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 commands/el-feo/ai-context/create-prdgit clone --depth 1 https://github.com/el-feo/ai-contextWhat 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 | $0.00025 | $0.04494 |
| Opus 5 | $0.00013 | $0.02247 |
| Sonnet 5 | $0.00005 | $0.00899 |
| Haiku 4.5 | $0.00003 | $0.00449 |
Grade A, and why
create-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 — 540 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optional environment variables:
GHPM_PROJECT- GitHub Project name to associate issue with. If not set, the command will query available projects for the repository owner and prompt for selection.
<usage_examples>
Detailed input (skips clarification):
/ghpm:create-prd Build a user authentication system with email/password and OAuth support for enterprise customers who need SSO to reduce IT friction during onboarding
→ Detailed input (30+ words, has who/what/why) → Proceeds directly to PRD generation
Vague input (triggers clarification):
/ghpm:create-prd Add a dashboard
→ Vague input (4 words, missing who/why/scope) → Presents clarifying questions:
- Who is the primary user? (Internal team, Customers, Admins, Developers)
- What problem does this solve? (Efficiency, Missing capability, UX, Compliance)
- What's the scope? (MVP, Feature complete, Production-ready, Enterprise-grade)
After user responds → Generates PRD with enriched context
Complex feature (typically detailed enough):
/ghpm:create-prd Add real-time collaboration features to the document editor, similar to Google Docs, so remote teams can co-edit documents without version conflicts
→ Detailed input → Proceeds directly to PRD generation
With project association (auto-prompt):
/ghpm:create-prd Implement dark mode across the application for users with visual sensitivities to reduce eye strain
→ If GHPM_PROJECT not set, prompts: "Which GitHub Project should this PRD be added to?" with available projects
With project pre-set (skip prompt):
export GHPM_PROJECT="MyOrg/Q1 Roadmap"
/ghpm:create-prd Implement dark mode across the application for users with visual sensitivities to reduce eye strain
→ Skips project selection prompt and uses pre-set project
</usage_examples>
<operating_rules>
- For vague input: Use
AskUserQuestiontool to gather context before generating the PRD. See<vagueness_detection>for criteria. - For detailed input: Proceed directly to PRD generation. Make reasonable assumptions and explicitly record them under Assumptions and Open Questions.
- Do not create or persist local markdown artifacts (no local PRD files). All artifacts must live in GitHub issue bodies/comments.
- Use Markdown in the issue body. Make the PRD self-contained.
- Keep scope crisp; if the request is broad, define a "V1" and park the rest in Out of Scope / Future Ideas.
- Clarification should be quick (max 4 questions) - do not interrogate the user. </operating_rules>
<prd_structure>
Required PRD Structure (Issue Body)
Use this exact outline:
# PRD: <Concise Name>
## Summary
## Problem / Opportunity
## Goals (Success Metrics)
## Non-Goals / Out of Scope
## Users & Use Cases
## Requirements
- Functional Requirements
- Non-Functional Requirements
## UX / UI Notes (if relevant)
## Data / Integrations (if relevant)
## Risks / Edge Cases
## Assumptions
## Open Questions
## Acceptance Criteria (high level)
## Rollout / Release Notes (brief)
## Implementation Notes (non-binding)
(Keep this section minimal; do not over-prescribe.)
</prd_structure>
<input_validation>
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 · 540 lines · 25 tokens per session scan A 8a63c6246a31
create-prd is a command published in the GitHub repository el-feo/ai-context (12 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 4,494 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.