### Example 1: Quick audit\n\n```bash\nAV='uvx --from git+https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification@07608b3b9e4e359bc0b93b3eca94bb78d67e369e#subdirectory=runner agentic-verification'\n$AV quick . --budget-minutes 5\n```\n\n### Example 2: Deep audit, then check a removal set on held-out mutants\n\n```bash\n$AV deep . --budget-minutes 30\n$AV prune-check . --tests-file removal.txt --from-run .test-suite-audit\u002F\u003Crun-dir>\n```\n\n## Limitations\n\n- Python and pytest only. Needs Python 3.12+ and `pytest-cov`; order checks need `pytest-randomly`.\n- A surviving mutant is a candidate gap, not proof of a bug.\n- A `not refuted` removal set is not proof that removal is safe.\n- Large projects may need a budget above the 30-minute default for a full deep audit.\n\n## Security & Safety Notes\n\n- The runner is fetched with `uvx` from GitHub at tag `v0.3.0` (commit `07608b3b9e4e359bc0b93b3eca94bb78d67e369e`). Review it before first use.\n- It works in a temporary copy of the project and writes only to `.test-suite-audit\u002F`.\n- Report-only: it does not edit, delete, skip or quarantine tests, and installs nothing without asking.\n\n## Related Skills\n\n- `@agentic-verify` - check one change by running every mutant of the changed lines.\n\nA temporary copy is not a security sandbox: pytest, fixtures, plugins, conftest and dependency hooks execute code and may access network, credentials and external services. Inspect the pinned runner and target test setup first; use a low-privilege isolated environment with no production credentials and controlled network access. Scope secrets and environment inheritance before running mutation\u002Freplay commands. A pin identifies code, not a completed safety review.\n","# Audit a test suite\n\n## Overview\n\nThis skill measures whether a Python test suite is a good check. It asks whether the suite would\nnotice broken code, whether it is fast and stable enough to run on every change, and which tests\ncost time without adding detection. A pinned runner does the measuring; the skill reports and\ndoes not change tests. Upstream source:\n[timini\u002Fagentic-verification](https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Ftree\u002Fv0.3.0\u002Fplugin\u002Fskills\u002Faudit).\n\n## When to Use This Skill\n\n- Use when the user asks to audit, review, speed up, prune or strengthen their pytest tests.\n- Use when the user asks which tests are useful, flaky or slow.\n- Use `@agentic-verify` instead to check a single change.\n\n## How It Works\n\nArguments: `$ARGUMENTS`. The first word is the mode: `quick` (default) or `deep`. Anything\nelse goes to the runner. To check a single change rather than the whole suite, use the\n`agentic-verify` skill instead.\n\nAn agent can only be trusted as far as the checks it runs. This skill measures whether a test\nsuite is a good check: whether it would notice broken code, whether it is fast and stable enough\nto run on every change, and which tests cost time without adding detection.\n\n### Runner\n\n```bash\nAV='uvx --from git+https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification@07608b3b9e4e359bc0b93b3eca94bb78d67e369e#subdirectory=runner agentic-verification'\n$AV quick|deep \u003Cpath> [--budget-minutes N]\n$AV prune-check \u003Cpath> --tests-file \u003Cfile> --from-run \u003Crun-dir>\n$AV replay --run \u003Crun-dir> --mutant \u003Cid>\n```\n\nThe runner works in a disposable copy and writes only to `.test-suite-audit\u002F` in the project;\nsuggest adding that to `.gitignore`. It needs Python 3.12+ and `pytest-cov` in the project's\nenvironment, and `pytest-randomly` for order checks. Use a Bash timeout of the budget plus 2\nminutes. The last stdout line is JSON with the report paths.\n\n### Rules\n\n- The runner measures; you interpret. Never state a number the report does not contain, and\n  never upgrade a label (an *inferred* finding stays inferred).\n- Report-only. Do not edit, delete, skip or quarantine tests, or change source code, unless the\n  user asks for a specific change after reading the report.\n- Coverage overlap is never evidence that a test is redundant, and tests that are each redundant\n  are not redundant together. Only a removal set that `prune-check` did not refute may be\n  presented as removable, and even then as \"not refuted\", not \"safe\".