Reapplies @zeotrix's PR #48 onto current main: - adds a dependency-free Python script computing RICE/ICE rankings so scoring is consistent across sessions (skills/ + plugins/ copies, kept identical) - documents it in a 'Programmatic Helper' section in both SKILL.md files - regenerates the platform exports so the check-generated CI stays green Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: zeotrix <zeotrix@users.noreply.github.com>
This commit is contained in:
mohitagw15856
GitHub
@@ -75,6 +75,29 @@ Recommend building: all Basic features first → Performance features for key us
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---
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## Programmatic Helper
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This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.
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```bash
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# RICE from JSON
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python3 scripts/feature_prioritisation.py initiatives.json --framework rice
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# RICE from CSV
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python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv
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# ICE from JSON
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python3 scripts/feature_prioritisation.py features.json --framework ice
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# Pipe into it
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 scripts/feature_prioritisation.py --framework ice -
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```
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Use `--json` to produce machine-readable output for downstream tooling.
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---
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## Output Format
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### Feature Prioritisation — [Product/Team] — [Date]
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@@ -75,6 +75,29 @@ Recommend building: all Basic features first → Performance features for key us
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---
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## Programmatic Helper
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This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.
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```bash
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# RICE from JSON
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python3 scripts/feature_prioritisation.py initiatives.json --framework rice
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# RICE from CSV
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python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv
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# ICE from JSON
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python3 scripts/feature_prioritisation.py features.json --framework ice
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# Pipe into it
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 scripts/feature_prioritisation.py --framework ice -
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```
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Use `--json` to produce machine-readable output for downstream tooling.
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---
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## Output Format
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### Feature Prioritisation — [Product/Team] — [Date]
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@@ -81,6 +81,29 @@ Recommend building: all Basic features first → Performance features for key us
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---
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## Programmatic Helper
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This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.
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```bash
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# RICE from JSON
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python3 scripts/feature_prioritisation.py initiatives.json --framework rice
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# RICE from CSV
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python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv
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# ICE from JSON
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python3 scripts/feature_prioritisation.py features.json --framework ice
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# Pipe into it
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 scripts/feature_prioritisation.py --framework ice -
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```
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Use `--json` to produce machine-readable output for downstream tooling.
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---
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## Output Format
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### Feature Prioritisation — [Product/Team] — [Date]
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@@ -79,6 +79,29 @@ Recommend building: all Basic features first → Performance features for key us
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---
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## Programmatic Helper
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This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.
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```bash
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# RICE from JSON
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python3 scripts/feature_prioritisation.py initiatives.json --framework rice
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# RICE from CSV
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python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv
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# ICE from JSON
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python3 scripts/feature_prioritisation.py features.json --framework ice
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# Pipe into it
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 scripts/feature_prioritisation.py --framework ice -
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```
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Use `--json` to produce machine-readable output for downstream tooling.
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---
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## Output Format
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### Feature Prioritisation — [Product/Team] — [Date]
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@@ -80,6 +80,29 @@ Recommend building: all Basic features first → Performance features for key us
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---
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## Programmatic Helper
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This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.
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```bash
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# RICE from JSON
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python3 scripts/feature_prioritisation.py initiatives.json --framework rice
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# RICE from CSV
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python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv
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# ICE from JSON
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python3 scripts/feature_prioritisation.py features.json --framework ice
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# Pipe into it
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 scripts/feature_prioritisation.py --framework ice -
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```
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Use `--json` to produce machine-readable output for downstream tooling.
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---
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## Output Format
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### Feature Prioritisation — [Product/Team] — [Date]
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@@ -80,6 +80,29 @@ Recommend building: all Basic features first → Performance features for key us
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---
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## Programmatic Helper
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This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.
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```bash
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# RICE from JSON
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python3 scripts/feature_prioritisation.py initiatives.json --framework rice
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# RICE from CSV
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python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv
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# ICE from JSON
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python3 scripts/feature_prioritisation.py features.json --framework ice
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# Pipe into it
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 scripts/feature_prioritisation.py --framework ice -
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```
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Use `--json` to produce machine-readable output for downstream tooling.
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---
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## Output Format
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### Feature Prioritisation — [Product/Team] — [Date]
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@@ -0,0 +1,193 @@
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#!/usr/bin/env python3
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"""Feature prioritisation helper for the feature-prioritisation skill.
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Computes ranking for common scoring frameworks so the same formulas and ordering
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are applied consistently. Supports RICE and ICE with JSON input.
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Input formats:
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- JSON list (default): each item includes `name` and framework-specific fields.
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- CSV: header-driven input when using --format csv.
