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 fzfclee/consulting-skills --skill rice-scoringgit clone --depth 1 https://github.com/fzfclee/consulting-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/fzfclee/consulting-skills/rice-scoring)<a href="https://agentmods.dev/skills/fzfclee/consulting-skills/rice-scoring"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/rice-scoring/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/fzfclee/consulting-skills/rice-scoring"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/rice-scoring.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 | $0.00056 | $0.00773 |
| Opus 5 | $0.00028 | $0.00387 |
| Sonnet 5 | $0.00011 | $0.00155 |
| Haiku 4.5 | $0.00006 | $0.00077 |
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 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RICE Scoring
Use this skill to run RICE Scoring as a practical consulting method, not as a generic framework explanation.
Method Notes
- Use Reach x Impact x Confidence / Effort.
- Use the same time window and reach unit for every item.
Required Inputs
Collect or infer these inputs before execution:
- candidate initiatives
- reach estimate
- impact estimate
- confidence level
- effort estimate
If an input is missing, do not block automatically. Mark it as missing, state the assumption used, and add a validation action.
When Not To Use
Do not use when reach, impact, confidence, and effort are measured at incompatible levels or when compliance and dependency gates dominate the choice. RICE ranks comparable initiatives; it does not make strategy.
Adjacent Methods
decision-matrix: choose among strategic alternatives.weighted-scorecard: repeatable vendor or governance scoring.wsjf-prioritization: use when Cost of Delay is the central economic logic.
Step-by-Step Execution
| Step | Required input | How to execute | Output |
|---|---|---|---|
| Define scoring window | Time horizon, audience, product/business objective. | Set the same reach period and outcome for all items. | RICE scope. |
| Estimate Reach | Users/accounts/transactions affected in the window. | Count or estimate how many units each initiative reaches. | Reach values. |
| Estimate Impact and Confidence | Expected outcome change, evidence strength. | Score impact and confidence using consistent anchors. | Impact/confidence values. |
| Estimate Effort | People/time/cost needed. | Estimate total effort in the same unit for every initiative. | Effort values. |
| Calculate and rank | Reach, Impact, Confidence, Effort. | Compute Reach x Impact x Confidence / Effort and inspect low-confidence winners. | Ranked RICE backlog. |
Output Template
### 1. Scope And Scale
Backlog:
Time horizon:
Reach unit:
Impact scale:
Effort unit:
### 2. Scores
| Initiative | Reach | Impact | Confidence | Effort | RICE | Evidence |
|---|---|---|---|---|---|---|
| | | | | | | |
### 3. Ranking And Caveats
| Rank | Initiative | Main uncertainty | Dependency |
|---|---|---|---|
| | | | |
### 4. Decision
Fund now:
Validate first:
Do not prioritize:
Review trigger:
### Evidence And Next Decision
- Confirmed facts:
- Assumptions:
- Missing evidence:
- Next action, owner, and timing:
- Expected signal and decision threshold:
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 First seen · 90 lines · 56 tokens per session scan A 4d89f4c2219f
rice-scoring is a skill published in the GitHub repository fzfclee/consulting-skills (4 stars, last pushed 22d ago), licensed Apache-2.0. It adds 56 tokens to every session and 773 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.
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