Config drift detector
Skill sisodiabhumca/agent-skills/skills/config-drift-detector
Vendor-neutral skill to detect configuration drift across environments and suggest normalization actions.From its SKILL.md
npx -y skills add sisodiabhumca/agent-skills --skill config-drift-detectorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
0.4 KB, 47 tokens by cl100k_base, as published. Nobody here has run it
Purpose
Use this skill to process structured input and produce a concise, actionable report.
Input
- JSON object or CSV rows with the relevant business signals.
Output
- Structured JSON summary with findings and recommendations.
What ships with it: 2 files
1.1 KB alongside SKILL.md, 1 of them executable
Gives 0 of the 12 instructions most project setup skills give in 47 tokens
Counted across 999 of the 1,637 authors here whose files we hold, read 2026-08-07
- Ask one question at a timein 29 of 999, across 28 files
- Detect the package manager from lockfilesin 28 of 999, across 9 files
- Present findings to the userin 26 of 999, across 5 files
- Explore current repo statein 24 of 999, across 3 files
- Update the agent skills block in place if it existsin 24 of 999, across 3 files
- Install husky lint-staged and prettierin 23 of 999, across 4 files
- Create the lintstagedrc filein 22 of 999, across 3 files
- Commit all changed filesin 22 of 999, across 3 files
- Run lint-staged to verify it worksin 22 of 999, across 3 files
- Create the husky pre-commit filein 21 of 999, across 2 files
- Create a prettierrc file if missingin 21 of 999, across 2 files
- Initialize huskyin 21 of 999, across 2 files
Said here and by no other author read
- process structured input data
- include findings in the summary
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.