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RICE fields:
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name,reach,impact,confidence,effort
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ICE fields:
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name,impact,confidence,ease
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Examples:
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python3 feature_prioritisation.py --framework rice initiatives.json
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python3 feature_prioritisation.py initiatives.csv --framework rice --format csv
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 feature_prioritisation.py --framework ice -
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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 Feature:
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name: str
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scores: dict[str, float]
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def rice_score(self) -> float:
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return (self.scores["reach"] * self.scores["impact"] * self.scores["confidence"]) / self.scores["effort"]
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def ice_score(self) -> float:
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return self.scores["impact"] + self.scores["confidence"] + self.scores["ease"]
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def _normalise_confidence(value: float, framework: str) -> float:
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"""Normalize confidence depending on framework conventions."""
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if framework == "rice":
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return value / 100.0 if value > 1 else value
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# ICE uses a 1-10 convention in this skill; accept 0-1 and 1-10, 80/100 as percent fallback.
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if value > 1:
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if value > 10:
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return value / 10.0
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return value
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return value
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def _to_feature(name: str, values: dict[str, object], framework: str) -> Feature:
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try:
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if framework == "rice":
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reach = float(values["reach"])
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effort = float(values["effort"])
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if effort <= 0:
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raise ValueError("effort must be greater than 0")
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return Feature(
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name=name,
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scores={
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"reach": reach,
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"impact": float(values["impact"]),
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"confidence": _normalise_confidence(float(values["confidence"]), "rice"),
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"effort": effort,
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},
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)
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# ICE
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return Feature(
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name=name,
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scores={
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"impact": float(values["impact"]),
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"confidence": _normalise_confidence(float(values["confidence"]), "ice"),
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"ease": float(values["ease"]),
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},
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)
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except KeyError as exc:
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raise ValueError(f"Missing required field {exc} in feature '{name}'.") from None
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def load_rice_json(rows: list[dict[str, object]]) -> list[Feature]:
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return [_to_feature(str(row["name"]).strip(), row, "rice") for row in rows]
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def load_ice_json(rows: list[dict[str, object]]) -> list[Feature]:
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return [_to_feature(str(row["name"]).strip(), row, "ice") for row in rows]
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def _load_csv(text: str, framework: str) -> list[dict[str, str]]:
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rows = list(csv.DictReader(io.StringIO(text)))
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if not rows:
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return []
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expected = {"rice": {"name", "reach", "impact", "confidence", "effort"},
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"ice": {"name", "impact", "confidence", "ease"}}
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present = set(rows[0].keys())
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missing = expected[framework] - present
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if missing:
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raise ValueError(f"CSV format missing required columns: {', '.join(sorted(missing))}")
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return rows
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def load(text: str, fmt: str, framework: str) -> list[Feature]:
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if fmt == "csv":
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rows = _load_csv(text, framework)
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if framework == "rice":
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return load_rice_json(rows)
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return load_ice_json(rows)
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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 feature objects.")
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if framework == "rice":
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return load_rice_json(rows)
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return load_ice_json(rows)
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def rank(features: list[Feature], framework: str) -> list[dict]:
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scored = []
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for feature in features:
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score = feature.rice_score() if framework == "rice" else feature.ice_score()
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row = {"name": feature.name, "score": round(float(score), 2)}
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row.update({k: v for k, v in feature.scores.items() if k != "score"})
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scored.append(row)
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scored.sort(key=lambda d: d["score"], reverse=True)
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for index, row in enumerate(scored, start=1):
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row["rank"] = index
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return scored
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def _render(ranked: list[dict], framework: str) -> str:
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if framework == "rice":
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header = f"{'#':>2} {'Feature':<30} {'Reach':>10} {'Impact':>7} {'Conf':>7} {'Effort':>7} {'RICE':>8}"
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lines = ["Feature Prioritisation (RICE)", "=" * len(header), header, "-" * len(header)]
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for row in ranked:
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lines.append(
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f"{row['rank']:>2} {row['name'][:30]:<30} "
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f"{row['reach']:>10g} {row['impact']:>7g} {row['confidence']:>6.2f} {row['effort']:>7g} {row['score']:>8g}"
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)
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return "\n".join(lines)
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header = f"{'#':>2} {'Feature':<30} {'Impact':>7} {'Conf':>7} {'Ease':>7} {'ICE':>8}"
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lines = ["Feature Prioritisation (ICE)", "=" * len(header), header, "-" * len(header)]
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for row in ranked:
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lines.append(
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f"{row['rank']:>2} {row['name'][:30]:<30} "
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f"{row['impact']:>7g} {row['confidence']:>6.2f} {row['ease']:>7g} {row['score']:>8g}"
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)
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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 input JSON/CSV file, or '-' for stdin.")
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parser.add_argument("--framework", choices=["rice", "ice"], default="rice",
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help="Scoring framework to use.")
