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/tmj-90/gaffer/ricenpx skills add tmj-90/gaffer --skill ricegit clone --depth 1 https://github.com/tmj-90/gafferWrote 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/tmj-90/gaffer/rice)<a href="https://agentmods.dev/skills/tmj-90/gaffer/rice"><img src="https://agentmods.dev/badge/skills/tmj-90/gaffer/rice.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 | $0.00068 | $0.00907 |
| Opus 5 | $0.00034 | $0.00453 |
| Sonnet 5 | $0.00014 | $0.00181 |
| Haiku 4.5 | $0.00007 | $0.00091 |
Grade A, and why
rice 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 4d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prioritise features with RICE scoring
RICE cuts through "loudest voice" prioritisation. Every feature gets a score from the same formula; the list sorts itself.
The formula
RICE = (Reach × Impact × Confidence) / Effort
| Factor | What it measures | Scale |
|---|---|---|
| Reach | Users affected per time period (e.g. per quarter) | Raw number (not a 1–5 scale) |
| Impact | Effect on the metric per user who encounters the feature | 0.25 (minimal) / 0.5 / 1 / 2 / 3 (massive) |
| Confidence | How certain are the estimates? | 0.5 (low) / 0.8 (medium) / 1.0 (high) |
| Effort | Person-months of work | Raw number (not a 1–5 scale) |
A higher score = build sooner. Within a sprint, also apply capacity constraints (effort sum ≤ sprint capacity).
Common calibration mistakes
- Reach is per time period — "all users" is meaningless; specify the window (per quarter / per month).
- Impact uses the fixed scale — resist the urge to invent 1–10 scales; the fixed scale forces honest comparisons.
- Confidence should hurt — if you're guessing, use 0.5. Most estimates that feel like 0.8 are actually 0.5.
- Effort in person-months — a 1-week task for 2 engineers = 0.5 person-months, not 1.
Steps
- Define the metric. RICE scores are only comparable when measuring impact on the same metric. Establish the North Star before scoring.
- List features. Collect all candidates. Don't pre-filter — let scoring do the filtering.
- Score each feature using the four factors. Be explicit about assumptions; document them next to the score.
- Apply confidence calibration. Push back on confidence scores above 0.8 unless there is user research, analytics, or a successful prior experiment behind the estimate.
- Rank. Sort descending by RICE score.
- Apply capacity constraints (if sprint planning). Sum effort from the top until capacity is consumed. Flag any item ≥ 5 person-months for decomposition.
- Sanity-check the top 5. Do the top items match intuition? If not — is the formula right, or is intuition wrong? Challenge both.
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.
- 4d ago First seen · 69 lines · 68 tokens per session scan A 58ec023b2834
rice is a skill published in the GitHub repository tmj-90/gaffer (2 stars, last pushed 4d ago), licensed Apache-2.0. It adds 68 tokens to every session and 907 once invoked, about $0.0003 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.
Other skills, from other repositories
plan-creation
Create structured implementation plans via a 4-role scrum team (Product Owner, Architect, Eng/Delivery Lead, QA/Critic) with optional Agent Teams peer debate mode.
pact-init
Initialize PACT governance files in the current project.
titanclip
Interact with the TitanClip control plane API to manage tasks, coordinate with other agents, and follow company governance. Use when you need to check assignments, update task status, delegate work, post comments, set up or manage routines (recurring scheduled tasks), or call any TitanClip API endpoint. Do NOT use for…
backlog-grooming-assistant
Execute backlog grooming assistant operations. Auto-activating skill for Enterprise Workflows. Triggers on: backlog grooming assistant, backlog grooming assistant Part of the Enterprise Workflows skill category. Use when working with backlog grooming assistant functionality. Trigger with phrases like "backlog grooming…
agile-product-owner
Agile product ownership toolkit for Senior Product Owner including INVEST-compliant user story generation, sprint planning, backlog management, and velocity tracking. Use for story writing, sprint planning, stakeholder communication, and agile ceremonies.
confluence-expert
Atlassian Confluence expert for creating and managing spaces, knowledge bases, documentation, planning, product discovery, page layouts, macros, templates, and all Confluence features. Use for documentation strategy, space architecture, content organization, and collaborative knowledge management.