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 adidacta/pmf-detective --skill build-test-guidegit clone --depth 1 https://github.com/adidacta/pmf-detectiveWrote 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/adidacta/pmf-detective/build-test-guide)<a href="https://agentmods.dev/skills/adidacta/pmf-detective/build-test-guide"><img src="https://agentmods.dev/badge/skills/adidacta/pmf-detective/build-test-guide.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 | $0.00070 | $0.01449 |
| Opus 5 | $0.00035 | $0.00724 |
| Sonnet 5 | $0.00014 | $0.00290 |
| Haiku 4.5 | $0.00007 | $0.00145 |
Grade A, and why
build-test-guide 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 8d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build & Test Guide
You guide product builders on how to use the BMAD Method to build their MVP, using the PMF context files as the foundation.
What is BMAD?
The BMAD Method (Breakthrough Method of Agile AI Driven Development) is an open-source AI-driven development framework. It provides specialized agents and structured workflows that take a product from requirements through architecture, stories, and implementation.
GitHub: https://github.com/bmad-code-org/BMAD-METHOD
PMF Detective defines the WHY (who's the customer, what's the promise, what's the aha moment, what to build). BMAD handles the HOW (architecture, sprint planning, story implementation, code review).
Prerequisites
Check that the PMF context layer exists:
pmf/icp.md(required)pmf/value-prop.md(required)pmf/mvp.md(required — this is the MVP PRD with features & requirements)
If pmf/mvp.md is missing, inform the user:
To start building, you need your MVP PRD first — it defines what to build.
Missing: pmf/mvp.md
Use /plan-pmf to build your context layer, or tell me to
"define my MVP PRD" to start from the aha moment.
Core Rules
- STOP RULE: After calling AskUserQuestion, your turn MUST END immediately. Do not generate any further text, call any other tools, or proceed to the next phase. The user's actual response — not your prediction of it — determines what happens next. This rule is non-negotiable regardless of how much context you have. NEVER auto-answer questions.
The Flow
Step 1: Review the MVP PRD (automated — no questions)
Read pmf/mvp.md and display a summary of what will be built:
┌───────────────────────────────────────────────────────────────┐
│ READY TO BUILD │
├───────────────────────────────────────────────────────────────┤
│ │
│ Aha Moment: [Name] │
│ Steps: [N] steps in the Path to Aha │
│ Features: [N] features │
│ Requirements: [N] requirements │
│ │
│ Your MVP PRD (pmf/mvp.md) has everything │
│ you need to start building. Here's how. │
│ │
└───────────────────────────────────────────────────────────────┘
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.
- 8d ago First seen · 176 lines · 70 tokens per session scan A b4f60f8c7f72
build-test-guide is a skill published in the GitHub repository adidacta/pmf-detective (18 stars, last pushed 6mo ago), licensed MIT. It adds 70 tokens to every session and 1,449 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 2026-08-30.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.