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/mshadmanrahman/pm-pilot/prdnpx skills add mshadmanrahman/pm-pilot --skill prdgit clone --depth 1 https://github.com/mshadmanrahman/pm-pilotWrote 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/mshadmanrahman/pm-pilot/prd)<a href="https://agentmods.dev/skills/mshadmanrahman/pm-pilot/prd"><img src="https://agentmods.dev/badge/skills/mshadmanrahman/pm-pilot/prd.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00073 | $0.01782 |
| Opus 5 | $0.00036 | $0.00891 |
| Sonnet 5 | $0.00015 | $0.00356 |
| Haiku 4.5 | $0.00007 | $0.00178 |
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 5d 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD: Product Requirements Document
Generate product specs at the right depth for the decision stage. From a quick one-pager to a full PRD to a technical RFC.
When to Activate
- User says "write a PRD", "product spec", "one-pager for X", "RFC for X"
- User asks for requirements, a spec, or a brief for a feature
- User says "spec this out" or "document the requirements"
Depth Modes
Auto-detect from user phrasing. If ambiguous, ask.
| Mode | When | Length | Contains |
|---|---|---|---|
| One-pager | Early exploration, exec alignment | 1 page | Problem, hypothesis, success criteria, key risks |
| Brief | Ready to start design/discovery | 2-3 pages | Above + user stories, scope (in/out), dependencies |
| Full PRD | Ready to build | 4-8 pages | Above + detailed requirements, edge cases, metrics, rollout plan, open questions |
| RFC | Technical decision needed | 2-5 pages | Context, proposal, alternatives considered, tradeoffs, migration plan |
Detection heuristics
- "quick spec", "one-pager", "executive summary" → One-pager
- "brief", "enough to start building", "design brief" → Brief
- "PRD", "full spec", "detailed requirements" → Full PRD
- "RFC", "tech spec", "architecture decision", "should we migrate" → RFC
Process
Step 1: Gather Context
Before writing, check for existing context:
- Check for market-research output: If competitive analysis exists for this domain, reference it
- Check memory: Look for project context, stakeholder preferences, prior decisions
- Ask the user (if context is thin):
- What problem does this solve?
- Who is it for?
- What does success look like?
- What constraints exist (timeline, team, tech)?
Do not block on missing context. Use what exists, flag what is assumed.
Step 2: Draft
Write the document at the detected depth level, following the templates below.
Tag uncertain claims:
[Assumption]for things you inferred but the user didn't confirm[Needs data]for metrics or claims that need validation[Open question]for decisions that need stakeholder input
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.
- 5d ago First seen · 268 lines · 73 tokens per session scan A 948bd4ad2cc6
prd is a skill published in the GitHub repository mshadmanrahman/pm-pilot (19 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 1,782 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.
Other skills, from other repositories
work-pipeline
Triggers the WORK-PIPELINE when a user request starts with a [] tag (e.g., [new-feature], [bugfix], [WORK start]). Use this skill whenever you detect a [] tag at the beginning of a user message.
feature-spec
Creates a complete product feature specification with acceptance criteria, scope, dependencies, and risks. Delegates to the Prometeo (PM) agent.
sdd-pipeline
파이프라인 에이전트(specifier, planner, scheduler, builder, verifier, committer)가 참조하는 규칙/스키마 문서 모음.
gmgn-dev-score
Decide whether a token creator's NEXT launch is safe to buy. Scores a dev address 0-100 on two separate axes — CONDUCT (will he dump on you at open) and POWER (has he ever actually built anything big) — from his full launch history and every trade he made in his own coins, then returns a buy / don't-buy call with a…
decided-import
Reformat ONE existing document (a decision, requirement, design, roadmap, or prompt) into ONE valid RAC (requirements-as-code) artifact, with a mandatory human-review step before any file is written and decided validate as the deterministic close. Use when a user wants to add or import a single existing decision or…
orch
AI agent orchestrator — manage teams of AI agents that work on your codebase in parallel. Use when the user wants to: run multiple agents, coordinate AI work, deploy agent teams, manage tasks/goals/agents, check orchestrator status, or mentions 'orch', 'orchestry', 'agents team', 'agent orchestration'.