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 skills add smallnest/autoresearch --skill prdgit clone --depth 1 https://github.com/smallnest/autoresearchWrote 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/smallnest/autoresearch/prd)<a href="https://agentmods.dev/skills/smallnest/autoresearch/prd"><img src="https://agentmods.dev/badge/skills/smallnest/autoresearch/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/smallnest/autoresearch/prd"><img src="https://agentmods.dev/badge/skills/smallnest/autoresearch/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 | $0.00086 | $0.03585 |
| Opus 5.5 | $0.00034 | $0.01434 |
| Sonnet 5.5 | $0.00017 | $0.00717 |
| Haiku 4.5 | $0.00009 | $0.00359 |
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 1mo 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 — 425 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD Generator + Issue Decomposer
Create detailed Product Requirements Documents that are clear, actionable, and suitable for implementation. After PRD is confirmed, decompose it into small, independent Issues and create them in the user's chosen platform.
The Job
- Receive a feature description from the user
- Ask 3-5 essential clarifying questions (with lettered options)
- Generate a structured PRD based on answers
- Present PRD to user for review — ask "Please review the PRD. Let me know if any adjustments are needed, or reply OK to confirm."
- Apply any adjustments, then save to
tasks/prd-[feature-name].md - Decompose PRD into Issues — break each User Story into one or more independent, implementable Issues
- Ask user to choose Issue creation mode — GitHub / Local / Baidu iCafe
- Create Issues in the chosen platform
Important: Do NOT start implementing. Just create the PRD and Issues.
Step 1: Clarifying Questions
Ask only critical questions where the initial prompt is ambiguous. Focus on:
- Problem/Goal: What problem does this solve?
- Core Functionality: What are the key actions?
- Scope/Boundaries: What should it NOT do?
- Success Criteria: How do we know it's done?
Format Questions Like This:
1. What is the primary goal of this feature?
A. Improve user onboarding experience
B. Increase user retention
C. Reduce support burden
D. Other: [please specify]
2. Who is the target user?
A. New users only
B. Existing users only
C. All users
D. Admin users only
3. What is the scope?
A. Minimal viable version
B. Full-featured implementation
C. Just the backend/API
D. Just the UI
This lets users respond with "1A, 2C, 3B" for quick iteration. Remember to indent the options.
Edge Cases & Fallback
| Scenario | Handling |
|---|---|
| User skips clarifying questions (e.g., replies "whatever", "just write it") | Fill with reasonable defaults, mark with [Assumption] in PRD, prompt user to confirm during review |
| User input is too vague (e.g., "add a feature") | Ask once for specifics; if still vague, infer from project context and mark assumptions |
tasks/ directory does not exist |
Auto-create tasks/ directory |
| feature-name is hard to extract from input | Ask the user directly: "Suggested PRD filename is prd-XXX.md, please confirm or modify" |
| User requests PRD changes after review | Apply changes and re-save without re-running the clarification flow |
| PRD content exceeds 500 lines | Suggest the user consider splitting into multiple sub-feature PRDs |
| User declines Issue creation | Just save the PRD, skip Step 3 |
gh CLI not authenticated for GitHub mode |
Show error, suggest gh auth login, offer to switch to Local mode |
icafe-cli / icode-cli not installed for baidu mode |
Show error, suggest installation, offer to switch to Local mode |
| Issue folder does not exist for Local mode | Auto-create the folder |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 425 lines · 86 tokens per session scan A 7429ed12027d
prd is a skill published in the GitHub repository smallnest/autoresearch (577 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 3,585 once invoked, about $0.0003 per session on Opus 5.5. 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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