Borrowing it
Nothing to install: this file belongs to doncheli/don-cheli-sdd. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/doncheli/don-cheli-sdd/main/.agent/skills/doncheli-prd/SKILL.mdgit clone --depth 1 https://github.com/doncheli/don-cheli-sddWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/doncheli/don-cheli-sdd/doncheli-prd)<a href="https://agentmods.dev/skills/doncheli/don-cheli-sdd/doncheli-prd"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-prd/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/doncheli/don-cheli-sdd/doncheli-prd"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-prd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.01155 |
| Opus 5 | $0.00041 | $0.00577 |
| Sonnet 5 | $0.00016 | $0.00231 |
| Haiku 4.5 | $0.00008 | $0.00115 |
Grade A, and why
doncheli-prd scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Don Cheli: PRD Generator
Instructions
-
Discover sources — Identify all available inputs:
- Figma links: analyze screens, flows, components, missing states
- Brief documents: extract objectives, audience, problems
- User research: identify personas, JTBD, pain points
- Existing code: detect technical constraints
- Conversation context: extract requirements mentioned by the user
-
Analyze Figma (if provided):
- Count screens/frames and map navigation flow
- Extract texts, labels, form fields, interactions
- Detect missing UI states: loading, empty, error, success, offline
- Identify gaps: flows not designed, edge cases missing
- Convert each screen → user stories → acceptance criteria
-
Risk analysis — For each feature, evaluate 7 risk categories:
- Technical (complexity, integrations, tech debt)
- Product (market fit, adoption, competition)
- UX (usability, accessibility, learning curve)
- Business (revenue, cost, time-to-market)
- Legal (GDPR, compliance, ToS)
- Security (OWASP, sensitive data, auth)
- Operational (support, scalability, monitoring)
- Assign probability (Low/Medium/High) and impact (Low/Medium/High/Critical)
- Propose mitigation for each risk
-
Prioritize features using MoSCoW + RICE:
- MoSCoW: Must / Should / Could / Won't
- RICE: (Reach × Impact × Confidence) / Effort
- Rank features by RICE score
-
Generate PRD with 13 sections:
- Executive Summary
- Problem (context, pain points, evidence)
- Objectives & Success Metrics (KPIs, North Star)
- Target Audience (personas, JTBD, user journey)
- Proposed Solution (overview, main flow, features by priority)
- Detailed Requirements (functional with Gherkin, non-functional, integrations, data model)
- Design & UX (Figma analysis, UI states, responsive, accessibility)
- Risk Analysis (matrix, mitigations, dependencies)
- Scope (in scope, out of scope, future iterations)
- Timeline & Milestones
- Competitive Analysis
- Launch Plan (rollout, monitoring, rollback)
- Appendices (glossary, references, change history)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 118 lines · 81 tokens per session scan A 271778034e35
doncheli-prd is a skill published in the GitHub repository doncheli/don-cheli-sdd (57 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,155 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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