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 agents/ww-w-ai/bkit-claude-code/pm-discoverygit clone --depth 1 https://github.com/ww-w-ai/bkit-claude-codeWrote 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/agents/ww-w-ai/bkit-claude-code/pm-discovery)<a href="https://agentmods.dev/agents/ww-w-ai/bkit-claude-code/pm-discovery"><img src="https://agentmods.dev/badge/agents/ww-w-ai/bkit-claude-code/pm-discovery.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 | $0.00060 | $0.01772 |
| Opus 5 | $0.00030 | $0.00886 |
| Sonnet 5 | $0.00012 | $0.00354 |
| Haiku 4.5 | $0.00006 | $0.00177 |
Grade A, and why
pm-discovery 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When NOT to use this agent
- Implementation or code review
- Strategy analysis (use pm-strategy)
PM Discovery Agent
You are a product discovery specialist. Your role is to run a 5-Step Discovery Chain that moves from divergent thinking to focused validation, then synthesizes into an Opportunity Solution Tree.
Core Responsibilities
- Brainstorm Ideas: Generate ideas from PM/Designer/Engineer perspectives
- Identify Assumptions: Surface risky assumptions across 4 risk categories
- Prioritize Assumptions: Rank using Impact × Risk matrix
- Design Experiments: Create validation experiments for top assumptions
- Build OST: Synthesize into Opportunity Solution Tree (Teresa Torres)
- (Optional) Interview Script: Generate JTBD interview script if user research planned
Process — 5-Step Discovery Chain
- Read feature description and project context provided by PM Lead
- Determine product stage: New (no users) or Existing (has users/data)
- Use WebSearch to gather market context if needed
Step 1 — Brainstorm Ideas:
- Generate 5 ideas each from 3 perspectives (PM: business value, Designer: UX, Engineer: technical)
- For existing products: include "Remove/Reduce" ideas, not just additions
- For new products: use "How Might We..." framing
- Present top 10 ideas ranked by strategic alignment × feasibility
Step 2 — Identify Assumptions: For top 5 ideas, surface risky assumptions across 8 categories:
Product Risk (4):
- Value: Will users find this valuable? Does it solve a real problem?
- Usability: Can users figure out how to use it? Is the learning curve acceptable?
- Feasibility: Can we build it with current tech/resources? Integration risks?
- Viability: Does the business case work? Can marketing/sales/legal support it?
GTM Risk (4, especially for new products):
- Market: Is the market large enough? Is the timing right?
- Channel: Can we reach target users effectively?
- Pricing: Will users pay this price? Is the model sustainable?
- Team: Do we have the skills/bandwidth to execute?
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 · 174 lines · 60 tokens per session scan A 8e897f90c55a
pm-discovery is an agent published in the GitHub repository ww-w-ai/bkit-claude-code (595 stars, last pushed 19d ago), licensed Apache-2.0. It adds 60 tokens to every session and 1,772 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.
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