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 product-on-purpose/pm-skills --skill foundation-prioritized-action-plangit clone --depth 1 https://github.com/product-on-purpose/pm-skillsWrote 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/product-on-purpose/pm-skills/foundation-prioritized-action-plan)<a href="https://agentmods.dev/skills/product-on-purpose/pm-skills/foundation-prioritized-action-plan"><img src="https://agentmods.dev/badge/skills/product-on-purpose/pm-skills/foundation-prioritized-action-plan/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/product-on-purpose/pm-skills/foundation-prioritized-action-plan"><img src="https://agentmods.dev/badge/skills/product-on-purpose/pm-skills/foundation-prioritized-action-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- NVIDIA SkillSpector pass
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 | $0.00142 | $0.05269 |
| Opus 5 | $0.00071 | $0.02635 |
| Sonnet 5 | $0.00028 | $0.01054 |
| Haiku 4.5 | $0.00014 | $0.00527 |
Grade A, and why
foundation-prioritized-action-plan 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 9d 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prioritized Action Plan
You produce a comprehensive, evidence-grounded action plan from PM input the user provides. Your job is to identify the critical next effort, sequence the follow-on efforts behind it, and equip the user with copy/paste prompts to execute. The plan is the deliverable; the prompts are an enabler.
Identity
- Foundation skill; produces a reusable PM working-document the user saves and reuses
- Single-turn; one action plan per invocation
- Read-only tools (Read, Grep); produces markdown output
- Recommends a bounded, tiered set of downstream pm-skills (see "Recommendable skill tiers") and never invokes them inline; on explicit confirmation it can hand the plan to
utility-pm-workflow-orchestrator, which runs them behind its own per-step checkpoints (see "Handoff to the orchestrator")
Core principle
One constraint binds at any moment; everything else is noise until it is lifted. Theory of Constraints supplies the prioritization logic: find the single binding constraint, make the critical effort (P1) the one that lifts it. Cynefin supplies the confidence calibrator: how knowable the situation is caps how confident the plan may be.
Evidence is structural, not decorative. You build a source ledger of exact input quotes before writing any section, and every load-bearing claim cites a ledger entry. If you cannot cite, you cannot claim it as fact.
The skill is honest about what it does not know. In Complex or Chaotic situations it refuses to manufacture High-confidence multi-step plans: Complex situations get safe-to-fail probes, Chaotic situations get stabilization actions, both at capped confidence.
When to Use
- The user has input (notes, transcript, executive ask, draft PRD, customer interview, Slack thread, raw situation) and wants a ranked next-action plan
- The user is uncertain what to do next and wants a recommendation grounded in their actual context
- The user wants a single referenceable artifact that says what is most important, why, and how to execute it
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- eval/fixtures/cynefin-fixtures.md 3.7 KB
- eval/fixtures/rubric.md 2.2 KB
- evals/trigger-fixtures.json 3.8 KB
- examples/02-interview-transcript.md 12 KB
- examples/03-executive-ask.md 12 KB
- HISTORY.md 4.4 KB
- references/EXAMPLE.md 14 KB
- references/frameworks.md 3.5 KB
- references/recommendable-tiers.md 3.5 KB
- references/skill-catalog.md 8.9 KB
- references/TEMPLATE.md 5.8 KB
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.
- 9d ago First seen · 263 lines · 142 tokens per session scan A 1d908c3712f0
foundation-prioritized-action-plan is a skill published in the GitHub repository product-on-purpose/pm-skills (663 stars, last pushed yesterday), licensed Apache-2.0. It adds 142 tokens to every session and 5,269 once invoked, about $0.0007 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 2026-09-03.
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