agentleFS
samsam007bsamsam007b/claude-code-standards-kit/skills/risk-score/SKILL.md

risk-score

Skill2 starsChanged 8 months agoMITView on GitHub

Calculate and display the composite SQWR Risk Score — weighted average across 8 audit domains producing a single 0-100 health indicator

2 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If "full" → run full recalculation

    line 42
  2. Very specific:

    If "quick" or empty → check .sqwr-last-state.sh for cached scores

    line 43
  3. Very specific:

    Read .sqwr-last-state.sh. If scores exist and SQWR_LAST_AUDIT_DATE is today or yesterday, use them.

    line 49
  4. Very specific:

    Run agents/AGENT-RISK-SCORE.md with argument "full".

    line 56

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

/risk-score — Composite Risk Score

Usage: /risk-score [mode]

Calculates the composite SQWR Risk Score using the weighted formula across all 8 audit domains.

Modes:

  • quick — Uses cached scores from .sqwr-last-state.sh (default if < 24h old)
  • full — Reruns all 8 audit agents for fresh scores

Weighted Formula

Risk Score = (Security × 0.22) + (Performance × 0.18) + (Code Quality × 0.18)
           + (Observability × 0.12) + (Accessibility × 0.12) + (Design × 0.08)
           + (AI Governance × 0.05) + (Deployment × 0.05)

Step 1 — Determine Mode

Parse $ARGUMENTS:

  • If "full" → run full recalculation
  • If "quick" or empty → check .sqwr-last-state.sh for cached scores

Step 2a — Quick Mode (cached scores)

Read .sqwr-last-state.sh. If scores exist and SQWR_LAST_AUDIT_DATE is today or yesterday, use them. Otherwise, warn user and switch to full mode.


Step 2b — Full Mode (rerun audits)

Run agents/AGENT-RISK-SCORE.md with argument "full".

This agent will orchestrate all 8 domain audits and compute the composite score.


Step 3 — Display Results

Apply the weighted formula and display the risk score report:

╔══════════════════════════════════════╗
║     SQWR RISK SCORE                  ║
╚══════════════════════════════════════╝

[Domain scores table]

COMPOSITE RISK SCORE: [X]/100 — [Level]

Scoring levels:

  • 85-100: Excellent — Ready for production
  • 70-84: Good — Minor improvements recommended
  • 50-69: Warning — Address before next release
  • 0-49: Critical — BLOCKING — fix before any deployment

Observable truth: Risk Score displayed with domain breakdown and actionable recommendations.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.