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 shennawardana23/skillme --skill feature-prioritization-frameworksgit clone --depth 1 https://github.com/shennawardana23/skillmeWrote 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/shennawardana23/skillme/feature-prioritization-frameworks)<a href="https://agentmods.dev/skills/shennawardana23/skillme/feature-prioritization-frameworks"><img src="https://agentmods.dev/badge/skills/shennawardana23/skillme/feature-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/shennawardana23/skillme/feature-prioritization-frameworks"><img src="https://agentmods.dev/badge/skills/shennawardana23/skillme/feature-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 | $0.00119 | $0.02076 |
| Opus 5 | $0.00060 | $0.01038 |
| Sonnet 5 | $0.00024 | $0.00415 |
| Haiku 4.5 | $0.00012 | $0.00208 |
Grade A, and why
feature-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 7d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Prioritization Frameworks
Prioritization frameworks answer different questions. RICE answers "which of these comparable features gives the most impact per unit of effort." MoSCoW answers "which of these committed-scope items can we cut under time pressure." Kano answers "what shape of satisfaction does this feature produce, and does building more of it even keep helping." Picking the wrong one for the situation produces a confident-looking but wrong answer — pick based on the question being asked, not habit.
RICE scoring (primary, quantitative)
RICE = (Reach × Impact × Confidence) / Effort
Compute each factor before combining them — don't eyeball the final score.
- Reach: how many users/customers this affects in a fixed time period (e.g., "per quarter"). A count, not a percentage — use actual or estimated numbers (e.g., 400 users/month), so reach isn't silently double-weighted against impact.
- Impact: how much it moves the needle per user reached, scored on
a discrete scale, not a continuum, because false precision here is the
most common RICE mistake:
- 3 = massive impact
- 2 = high impact
- 1 = medium impact
- 0.5 = low impact
- 0.25 = minimal impact
- Confidence: how sure you are about the Reach and Impact estimates,
as a percentage, reflecting evidence quality:
- 100% = backed by data (analytics, experiment results)
- 80% = backed by partial data or strong qualitative signal
- 50% = a guess with some reasoning behind it
- Below 50% — the estimate is too weak to score; go get more evidence or explicitly flag the score as low-confidence in the output, don't silently treat it as equal to a data-backed guess.
- Effort: total person-time to ship, in a consistent unit (e.g., "person-months"), including design/QA/rollout, not just the coding estimate — effort estimates that only count implementation time systematically overrate features with a hidden testing or migration cost.
Worked example and application steps
What ships with it
3 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.
- 7d ago First seen · 172 lines · 119 tokens per session scan A 64638b6ed2f0
feature-prioritization-frameworks is a skill published in the GitHub repository shennawardana23/skillme (2 stars, last pushed 13d ago), licensed Apache-2.0. It adds 119 tokens to every session and 2,076 once invoked, about $0.0006 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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