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archify-review

An AI agent skill from tt-a1i/archify, scored 58 out of 100 by code.

58/ 100C

Code score by group

  • Spec20/20
  • Description12/20
  • Procedure0/15
  • Done criteria4/15
  • Resources7/10
  • Scripts5/8
  • Safety7/7
  • Upkeep3/5

It loses the most points in Procedure (15), the group that checks whether the steps are numbered, read the context first and say what to do when something fails.

What the author says it does

Review Archify issues, PRs, or code through value, cost, and impact to support evidence-based maintenance decisions. Use for issue triage, change reviews, and code quality assessments.

This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.

Repository
tt-a1i/archify
Folder
.agents/skills/archify-review
License
Apache-2.0
GitHub stars
95,776
SKILL.md lines
23
Other files
0

Add archify-review to your agent

npx skills add tt-a1i/archify --skill archify-review

The command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored.

More skills from tt-a1i/archify

  1. archify

    Create validated architecture, workflow, sequence, data-flow, lifecycle/state, ERD, class, tree, timeline, and waterfall diagrams as explorable standalone HTML with dark/light the…

    77B
  2. archify-tuning

    Improve Archify diagram accuracy, visual quality and complete authoring speed with fewer repairs. Use for generation experiments or evaluating changes to layout, routing, diagnost…

    67C
  3. simplify-codebase

    Simplification audit or authorized codebase simplification whose stated objective is to remove accidental complexity. Use for evidence-backed deletion or consolidation of dead cod…

    65C
  4. test-value

    Improve test value and feedback cost when auditing slow, flaky, redundant or brittle tests; choosing tests during implementation; or reviewing test changes before a commit or PR.…

    64C

All skills in tt-a1i/archify

Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.