\n- A surviving mutant is a candidate gap, not proof of a bug. Do not decide that a mutant is\n  equivalent yourself; say it needs human review.\n- When the user pushes for one of these conclusions, explain the measured reason from\n  [the study evidence](https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Fblob\u002Fv0.3.0\u002Fplugin\u002Fskills\u002Faudit\u002Freference\u002Fevidence.md) instead of just declining.\n\n### Steps\n\n1. **Check the target.** Confirm it is a pytest project. Look for `test-suite-audit.toml`; if\n   absent, the runner discovers source packages, test directories and the interpreter\n   (`.venv\u002Fbin\u002Fpython`, then `python3`). If discovery looks wrong (for example a monorepo),\n   write a `test-suite-audit.toml` only after asking. Keys: `python`, `source`, `tests`,\n   `pytest_args`, `env`, `workers`, `protected` (node ID prefixes or marker names of tests that\n   must never be proposed for removal). If `pytest-cov` is missing, say coverage will be \"not\n   assessed\" and offer the install command; do not install anything without asking.\n2. **State the budget** before running. Quick is 5 minutes: one baseline run, timings, coverage,\n   overlap, no mutants. Deep is 30 minutes by default: three file-order and three random-order\n   runs, coverage, every deletion mutant, then a seeded sample of classical mutants until the\n   budget ends. In our benchmark, 12 to 14 minutes completed deep audits of projects up to about\n   3,300 executable lines; dateutil (about 3,600) and click (about 5,000) needed more.\n3. **Run the audit** and read `report.json` (full evidence) and `report.md`. See\n   [reading the report](https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Fblob\u002Fv0.3.0\u002Fplugin\u002Fskills\u002Faudit\u002Freference\u002Freport.md) for statuses, labels and the detection rate.\n4. **If the deep audit proposed removal candidates**, treat them as an upper bound: in our\n   study such sets covered about 80% of each suite and every one lost kills on held-out mutants.\n   Run `prune-check` on them with\n   `--from-run \u003Crun-dir>`, so the check uses mutants the set was not chosen from. Give it at\n   least 10 minutes. Report its verdict: `refuted` (with the kills or lines lost) or `not refuted`\n   (with the number of killed mutants checked and the upper bound it gives).\n   If it is refuted, keep the tests it names as sole killers, and say the rest is still\n   unproven. Never present a refuted set as removable.\n5. **Summarise under three headings**, findings in the report's order, each with its label,\n   evidence and next validation step:\n   - *Does it catch broken code?* Surviving mutants, pseudo-tested functions, deletable calls,\n     weak tests, uncovered code, and whether property-based tests exist.\n   - *Can an agent run it on every change?* Suite wall time against the one-minute target,\n     slowest and setup-heavy tests, intermittent and order-dependent tests.\n   - *Is it bloated?* The removal set and its prune-check verdict, with the time it would save.\n     Overlap pairs are listed only as things to look at.\n   Give the reproduction command for at least one mutant finding. List what was not assessed.\n   Link `report.md`.\n6. **Offer follow-ups**, not changes: replaying a survivor; drafting a test that passes on the\n   original and fails on a confirmed mutant (the `agentic-verify` skill does this for changed code);\n   removing a not-refuted set in its own commit; or a deeper run with a larger budget.\n\nIf the run is partial (budget or interruption), say so first and explain which steps are missing.\n\n## Examples\n\n### Example 1: Quick audit\n\n```bash\nAV='uvx --from git+https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification@07608b3b9e4e359bc0b93b3eca94bb78d67e369e#subdirectory=runner agentic-verification'\n$AV quick . --budget-minutes 5\n```\n\n### Example 2: Deep audit, then check a removal set on held-out mutants\n\n```bash\n$AV deep . --budget-minutes 30\n$AV prune-check . --tests-file removal.txt --from-run .test-suite-audit\u002F\u003Crun-dir>\n```\n\n## Limitations\n\n- Python and pytest only. Needs Python 3.12+ and `pytest-cov`; order checks need `pytest-randomly`.\n- A surviving mutant is a candidate gap, not proof of a bug.\n- A `not refuted` removal set is not proof that removal is safe.\n- Large projects may need a budget above the 30-minute default for a full deep audit.