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 stefanoskarakasis/Product-Marketing-Skills --skill prioritization-frameworksgit clone --depth 1 https://github.com/stefanoskarakasis/Product-Marketing-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/stefanoskarakasis/product-marketing-skills/prioritization-frameworks)<a href="https://agentmods.dev/skills/stefanoskarakasis/product-marketing-skills/prioritization-frameworks"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-skills/prioritization-frameworks/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/stefanoskarakasis/product-marketing-skills/prioritization-frameworks"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-skills/prioritization-frameworks.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 | $0.00089 | $0.03553 |
| Opus 5.5 | $0.00036 | $0.01421 |
| Sonnet 5.5 | $0.00018 | $0.00711 |
| Haiku 4.5 | $0.00009 | $0.00355 |
Grade A, and why
prioritization-frameworks 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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prioritization-Frameworks — Skill
How This Works
Prioritization frameworks are decision tools, not prescriptions. Your job is to pick the right one for the question, apply it with integrity, and translate the output into a tier recommendation (T1–T4) that drives GTM resource allocation.
The skill runs in 7 core steps:
Step 0 — Load brain context (ICP, alternatives & positioning) and guardrails from /context/meta-patterns.md, if it exists (e.g., "frameworks without customer confidence >7 have led to prior T1 overstatements" — an example pattern shape, not a claim already observed).
Step 1 — Intake: Understand what you're deciding (launch tier? roadmap backlog? strategic choice?) and what signals you have.
Step 2 — Framework selection: Based on decision type and available data, recommend the right framework(s).
Step 3 — Scoring: Run the framework, surface Confidence weaknesses, apply Quality Gates.
Step 4 — Tier translation: Convert framework output to T1–T4 with one-sentence rationale.
Step 5 — Audit & recommendation: Pressure-test the tier assignment; deliver go/no-go on tier or recommend additional validation.
Step 6 — Output: Tier assignment card + scoring table + next step.
Step 7 — Learning Close: log the session to /context/skill-sessions.md.
Trigger
- When: Choosing and applying a prioritization framework to score initiatives, backlog items, or strategic choices, and translating the result into a launch tier (T1–T4).
- Not for: Assigning a launch tier directly from a GTM brief without a scoring pass → use
go-to-market-strategy. Messaging hierarchy specifically → run Kano here, then hand off topositioning-messaging. - Example prompts:
- "Help me prioritize this backlog"
- "What framework should I use to decide between these two initiatives?"
- "Score this with RICE"
- "Which of these should we do first?"
Inputs
- Args: What's being decided (launch tier, backlog, strategic choice), number of initiatives being compared, available signals, timeline, audience for the output. Free format — Step 1 intake fills gaps conversationally.
- Defaults: If brain context is unavailable, this skill still runs — brain sharpens how "Opportunity" and "Impact" are interpreted, but is not a hard blocker.
- Context keys:
/foundation/brain.md— optional but recommended. Sections 2 (ICP), 3 (Alternatives & Positioning).
/context/meta-patterns.md— optional; recurring patterns the user has logged from prior scoring sessions.- Brain contract: Reads Sections 2, 3. Writes: none — this skill does not write to
/foundation/brain.md.
What ships with it
1 file 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.
- 11d ago Changed · +4 lines · -1 tokens per session bea8e6bc37d2
- 19d ago Changed a66f494af96e
- 1mo ago Changed · -2 tokens per session ec98e4caf7c1
- 1mo ago Changed · +2 lines aa7ef91bfb98
- 2mo ago First seen · 342 lines · 92 tokens per session scan A e829270d5d54
prioritization-frameworks is a skill published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (8 stars, last pushed 3d ago), licensed MIT. It adds 89 tokens to every session and 3,553 once invoked, about $0.0004 per session on Opus 5.5. 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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