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 konveyor-ecosystem/playpen-pf-mig-skills --skill prdgit clone --depth 1 https://github.com/konveyor-ecosystem/playpen-pf-mig-skillsWrote 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/konveyor-ecosystem/playpen-pf-mig-skills/prd)<a href="https://agentmods.dev/skills/konveyor-ecosystem/playpen-pf-mig-skills/prd"><img src="https://agentmods.dev/badge/skills/konveyor-ecosystem/playpen-pf-mig-skills/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/konveyor-ecosystem/playpen-pf-mig-skills/prd"><img src="https://agentmods.dev/badge/skills/konveyor-ecosystem/playpen-pf-mig-skills/prd.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00060 | $0.01671 |
| Opus 5 | $0.00030 | $0.00835 |
| Sonnet 5 | $0.00012 | $0.00334 |
| Haiku 4.5 | $0.00006 | $0.00167 |
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 11d 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.
This is a copy
94% identical to prd — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD Generator
Create detailed Product Requirements Documents that are clear, actionable, and suitable for implementation.
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
- Save to
tasks/prd-[feature-name].md
Important: Do NOT start implementing. Just create the PRD.
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.
Step 2: PRD Structure
Generate the PRD with these sections:
1. Introduction/Overview
Brief description of the feature and the problem it solves.
2. Goals
Specific, measurable objectives (bullet list).
3. User Stories
Each story needs:
- Title: Short descriptive name
- Description: "As a [user], I want [feature] so that [benefit]"
- Acceptance Criteria: Verifiable checklist of what "done" means
Each story should be small enough to implement in one focused session.
Format:
### US-001: [Title]
**Description:** As a [user], I want [feature] so that [benefit].
**Acceptance Criteria:**
- [ ] Specific verifiable criterion
- [ ] Another criterion
- [ ] Typecheck/lint passes
- [ ] **[UI stories only]** Verify in browser using dev-browser skill
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.
- 11d ago First seen · 241 lines · 60 tokens per session scan A 1ca5be7f8baf
prd is a skill published in the GitHub repository konveyor-ecosystem/playpen-pf-mig-skills (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 1,671 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to prd, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…