SkillCheck validator, Cursor exports, and per-agent installers (#27)

Three more learnings from alirezarezvani/claude-skills, applied:

1. SkillCheck validator (scripts/skillcheck.mjs) — validates every SKILL.md
   against the authoring standard (frontmatter, name/folder match, trigger +
   produces clauses, required headings) plus tier referential integrity.
   Errors fail CI; --strict fails on warnings too. New skillcheck.yml workflow
   and a SkillCheck status badge in the README. Current: 0 errors / 14 advisory
   warnings across 172 skills.

2. Cursor export platform — build-exports.mjs now generates
   exports/cursor/<bundle>/<skill>/<skill>.mdc rule files. The PLATFORMS
   registry now supports per-skill filenames (file as a function).

3. Per-agent installers — scripts/install.sh unifies install for
   claude/hermes/codex/openclaw/cursor (--link, --target, --dry-run, --list).
   Curl-able one-liners codex-install.sh, openclaw-install.sh, and
   cursor-install.sh clone the library and install in a single command.

README documents the one-line installs and Cursor exports; CHANGELOG and the
authoring standard updated.


Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px

Co-authored-by: Claude <noreply@anthropic.com>
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---
description: "Apply prioritisation frameworks (RICE, MoSCoW, Kano, ICE, Opportunity Scoring) to rank features and backlog items. Use when asked to prioritise features, rank a backlog, decide what to build next, or evaluate tradeoffs between competing ideas. Produces a scored, ranked feature list with framework-specific tables, recommended build order, deprioritised items, and assumptions made."
globs:
alwaysApply: false
---
# Feature Prioritisation Skill
Apply the right prioritisation framework to any backlog and produce a clear, defensible ranking with rationale — not just a sorted list.
## Required Inputs
Ask the user for these if not provided:
- **List of features or initiatives to prioritise**
- **Goal or metric** being prioritised against (OKR, launch, sprint)
- **Preferred framework** (or recommend based on context below)
- **Team data**: reach estimates, effort estimates, velocity (for RICE)
## Framework Selection Guide
Ask the user which framework they prefer, or recommend based on context:
| Situation | Recommended Framework |
|---|---|
| Need a quick, data-driven score | RICE |
| Stakeholder alignment meeting | MoSCoW |
| Understanding customer delight vs expectations | Kano |
| Early-stage startup, fast decisions | ICE |
| Identifying underserved customer needs | Opportunity Scoring |
| Strategic portfolio decisions | Value vs Effort Matrix |
---
## RICE Scoring
**Formula:** (Reach × Impact × Confidence) ÷ Effort
| Factor | Definition | Scale |
|---|---|---|
| Reach | Users impacted per quarter | Actual number |
| Impact | Effect on goal per user | 0.25 / 0.5 / 1 / 2 / 3 |
| Confidence | How certain are you? | 50% / 80% / 100% |
| Effort | Person-months required | Actual number |
Output table:
| Feature | Reach | Impact | Confidence | Effort | RICE Score | Priority |
|---|---|---|---|---|---|---|
---
## MoSCoW Method
Categorise each feature as:
- **Must Have** — non-negotiable for launch/sprint; product fails without it
- **Should Have** — important but not critical; workarounds exist
- **Could Have** — nice to have; include only if time allows
- **Won't Have (this time)** — explicitly out of scope now; may revisit
Always ask: "Must have for *what*?" — define the scope (launch, sprint, quarter) before categorising.
---
## ICE Scoring (Startup/fast mode)
**Formula:** Impact + Confidence + Ease (each 110)
Quick, subjective — good for early decisions before data exists.
---
## Kano Model
Classify features into:
- **Basic (Must-be):** Expected; absence causes dissatisfaction
- **Performance:** More = better satisfaction; linear relationship
- **Excitement (Delighters):** Unexpected; creates delight; absence is neutral
- **Indifferent:** Users don't care either way
- **Reverse:** Some users want it, others don't
Recommend building: all Basic features first → Performance features for key use cases → 12 Excitement features per release.
---
## Output Format
### Feature Prioritisation — [Product/Team] — [Date]
**Framework Used:** [RICE / MoSCoW / ICE / Kano / Custom]
**Scope:** [Sprint / Quarter / Release]
**Goal being prioritised against:** [Metric or objective]
[Scored table using selected framework]
**Recommended Build Order:**
1. [Feature] — [1-line rationale]
2. [Feature] — [1-line rationale]
3. ...
**Explicitly Deprioritised:**
- [Feature] — Reason: [brief]
**Assumptions Made:**
- [Any estimates or judgements used in scoring]
---
## Guidelines
- Always anchor prioritisation to a specific goal or metric — never prioritise in a vacuum
- Flag when two features have similar scores but very different risk profiles
- If stakeholder politics are influencing prioritisation, name it explicitly and suggest separating the framework score from the final decision
- Recommend revisiting priorities every 2 weeks minimum
- Never produce a single-column ranked list without rationale — explain the top 3 and bottom 3 decisions
## Quality Checks
- [ ] Every item is scored against the same goal or metric (not different goals per item)
- [ ] Deprioritised items are explicitly listed with reasons (not just absent from the ranked list)
- [ ] Assumptions used in scoring are documented
- [ ] Stakeholder politics or personal preferences are separated from framework score
- [ ] Prioritisation is anchored to a specific scope (sprint / quarter / launch)
## Anti-Patterns
- [ ] Do not score items against different goals — every item in a prioritisation session must be scored against the same objective
- [ ] Do not omit deprioritised items — explicitly listing what was cut and why is as important as the ranked list
- [ ] Do not let stakeholder politics override framework scores without documenting the override and reason
- [ ] Do not mix RICE, ICE, or MoSCoW scores across frameworks in a single session — pick one framework per prioritisation exercise
- [ ] Do not treat the output as final without documenting the assumptions used in scoring — assumptions change, and the list must be revisitable