Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/fagnerjs/ralphi/prdnpx skills add fagnerjs/ralphi --skill prdgit clone --depth 1 https://github.com/fagnerjs/ralphiWhat 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 | $0.00068 | $0.01750 |
| Opus 5 | $0.00034 | $0.00875 |
| Sonnet 5 | $0.00014 | $0.00350 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
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 2d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD Writer
Produce Product Requirements Documents that are specific, implementation-ready, and easy for humans or agents to follow.
Mission
Your responsibilities are:
- Take the user's feature idea or project brief
- Ask 3-5 important clarifying questions with lettered answer choices
- Turn the clarified input into a structured PRD
- Save the result to
tasks/prd-[feature-name].md
Do not implement the feature. The only deliverable is the PRD.
Clarify Before Writing
Ask follow-up questions only when the initial request leaves meaningful gaps. Prioritize questions that clarify:
- Outcome: what improvement or result the feature should create
- Core workflow: what the user must actually be able to do
- Scope limits: what is intentionally excluded from this effort
- Success definition: what must be true for the work to count as done
Question Format
Use a format like this:
1. Which outcome matters most for this feature?
A. Faster onboarding
B. Better retention
C. Lower support volume
D. Other: [please specify]
2. Who should this serve first?
A. New users only
B. Existing users only
C. All users
D. Internal admins only
3. How broad should the first release be?
A. Smallest useful version
B. Full first release
C. Backend/API only
D. UI only
This allows the user to respond quickly with something like 1B, 2C, 3A. Keep the answer choices indented beneath each question.
PRD Layout
Create the PRD with these sections.
1. Overview
Summarize the feature, the problem it addresses, and why the work matters.
2. Goals
List concrete outcomes the feature is expected to achieve.
3. User Stories
Each story should include:
- Title: brief descriptive label
- Description:
As a [user], I want [capability] so that [benefit] - Acceptance Criteria: a checklist of observable, verifiable outcomes
Keep each story small enough to fit into one focused implementation session.
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.
- 2d ago First seen · 249 lines · 68 tokens per session scan A 26047cb20d95
prd is a skill published in the GitHub repository fagnerjs/ralphi (6 stars, last pushed 5mo ago), licensed MIT. It adds 68 tokens to every session and 1,750 once invoked, about $0.0003 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 2026-08-31.
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