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parser.add_argument("--format", choices=["json", "csv"], help="Input format (inferred from extension when 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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if args.input == "-":
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text = sys.stdin.read()
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fmt = args.format or "json"
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else:
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try:
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with open(args.input, "r", encoding="utf-8") as f:
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text = f.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 args.format:
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fmt = args.format
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else:
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fmt = "csv" if args.input.lower().endswith(".csv") else "json"
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try:
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ranked = rank(load(text, fmt, args.framework), args.framework)
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except (ValueError, json.JSONDecodeError, KeyError) 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(ranked, indent=2) if args.as_json else _render(ranked, args.framework))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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@@ -80,6 +80,29 @@ Recommend building: all Basic features first → Performance features for key us
|
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|
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---
|
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|
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## Programmatic Helper
|
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|
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This skill ships with a stdlib-only Python script that computes ranking for the math-based frameworks (RICE, ICE) so feature scoring is consistent across sessions.
|
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|
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```bash
|
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# RICE from JSON
|
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python3 scripts/feature_prioritisation.py initiatives.json --framework rice
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|
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# RICE from CSV
|
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python3 scripts/feature_prioritisation.py initiatives.csv --framework rice --format csv
|
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|
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# ICE from JSON
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python3 scripts/feature_prioritisation.py features.json --framework ice
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|
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# Pipe into it
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
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| python3 scripts/feature_prioritisation.py --framework ice -
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```
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Use `--json` to produce machine-readable output for downstream tooling.
|
||||
|
||||
---
|
||||
|
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## Output Format
|
||||
|
||||
### Feature Prioritisation — [Product/Team] — [Date]
|
||||
|
||||
@@ -0,0 +1,193 @@
|
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#!/usr/bin/env python3
|
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"""Feature prioritisation helper for the feature-prioritisation skill.
|
||||
|
||||
Computes ranking for common scoring frameworks so the same formulas and ordering
|
||||
are applied consistently. Supports RICE and ICE with JSON input.
|
||||
|
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Input formats:
|
||||
- JSON list (default): each item includes `name` and framework-specific fields.
|
||||
- CSV: header-driven input when using --format csv.
|
||||
|
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RICE fields:
|
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name,reach,impact,confidence,effort
|
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|
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ICE fields:
|
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name,impact,confidence,ease
|
||||
|
||||
Examples:
|
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python3 feature_prioritisation.py --framework rice initiatives.json
|
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python3 feature_prioritisation.py initiatives.csv --framework rice --format csv
|
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printf '%s\n' '[{"name":"API refactor","impact":8,"confidence":80,"ease":5}]' \
|
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| python3 feature_prioritisation.py --framework ice -
|
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"""
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from __future__ import annotations
|
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|
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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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|
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|
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@dataclass
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class Feature:
|
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name: str
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scores: dict[str, float]
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|
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def rice_score(self) -> float:
|
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return (self.scores["reach"] * self.scores["impact"] * self.scores["confidence"]) / self.scores["effort"]
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|
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def ice_score(self) -> float:
|
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return self.scores["impact"] + self.scores["confidence"] + self.scores["ease"]
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|
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|
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def _normalise_confidence(value: float, framework: str) -> float:
|
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"""Normalize confidence depending on framework conventions."""
|
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if framework == "rice":
|
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return value / 100.0 if value > 1 else value
|
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# ICE uses a 1-10 convention in this skill; accept 0-1 and 1-10, 80/100 as percent fallback.
|
||||
if value > 1:
|
||||
if value > 10:
|
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return value / 10.0
|
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return value
|
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return value
|
||||
|
||||
|
||||
def _to_feature(name: str, values: dict[str, object], framework: str) -> Feature:
|
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try:
|
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if framework == "rice":
|
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reach = float(values["reach"])
|
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effort = float(values["effort"])
|
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if effort <= 0:
|
||||
raise ValueError("effort must be greater than 0")
|
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return Feature(
|
||||
name=name,
|
||||
scores={
|
||||
"reach": reach,
|
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"impact": float(values["impact"]),
|
||||
"confidence": _normalise_confidence(float(values["confidence"]), "rice"),
|
||||
"effort": effort,
|
||||
},
|
||||
)
|
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|
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# ICE
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||||
return Feature(
|
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name=name,
|
||||
scores={
|
||||
"impact": float(values["impact"]),
|
||||
"confidence": _normalise_confidence(float(values["confidence"]), "ice"),
|
||||
"ease": float(values["ease"]),
|
||||
},
|
||||
)
|
||||
except KeyError as exc:
|
||||
raise ValueError(f"Missing required field {exc} in feature '{name}'.") from None
|
||||
|
||||
|
||||
def load_rice_json(rows: list[dict[str, object]]) -> list[Feature]:
|
||||
return [_to_feature(str(row["name"]).strip(), row, "rice") for row in rows]
|
||||
|
||||
|
||||
def load_ice_json(rows: list[dict[str, object]]) -> list[Feature]:
|
||||
return [_to_feature(str(row["name"]).strip(), row, "ice") for row in rows]
|
||||
|
||||
|
||||
def _load_csv(text: str, framework: str) -> list[dict[str, str]]:
|
||||
rows = list(csv.DictReader(io.StringIO(text)))
|
||||
if not rows:
|
||||
return []
|
||||
expected = {"rice": {"name", "reach", "impact", "confidence", "effort"},
|
||||
"ice": {"name", "impact", "confidence", "ease"}}
|
||||
present = set(rows[0].keys())
|
||||
missing = expected[framework] - present
|
||||
if missing:
|
||||
raise ValueError(f"CSV format missing required columns: {', '.join(sorted(missing))}")
|
||||
return rows
|
||||
|
||||
|
||||
def load(text: str, fmt: str, framework: str) -> list[Feature]:
|
||||
if fmt == "csv":
|
||||
rows = _load_csv(text, framework)
|
||||
if framework == "rice":
|
||||
return load_rice_json(rows)
|
||||
return load_ice_json(rows)
|
||||
|
||||
rows = json.loads(text)
|
||||
if not isinstance(rows, list):
|
||||
raise ValueError("Input must be a list of feature objects.")