\n\n## Security & Safety Notes\n\n- The runner is fetched with `uvx` from GitHub at tag `v0.3.0` (commit `07608b3b9e4e359bc0b93b3eca94bb78d67e369e`). Review it before first use.\n- It works in a temporary copy of the project and writes only to `.test-suite-audit\u002F`.\n- Report-only: it does not edit, delete, skip or quarantine tests, and installs nothing without asking.\n\n## Related Skills\n\n- `@agentic-verify` - check one change by running every mutant of the changed lines.\n\nA temporary copy is not a security sandbox: pytest, fixtures, plugins, conftest and dependency hooks execute code and may access network, credentials and external services. Inspect the pinned runner and target test setup first; use a low-privilege isolated environment with no production credentials and controlled network access. Scope secrets and environment inheritance before running mutation\u002Freplay commands. A pin identifies code, not a completed safety review.\n",{"name":5,"risk":48,"tags":54,"tools":57,"author":59,"source":34,"license":14,"category":29,"date_added":60,"description":7,"source_repo":61,"source_type":34,"argument-hint":62,"license_source":63},[29,36,35,47,55,56],"flaky-tests","test-audit",[58],"claude","Tim Richardson","2026-10-16","timini\u002Fagentic-verification","quick | deep [--budget-minutes N] [--max-mutants N] [path]","https:\u002F\u002Fgithub.com\u002Ftimini\u002Fagentic-verification\u002Fblob\u002Fv0.3.0\u002FLICENSE","main","e8c4cb5cbafb600fab08ffdf4b23359c9f608d9d","skills\u002Fantigravity-agentic-test-audit",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-11 14:20:43",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add boisenoise\u002Fskills-collections --skill agentic-test-audit",{"tool":77,"command":78},"bunx","bunx skills add boisenoise\u002Fskills-collections --skill agentic-test-audit",{"tool":80,"command":81},"pnpm","pnpm dlx skills add boisenoise\u002Fskills-collections --skill agentic-test-audit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-agentic-test-audit\u002FSKILL.md",true,[],[86,114,137,162,181,204],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":90,"licenseFileUrl":91,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":49},"01M6WNH92BHVFXHGPDZCPJ0AG0","web-access","Use Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[95,96,97,98,99,100,101,102,103,104,105,38,106,107,108,109,110,111,112,113,28],"web","access","web access","official","development","browser","search","data","Tools","read","fetch","youtube","free tools","platform","critical","free","cover","normal","when",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":90,"licenseFileUrl":118,"sourceHtmlUrl":119,"categories":120,"keywords":124,"publishedAt":49},"01M6WNF9XAYAB2CZDDJBAKNM93","treg","Use the Treg CLI or MCP catalog to discover, price and call authorized external tools with team-scoped credentials and a user-approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FSKILL.md",[121],{"categoryNo":122,"categoryName":123,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[116,125,126,46,98,99,42,103,40,127,128,129,130,131,132,109,133,134,135,136,28],"MCP","cli","credentials","team","catalog","use","call","discover","external tools","with","scoped","blob",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":90,"licenseFileUrl":141,"sourceHtmlUrl":142,"categories":143,"keywords":149,"publishedAt":49},"01M6WN93MEAZ4E41DYAHJRPHQT","recruit","Turn a job description into an evidence-linked candidate shortlist through Treg, with job-related ranking and optional authorized business contact enrichment.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FSKILL.md",[144],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[139,46,98,42,41,150,151,152,153,154,109,155,156,157,134,158,159,160,161,136,116,28],"email","enrichment","business","evidence","phone","ranking","post","job description","for","person","candidate","reason",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":90,"licenseFileUrl":166,"sourceHtmlUrl":167,"categories":168,"keywords":169,"publishedAt":49},"01M6WN8R5RXSMHM9B4D9FAFX42","rea-growth","Analyze a company’s public growth and content through Treg, distinguish observations from hypotheses, and produce an evidence-linked playbook within an approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FSKILL.md",[],[170,171,172,46,98,42,173,174,41,175,40,176,177,178,153,109,179,180,136,116,28],"growth","rea","rea 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