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
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Claude
@@ -25,6 +25,24 @@ Ask the user for these if not provided:
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## RICE Formula
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RICE Score = (Reach × Impact × Confidence) / Effort
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## Programmatic Helper
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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.
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```bash
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# From a JSON file (confidence accepts 0.8 or 80)
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python3 scripts/rice_calculator.py initiatives.json
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# Or from a CSV with header: name,reach,impact,confidence,effort
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python3 scripts/rice_calculator.py initiatives.csv --format csv
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# Or piped in
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echo '[{"name":"Onboarding","reach":5000,"impact":2,"confidence":0.8,"effort":3}]' \
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| python3 scripts/rice_calculator.py -
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```
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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.
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## Process
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1. For each initiative provided, gather or estimate R, I, C, E values
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2. Flag where estimates are weak and note what data would improve them
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@@ -0,0 +1,170 @@
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#!/usr/bin/env python3
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"""RICE score calculator for the rice-prioritisation skill.
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Computes RICE = (Reach × Impact × Confidence) / Effort for a list of
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initiatives, ranks them, and flags quick wins and moonshots so the ranking in a
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prioritisation doc is calculated consistently rather than eyeballed. Pure Python
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standard library — no dependencies, no network access.
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Input
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-----
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A JSON or CSV list of initiatives. Each needs: name, reach, impact, confidence,
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effort.
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- impact uses the standard RICE scale (3, 2, 1, 0.5, 0.25) but any number works.
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- confidence is a fraction (0.8) or a percentage (80) — both are accepted.
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- effort is in person-months and must be > 0.
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JSON example (rice.json):
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[
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{"name": "Onboarding redesign", "reach": 5000, "impact": 2, "confidence": 0.8, "effort": 3},
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{"name": "Dark mode", "reach": 8000, "impact": 0.5, "confidence": 1.0, "effort": 1}
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]
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CSV example (header row required):
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name,reach,impact,confidence,effort
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Onboarding redesign,5000,2,0.8,3
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Dark mode,8000,0.5,1.0,1
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Usage
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-----
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python3 rice_calculator.py rice.json
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python3 rice_calculator.py rice.csv --format csv
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cat rice.json | python3 rice_calculator.py - --json
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"""
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from __future__ import annotations
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import argparse
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import csv
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import io
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import json
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import sys
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from dataclasses import dataclass
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@dataclass
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class Initiative:
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name: str
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reach: float
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impact: float
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confidence: float
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effort: float
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@property
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def score(self) -> float:
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if self.effort <= 0:
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raise ValueError(f"Effort for '{self.name}' must be greater than 0.")
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return (self.reach * self.impact * self.confidence) / self.effort
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def _normalise_confidence(value: float) -> float:
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"""Accept 80 or 0.8; return a fraction between 0 and 1."""
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return value / 100.0 if value > 1 else value
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def _to_initiative(row: dict) -> Initiative:
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try:
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return Initiative(
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name=str(row["name"]).strip(),
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reach=float(row["reach"]),
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impact=float(row["impact"]),
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confidence=_normalise_confidence(float(row["confidence"])),
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effort=float(row["effort"]),
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)
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except KeyError as exc:
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raise ValueError(f"Missing required field {exc} in row: {row}") from None
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def load(text: str, fmt: str) -> list[Initiative]:
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if fmt == "csv":
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rows = list(csv.DictReader(io.StringIO(text)))
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else:
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rows = json.loads(text)
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if not isinstance(rows, list):
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raise ValueError("Input must be a list of initiatives.")
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return [_to_initiative(r) for r in rows]
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def rank(initiatives: list[Initiative]) -> list[dict]:
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scored = []
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for i in initiatives:
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scored.append({
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"name": i.name,
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"reach": i.reach,
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"impact": i.impact,
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"confidence": round(i.confidence, 2),
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"effort": i.effort,
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"rice_score": round(i.score, 1),
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})
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scored.sort(key=lambda d: d["rice_score"], reverse=True)
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if scored:
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max_score = max(d["rice_score"] for d in scored) or 1
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max_effort = max(d["effort"] for d in scored) or 1
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for rank_index, d in enumerate(scored, start=1):
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d["rank"] = rank_index
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flags = []
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# Quick win: strong score relative to the field, low relative effort.
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if d["rice_score"] >= 0.5 * max_score and d["effort"] <= 0.33 * max_effort:
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flags.append("quick-win")
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# Moonshot: high raw impact, high relative effort.
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if d["impact"] >= 2 and d["effort"] >= 0.66 * max_effort:
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flags.append("moonshot")
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# Low-confidence estimates should be revisited before acting.
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if d["confidence"] <= 0.5:
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flags.append("low-confidence")
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d["flags"] = flags
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return scored
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def _render(scored: list[dict]) -> str:
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header = f"{'#':>2} {'Initiative':<32} {'Reach':>8} {'Imp':>4} {'Conf':>5} {'Eff':>5} {'RICE':>8} Flags"
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lines = ["RICE Prioritisation", "=" * len(header), header, "-" * len(header)]
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for d in scored:
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lines.append(
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f"{d['rank']:>2} {d['name'][:32]:<32} {d['reach']:>8g} {d['impact']:>4g} "
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f"{d['confidence']:>5.2f} {d['effort']:>5g} {d['rice_score']:>8g} {', '.join(d['flags'])}"
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)
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quick = [d["name"] for d in scored if "quick-win" in d["flags"]]
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moon = [d["name"] for d in scored if "moonshot" in d["flags"]]
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lowc = [d["name"] for d in scored if "low-confidence" in d["flags"]]
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lines.append("")
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lines.append(f"Quick wins (do alongside bigger bets): {', '.join(quick) or 'none'}")
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lines.append(f"Moonshots (high impact, high effort): {', '.join(moon) or 'none'}")
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lines.append(f"Low confidence — revisit estimates: {', '.join(lowc) or 'none'}")
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return "\n".join(lines)
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def main(argv: list[str] | None = None) -> int:
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parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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parser.add_argument("input", help="Path to a JSON/CSV file of initiatives, or '-' for stdin.")
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parser.add_argument("--format", choices=["json", "csv"], help="Input format (inferred from extension if omitted).")
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parser.add_argument("--json", action="store_true", dest="as_json", help="Emit ranked JSON instead of a table.")
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args = parser.parse_args(argv)
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text = sys.stdin.read() if args.input == "-" else None
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fmt = args.format
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if text is None:
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try:
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text = open(args.input).read()
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except OSError as exc:
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print(f"Error: {exc}", file=sys.stderr)
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return 1
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if fmt is None:
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fmt = "csv" if args.input.lower().endswith(".csv") else "json"
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fmt = fmt or "json"
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try:
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scored = rank(load(text, fmt))
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except (ValueError, json.JSONDecodeError) as exc:
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print(f"Error: {exc}", file=sys.stderr)
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return 1
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print(json.dumps(scored, indent=2) if args.as_json else _render(scored))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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