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Add cross-tool positioning, Python helpers, tiers, and hygiene docs

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Five improvements to position the library as a serious engineering project:

1. Cross-tool compatibility — new README "Works With" section honestly
   documenting where skills run (Claude Code natively; SKILL.md bodies
   port to other agents and chat LLMs as system prompts).

2. Python helper scripts (stdlib-only) for the three strongest skills:
   - sprint-planning: capacity_calculator.py (recommended commitment)
   - rice-prioritisation: rice_calculator.py (ranks, flags quick wins/moonshots)
   - cs-health-scorecard: health_score.py (weighted total + RAG)
   Each is wired into its SKILL.md and synced to the plugin copies.

3. Explicit skill tiering — TIERS.md + README section marking 46
   Production-Ready skills and calling out Experimental (external-dependency)
   ones; everything else is Stable.

4. Repository hygiene — new CHANGELOG.md (Keep a Changelog format) and
   SKILL-AUTHORING-STANDARD.md; refreshed SECURITY.md version table and
   helper-script disclosure; added .gitignore.

5. Related Projects — README section linking to alirezarezvani/claude-skills
   and the major awesome-claude-skills / awesome-claude-code lists.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px
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2026-06-17 07:48:48 +00:00
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@@ -35,6 +35,20 @@ Score each dimension 1–5. Weight as shown. Calculate weighted total out of 100
- 60–79: Amber (at risk, needs attention)
- 0–59: Red (high churn risk, escalate)
## Programmatic Helper
This skill ships with a stdlib-only Python script that applies the weights above and converts the weighted total to a RAG status — so the headline score is computed identically every time and weights always sum to 100%.
```bash
# Five scores 1-5 in order: adoption engagement outcomes support commercial
python3 scripts/health_score.py --scores 4 3 4 2 5 --account "Acme Corp"
# Or from JSON (lets you override the default weights per account/segment)
python3 scripts/health_score.py --input account.json
```
It returns the per-dimension weighted points, the **total out of 100**, and the **RAG band** (Green ≥80, Amber 60–79, Red <60) with a one-line next step. Run it to set the headline number, then write the dimension detail and actions below around it. Add `--json` for downstream tooling.
## Output Format
---