How do I install the Y Score Readiness skill?

Run npx skillmds@latest add vignesh-s7/y-score-readiness in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Y Score Readiness safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Y Score Readiness?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Y Score Readiness free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Y Score Readiness?

vignesh-s7 (@vignesh-s7) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Y Score Readiness by vignesh-s7 — Agent Skill

Name: Y Score Readiness
Author: vignesh-s7

Y Score Readiness

Score a PRD or pilot against the 7-dimension Y-Score launch-readiness rubric (problem clarity, target user, success metric, constraints, compliance, eval plan, rollback). Returns a 0-100 score plus per-dimension findings and a go / no-go recommendation. Use when a PO is deciding whether a pilot is ready to launch, especially in BFSI / healthcare / regulated domains.

vignesh-s7 Updated Sep 17, 2026

File contents

y-score-readiness

The PO gate that runs before prd-to-sdlc produces any artifacts. Refuses to proceed if score < 70.

When to trigger

/y-score <prd.md>
User says "is this ready to launch?", "readiness check", "score this PRD"
Auto-invoked by prd-to-sdlc orchestrator stage 1

The 7 dimensions

#	Dimension	Weight	Anchor for full marks
1	Problem clarity	15	One-sentence statement, measurable, user-visible
2	Target user	10	Named persona + JTBD + frequency
3	Success metric	20	Leading + lagging metric with baseline + target
4	Constraints	10	Latency / cost / data residency / SLA defined
5	Compliance posture	15	Domain checklist done (HIPAA / PCI / GDPR / EU-AI-Act)
6	Eval plan	20	Golden set + judge + regression suite + drift monitor
7	Rollback plan	10	Kill switch + comms + data-recovery RTO defined

Scoring

Per dimension: 0 (missing) / 50 (partial) / 100 (full)
Weighted total = sum(dim_score × weight) / 100
Gate: total ≥ 70 → proceed. < 70 → return findings, block downstream stages.

Output format

{
  "score": 78,
  "verdict": "go",
  "dimensions": {
    "problem_clarity": {"score": 100, "note": "..."},
    "target_user":     {"score": 50,  "note": "Persona named but JTBD missing"},
    ...
  },
  "blockers": [],
  "recommendations": ["Add LLM-as-judge calibration step to eval plan"]
}

Domain-aware compliance dimension

Compliance dimension auto-checks against domain:

bfsi → PCI-DSS, FATF, BSA, fair-lending, EU-AI-Act high-risk
healthcare → HIPAA, FHIR consent, FDA SaMD class (if applicable)
eu → GDPR DPIA, EU-AI-Act risk tier
generic → baseline SOC 2 + privacy review

Author

Vignesh AIPM — distilled from 10 years shipping AI in regulated BFSI / healthcare environments.

---name: y-score-readinessdescription: Score a PRD or pilot against the 7-dimension Y-Score launch-readiness rubric (problem clarity, target user, success metric, constraints, compliance, eval plan, rollback). Returns a 0-100 score plus per-dimension findings and a go / no-go recommendation. Use when a PO is deciding whether a pilot is ready to launch, especially in BFSI / healthcare / regulated domains.---# y-score-readinessThe PO gate that runs before `prd-to-sdlc` produces any artifacts. Refuses to proceed if score < 70.## When to trigger- `/y-score <prd.md>`- User says "is this ready to launch?", "readiness check", "score this PRD"- Auto-invoked by `prd-to-sdlc` orchestrator stage 1## The 7 dimensions| # | Dimension | Weight | Anchor for full marks ||---|---|---|---|| 1 | Problem clarity | 15 | One-sentence statement, measurable, user-visible || 2 | Target user | 10 | Named persona + JTBD + frequency || 3 | Success metric | 20 | Leading + lagging metric with baseline + target || 4 | Constraints | 10 | Latency / cost / data residency / SLA defined || 5 | Compliance posture | 15 | Domain checklist done (HIPAA / PCI / GDPR / EU-AI-Act) || 6 | Eval plan | 20 | Golden set + judge + regression suite + drift monitor || 7 | Rollback plan | 10 | Kill switch + comms + data-recovery RTO defined |## Scoring- Per dimension: 0 (missing) / 50 (partial) / 100 (full)- Weighted total = sum(dim_score × weight) / 100- **Gate:** total ≥ 70 → proceed. < 70 → return findings, block downstream stages.## Output format```json{ "score": 78, "verdict": "go", "dimensions": { "problem_clarity": {"score": 100, "note": "..."}, "target_user": {"score": 50, "note": "Persona named but JTBD missing"}, ... }, "blockers": [], "recommendations": ["Add LLM-as-judge calibration step to eval plan"]}```## Domain-aware compliance dimensionCompliance dimension auto-checks against domain:- `bfsi` → PCI-DSS, FATF, BSA, fair-lending, EU-AI-Act high-risk- `healthcare` → HIPAA, FHIR consent, FDA SaMD class (if applicable)- `eu` → GDPR DPIA, EU-AI-Act risk tier- `generic` → baseline SOC 2 + privacy review## AuthorVignesh AIPM — distilled from 10 years shipping AI in regulated BFSI / healthcare environments.

Frequently asked questions