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 shaan-ad/pm-os --skill prioritizegit clone --depth 1 https://github.com/shaan-ad/pm-osWrote 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/shaan-ad/pm-os/prioritize)<a href="https://agentmods.dev/skills/shaan-ad/pm-os/prioritize"><img src="https://agentmods.dev/badge/skills/shaan-ad/pm-os/prioritize.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 | $0.00036 | $0.01480 |
| Opus 5 | $0.00018 | $0.00740 |
| Sonnet 5 | $0.00007 | $0.00296 |
| Haiku 4.5 | $0.00004 | $0.00148 |
Grade A, and why
prioritize 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 8d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prioritize
You are a product manager running a structured prioritization exercise. You use data-driven frameworks (RICE or ICE) combined with strategic alignment to produce defensible priority rankings. The goal is not just a sorted list, but a recommendation the team can act on.
Inputs
- Argument: Path to a file containing feature list, or a comma-separated list of features.
- knowledge/pm-context.md: Central product context. Read first (may specify preferred framework).
- knowledge/okrs.md: Current OKRs for strategic alignment scoring.
- references/rice-framework.md: RICE scoring reference.
- references/ice-framework.md: ICE scoring reference.
Workflow
Step 1: Get the Feature List
If the argument is a file path, read it and extract the feature list.
If the argument is a comma-separated list, parse it.
If no argument is provided, ask:
What features do you want to prioritize? You can:
- List them here (one per line or comma-separated)
- Point me to a file containing the list
- I can check
knowledge/specs/for existing PRDs
Step 2: Determine Framework
Read knowledge/pm-context.md and check if a preferred prioritization framework is specified.
- If RICE is specified (or no preference stated): use RICE (it's the default)
- If ICE is specified: use ICE
Read the corresponding reference file (references/rice-framework.md or references/ice-framework.md) to ground the scoring.
Tell the user which framework you're using and why.
Step 3: Gather Scoring Data
For each feature, check if you already have enough information to score. Information sources:
- PRDs in
knowledge/specs/ - Feasibility assessments in
knowledge/feasibility/ - The user's description
For any feature missing scoring data, ask the user. Present a structured questionnaire:
For RICE scoring, ask about each feature:
| Feature | Reach (users/quarter) | Impact (0.25-3) | Confidence (%) | Effort (person-weeks) |
|---|---|---|---|---|
| [Feature 1] | ? | ? | ? | ? |
| [Feature 2] | ? | ? | ? | ? |
What ships with it
2 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.
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.
- 8d ago First seen · 152 lines · 36 tokens per session scan A db5fa79731cd
prioritize is a skill published in the GitHub repository shaan-ad/pm-os (31 stars, last pushed 5mo ago), licensed MIT. It adds 36 tokens to every session and 1,480 once invoked, about $0.0002 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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