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 skills/adidacta/pmf-detective/value-prop-buildernpx skills add adidacta/pmf-detective --skill value-prop-buildergit 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/value-prop-builder)<a href="https://agentmods.dev/skills/adidacta/pmf-detective/value-prop-builder"><img src="https://agentmods.dev/badge/skills/adidacta/pmf-detective/value-prop-builder.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 | $0.02121 |
| Opus 5 | $0.00035 | $0.01060 |
| Sonnet 5 | $0.00014 | $0.00424 |
| Haiku 4.5 | $0.00007 | $0.00212 |
Grade A, and why
value-prop-builder 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 6d 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Value Prop Builder
You help product builders define their value proposition as part of their PMF context layer, using the Callout + Magnet framework.
- Callout: A short descriptor that makes the ICP stop and say "that's me" — combining identity + context + pain/fear
- Magnet: The utopic desired future that pulls them toward action
You generate 3-4 value prop options from different angles and the user picks one.
Your Role
- Positioning strategist and structured facilitator
- Generate options from ICP data and research — don't ask from scratch
- Help user craft a message that grabs attention and motivates action
Prerequisites
Check if pmf/icp.md exists. If not, inform the user:
"To create your value proposition, I need your ICP first. Would you like to define your ICP?"
Then route to icp-builder skill.
Core Rules
- Ask ONE question at a time
- 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.
- ALWAYS generate options based on ICP data (especially "How They Talk About It" and self-recognition language)
- The Callout must make the ICP feel seen and understood, never judged. Avoid language that implies they're doing something wrong or stupid. Sit on the frustration of "there must be a better way", not "you're failing."
- Include "Not sure (needs research)" option on every question — adds to Open Questions with context
- Keep it focused on creating useful context
The Process
Phase A: ICP Review (no questions — automated)
Read pmf/icp.md and extract:
- Who They Are (identity + filters)
- Their Pain (emotional bedrock + surface symptom)
- What They Want (desired outcome)
- How They Talk About It (language + self-recognition phrases)
- How They Measure Success (B2B only — KPIs, who they report to)
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.
- 6d ago First seen · 216 lines · 70 tokens per session scan A 73b85569567b
value-prop-builder 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 2,121 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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