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 ljucask/pureinn-product-development --skill jtbd-buildinggit clone --depth 1 https://github.com/ljucask/pureinn-product-developmentWrote 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/ljucask/pureinn-product-development/jtbd-building)<a href="https://agentmods.dev/skills/ljucask/pureinn-product-development/jtbd-building"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/jtbd-building/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/ljucask/pureinn-product-development/jtbd-building"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/jtbd-building.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 | $0.00057 | $0.04164 |
| Opus 5 | $0.00028 | $0.02082 |
| Sonnet 5 | $0.00011 | $0.00833 |
| Haiku 4.5 | $0.00006 | $0.00416 |
Grade A, and why
jtbd-building 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.
How it starts
The opening of the file, as written. The whole thing — 410 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PM - JTBD Analysis
Agent mode (--agent)
Supports --agent: runs autonomously in a subagent, drafts the artifact from existing inputs, and returns a short summary + coverage note.
- No flag → interactive (default); if inputs are heavy, offer agent mode.
--agent→ obey. First check inputs are complete. Anything missing: do NOT invent it - mark[ASSUMED - what/why]in the output and summary. Never hallucinate to fill a gap.- Review required: the artifact contains commitments - after drafting, require the user's review before finalizing; do not close decisions autonomously.
What this skill does
Applies Jobs-to-be-Done theory to the validated customer research and personas to produce:
- Job Stories - When / I want to / So I can formulations per key persona
- Forces Diagram - what pushes away from the current solution and pulls toward a new one
- Switching logic - what triggers the decision to change, and what blocks it
JTBD analysis goes deeper than persona pain points. It explains the causal mechanism behind why someone would switch - the forces operating on their decision. This is the input for design decisions in Phase 3a (Design Thinking).
Run this after pm-personas. Personas give you who - JTBD gives you why.
Commissioned builds - two levels of job: the commissioner (client/sponsor) has their own job - they "hire" the product at business level (earn more / spend less / reduce risk / comply / status). That is distinct from the end users' jobs analyzed here. Record it as a separate, clearly labeled Commissioner Job story (source: client-discovery notes) - it governs prioritization and the engagement's success measure, while user jobs govern design. Never average the two into one job story.
Dependencies
Required before running:
pm-personas- personas and their pains are the foundation for JTBD analysis
Recommended before running:
- Raw interview transcripts - JTBD is most powerful when grounded in actual interview data, not synthesized personas alone
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 · 410 lines · 57 tokens per session scan A ac7e5f9c0bdb
jtbd-building is a skill published in the GitHub repository ljucask/pureinn-product-development (2 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 4,164 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 2026-08-31.
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validation-loop
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