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code-health

Skill1 starsChanged 56 days agoMITView on GitHub

Score repo quality 0-10 from real tool output, with before/after and trends.

10 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use dependency-watch, perform-task and 3 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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    perform-task

    Resolve, start or resume, implement, review, test, and publish exactly one existing ai-tdesktop task by short slug or full dated id, including rare blocked retries and split-required results. Use when the user invokes $perform-task or /perform-task with a known task name, or when the continue scheduler delegates one selected task. Runs standard review lenses with fast applicability bailouts and selects task-specific domain and evidence instruments without selecting additional work.

    Also in 3 projects that use this

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  • Trejon-888Skill

    continue

    Load project context, alignment audit, status check, suggest next action. Auto-invoke at session start, after context switches, or when user says 'continue', 'pick up where we left off', 'resume', 'what's next', 'status check', 'what now'.

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The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 10 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run the project's real tooling (read the repo's declared commands, never invent):

    line 15
  2. Very specific:

    If the project keeps a health history, store scores in the repo's own data file (e.g.

    line 37

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<!-- hermes-add-skill: concept adapted from gstack /health (garrytan/gstack, MIT); implementation is host-agnostic. -->

Code health score

Quantify repo quality from the project's own tools — a composite, weighted score with before/after and trend tracking. It turns "it looks clean" into a number you can audit and compare between stages.

How

Run the project's real tooling (read the repo's declared commands, never invent):

  • type checker
  • linter (with the repo's configured rules)
  • test runner
  • dead-code / unused-import detector, if the project has one
  • shell linter for any shell files

Compute a composite 0-10 score with explicit weights you state up front (defaults: tests 40%, type-check 25%, lint 20%, dead-code 10%, shell 5% — adjust to the project and declare the weights in the report). Each failing check subtracts proportionally.

What must be in the report

  • The commands run and their real output (evidence, per rules/quality.md).
  • The score breakdown and the weights used.
  • Any check that could not run, and why (blocked, not silently zero-rated).
  • A before/after when used across stages (e.g. a fix's effect on the score) or the trend against previously stored scores, when a history exists.

Persistence (optional, per-project)

If the project keeps a health history, store scores in the repo's own data file (e.g. .context/health.jsonl) so trends and before/after survive across sessions. Do not store scores in global memory — project data lives in the project.

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