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 pm-decision-modelgit 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/pm-decision-model)<a href="https://agentmods.dev/skills/ljucask/pureinn-product-development/pm-decision-model"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/pm-decision-model/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/pm-decision-model"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/pm-decision-model.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 | $0.00101 | $0.01822 |
| Opus 5 | $0.00051 | $0.00911 |
| Sonnet 5 | $0.00020 | $0.00364 |
| Haiku 4.5 | $0.00010 | $0.00182 |
Grade A, and why
pm-decision-model 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 10d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PM - Decision Model (JIT Helper)
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
Adds a single decision table (TBL-[DOMAIN]-NN) to domain/decision_models.md (Live Register 3) - without re-running the full pm-business-rules-library. This is the decision-models counterpart to the single-rule helpers: where pm-business-rule-core/critical/governance add one business rule to Register 2, this adds one decision table to Register 3.
A decision table belongs here when:
- Several input conditions combine to produce different outputs (not a single if/then)
- The logic is best read as a matrix - rows = condition combinations, columns = inputs + output
- Examples: discount tiers by customer level + order value + promo, eligibility by age + region + status, risk score by multiple signals, pricing matrix
If the logic is a single condition → one output, it is a business rule, not a table → use pm-business-rule-core / -critical / -governance instead.
When to run: During pm-feature-design when the JIT design reveals multi-condition logic that should be centralized. Or standalone when a decision matrix needs to be formalized before a feature enters build. (pm-business-rule-core routes here when a rule it is adding turns out to have multiple condition combinations.)
Dependencies
Required before running:
pm-business-rules-library-domain/decision_models.mdmust exist (initialized in Phase 4)pm-entity-registry- entity context for scoping the table
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.
- 10d ago First seen · 162 lines · 101 tokens per session scan A da51f1ea3b84
pm-decision-model is a skill published in the GitHub repository ljucask/pureinn-product-development (2 stars, last pushed today), licensed MIT. It adds 101 tokens to every session and 1,822 once invoked, about $0.0005 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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