|
||||
if framework == "rice":
|
||||
return load_rice_json(rows)
|
||||
return load_ice_json(rows)
|
||||
|
||||
|
||||
def rank(features: list[Feature], framework: str) -> list[dict]:
|
||||
scored = []
|
||||
for feature in features:
|
||||
score = feature.rice_score() if framework == "rice" else feature.ice_score()
|
||||
row = {"name": feature.name, "score": round(float(score), 2)}
|
||||
row.update({k: v for k, v in feature.scores.items() if k != "score"})
|
||||
scored.append(row)
|
||||
|
||||
scored.sort(key=lambda d: d["score"], reverse=True)
|
||||
for index, row in enumerate(scored, start=1):
|
||||
row["rank"] = index
|
||||
return scored
|
||||
|
||||
|
||||
def _render(ranked: list[dict], framework: str) -> str:
|
||||
if framework == "rice":
|
||||
header = f"{'#':>2} {'Feature':<30} {'Reach':>10} {'Impact':>7} {'Conf':>7} {'Effort':>7} {'RICE':>8}"
|
||||
lines = ["Feature Prioritisation (RICE)", "=" * len(header), header, "-" * len(header)]
|
||||
for row in ranked:
|
||||
lines.append(
|
||||
f"{row['rank']:>2} {row['name'][:30]:<30} "
|
||||
f"{row['reach']:>10g} {row['impact']:>7g} {row['confidence']:>6.2f} {row['effort']:>7g} {row['score']:>8g}"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
header = f"{'#':>2} {'Feature':<30} {'Impact':>7} {'Conf':>7} {'Ease':>7} {'ICE':>8}"
|
||||
lines = ["Feature Prioritisation (ICE)", "=" * len(header), header, "-" * len(header)]
|
||||
for row in ranked:
|
||||
lines.append(
|
||||
f"{row['rank']:>2} {row['name'][:30]:<30} "
|
||||
f"{row['impact']:>7g} {row['confidence']:>6.2f} {row['ease']:>7g} {row['score']:>8g}"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def main(argv: list[str] | None = None) -> int:
|
||||
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
parser.add_argument("input", help="Path to input JSON/CSV file, or '-' for stdin.")
|
||||
parser.add_argument("--framework", choices=["rice", "ice"], default="rice",
|
||||
help="Scoring framework to use.")
|
||||
parser.add_argument("--format", choices=["json", "csv"], help="Input format (inferred from extension when omitted).")
|
||||
parser.add_argument("--json", action="store_true", dest="as_json", help="Emit ranked JSON instead of a table.")
|
||||
args = parser.parse_args(argv)
|
||||
|
||||
if args.input == "-":
|
||||
text = sys.stdin.read()
|
||||
fmt = args.format or "json"
|
||||
else:
|
||||
try:
|
||||
with open(args.input, "r", encoding="utf-8") as f:
|
||||
text = f.read()
|
||||
except OSError as exc:
|
||||
print(f"Error: {exc}", file=sys.stderr)
|
||||
return 1
|
||||
if args.format:
|
||||
fmt = args.format
|
||||
else:
|
||||
fmt = "csv" if args.input.lower().endswith(".csv") else "json"
|
||||
|
||||
try:
|
||||
ranked = rank(load(text, fmt, args.framework), args.framework)
|
||||
except (ValueError, json.JSONDecodeError, KeyError) as exc:
|
||||
print(f"Error: {exc}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
print(json.dumps(ranked, indent=2) if args.as_json else _render(ranked, args.framework))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in New Issue
Block a user