justin/pm-claude-skills

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Claude 760f979365 Add cross-tool positioning, Python helpers, tiers, and hygiene docs

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

2026-06-17 07:48:48 +00:00

4.4 KiB

Raw Blame History

name, description

name	description
rice-prioritisation	Scores and ranks product initiatives using the RICE framework. Use when asked to prioritise features, rank a backlog using RICE, score initiatives for quarterly planning, or apply an objective framework to a list of competing ideas. Produces a ranked RICE table with scores, quick wins and moonshot flags, dependency notes, and a recommended sequencing order.

RICE Prioritisation Skill

Apply consistent, criteria-based RICE scoring to a list of features or initiatives to produce an objective prioritisation ranking.

Required Inputs

Ask the user for these if not provided:

List of initiatives or features to score (names and brief descriptions)
Reach estimates (users affected per quarter — from analytics if available)
Impact estimates (use the standard scale below)
Effort estimates (person-months — from engineering if available)
Quarter or planning period

RICE Definitions (adapt to your context)

Reach: Number of users affected per quarter (use actual DAU/MAU data where available)
Impact: Effect on your primary metric — use scale: 3=massive, 2=high, 1=medium, 0.5=low, 0.25=minimal
Confidence: How certain are we about R and I estimates? 100%=high, 80%=medium, 50%=low
Effort: Person-months required across all functions

RICE Formula

RICE Score = (Reach × Impact × Confidence) / Effort

Programmatic Helper

This skill ships with a stdlib-only Python script that calculates and ranks RICE scores so the maths is consistent and the quick-win / moonshot flags are applied by rule, not by feel. Feed it the initiatives once R, I, C, and E are gathered.

# From a JSON file (confidence accepts 0.8 or 80)
python3 scripts/rice_calculator.py initiatives.json

# Or from a CSV with header: name,reach,impact,confidence,effort
python3 scripts/rice_calculator.py initiatives.csv --format csv

# Or piped in
echo '[{"name":"Onboarding","reach":5000,"impact":2,"confidence":0.8,"effort":3}]' \
  | python3 scripts/rice_calculator.py -

It outputs a ranked table with computed RICE scores and auto-flags quick-win (strong score, low relative effort), moonshot (high impact, high effort), and low-confidence (≤50%) items. Use the computed ranking as the starting point, then apply the validation step below — never accept a surprising top rank without checking the estimates behind it.

Process

For each initiative provided, gather or estimate R, I, C, E values
Flag where estimates are weak and note what data would improve them
Calculate RICE score for each
Rank highest to lowest
Flag any "quick wins" (high RICE score, low effort) and "moonshots" (high impact, high effort)
Note dependencies between items that affect sequencing
Validate — Cross-check: if the top-ranked item surprises the team, investigate whether an estimate is inflated. RICE is a tool, not a verdict.

Output Structure

RICE Prioritisation: [Backlog/Quarter]

Initiative	Reach	Impact	Confidence	Effort	RICE Score	Notes
[name]	[n]	[score]	[%]	[months]	[score]	[flags]

Recommended Sequence

[Top 5 initiatives with rationale]

Quick Wins (high score, low effort)

[Items to pick up alongside bigger bets]

Data Gaps to Address

[What information would most improve scoring accuracy]

Quality Checks

Every initiative has all four RICE components estimated (even roughly)
Confidence is 50% for anything without data backing (not 100% as a default)
Quick wins and moonshots are explicitly called out
Dependencies that affect sequencing are noted
Any surprising ranking is investigated before accepting it

Anti-Patterns

Do not default to 100% confidence on estimates that lack supporting data — this inflates scores and misleads planning
Do not treat RICE scores as a final decision — a ranking that surprises the team must be investigated before it is accepted
Do not omit effort estimates from engineering — PM-only effort estimates are frequently optimistic and skew results
Do not forget to note dependencies that would change the sequencing even if RICE scores suggest otherwise
Do not score every initiative at the same impact level — if everything is "high impact," the framework produces no useful signal