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 agentmods add skills/tupe12334/instinct/rice-scoringnpx skills add tupe12334/instinct --skill rice-scoringgit clone --depth 1 https://github.com/tupe12334/instinctWrote 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/tupe12334/instinct/rice-scoring)<a href="https://agentmods.dev/skills/tupe12334/instinct/rice-scoring"><img src="https://agentmods.dev/badge/skills/tupe12334/instinct/rice-scoring.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 | $0.00023 | $0.01940 |
| Opus 5 | $0.00012 | $0.00970 |
| Sonnet 5 | $0.00005 | $0.00388 |
| Haiku 4.5 | $0.00002 | $0.00194 |
Grade A, and why
rice-scoring 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 5d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RICE Scoring
Overview
RICE is a quantitative prioritization framework that computes a single comparable score for each feature or initiative. It prevents gut-feel ranking by forcing explicit estimates across four dimensions: how many people are affected, how much it helps them, how sure you are, and how much work it takes.
RICE Score = (Reach × Impact × Confidence) / Effort
Higher score = higher priority. Items are ranked by score descending.
The Four Dimensions
Reach — "How many people, in what time window?"
Count the number of users or customers affected per time period (typically per quarter). Use real data: DAU, MAU, conversion funnel counts, support ticket volume. Do NOT estimate in percentages here — use absolute numbers.
- Example: 2,400 users/quarter who go through the checkout flow
Impact — "How much does this move the needle per person?"
Rate the impact on the individual user when they encounter the feature. Use a fixed scale:
| Score | Meaning |
|---|---|
| 3 | Massive (transforms the experience) |
| 2 | High (clear improvement) |
| 1 | Medium (noticeable) |
| 0.5 | Low (minor) |
| 0.25 | Minimal (barely perceptible) |
Confidence — "How sure are we about Reach and Impact?"
Express as a percentage reflecting evidence quality:
| % | Evidence |
|---|---|
| 100% | Hard data (A/B test, analytics, user research) |
| 80% | Some data (anecdotal, partial research) |
| 50% | Weak data (gut feel, one conversation) |
Never exceed 100%. Round to 100/80/50 — false precision is noise.
Effort — "How many person-months does this take?"
Estimate total engineering + design + PM time in person-months. Minimum value: 0.5 (half a person-month). Do NOT use story points — convert to time.
- 1 engineer for 2 weeks = 0.5 person-months
- 2 engineers + 1 designer for 1 month = 3 person-months
How to Apply
Step 1 — List all candidates
Write out every feature, project, or initiative under consideration. Aim to score at least 5–10 items so the ranking is meaningful.
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.
- 5d ago First seen · 137 lines · 23 tokens per session scan A 6a432d1b7275
rice-scoring is a skill published in the GitHub repository tupe12334/instinct (1 stars, last pushed 19d ago), licensed MIT. It adds 23 tokens to every session and 1,940 once invoked, about $0.0001 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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