~aod-score

A backlog tool for updating an idea’s ICE score—Impact, Confidence, and Effort—when the situation changes. It stores the updated idea data in its GitHub Issue.

In plain words
What is it for?
Re-scoring an idea by its number, #number, or older IDEA-number format. It helps update the idea’s priority and status in GitHub.
Why use it?
It keeps idea priorities current when new information or circumstances make an earlier score outdated. GitHub Issues remain the single record instead of relying on a generated backlog file.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/davidmatousek/agentic-oriented-development-kit/aod-score
Any agent
npx skills add davidmatousek/agentic-oriented-development-kit --skill aod-score
Clone the repo
git clone --depth 1 https://github.com/davidmatousek/agentic-oriented-development-kit

Made for: Claude Code, Codex.

Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,971 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00044 $0.01971
Opus 5 $0.00022 $0.00986
Sonnet 5 $0.00009 $0.00394
Haiku 4.5 $0.00004 $0.00197

Measured 2d ago against content hash 1f2edfe26bd1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

~aod-score 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 2d 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.

.claude/skills/~aod-score/SKILL.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AOD Re-Score Skill

Purpose

Update an existing idea's ICE (Impact, Confidence, Effort) score when circumstances change, new information emerges, or priorities shift. Reads from and writes to the idea's GitHub Issue.

Source of Truth

GitHub Issues are the sole source of truth for backlog items. All idea state (ICE scores, status, evidence) is stored in the GitHub Issue body. BACKLOG.md is an auto-generated view regenerated from Issues.

Inputs

  • NNN, #NNN, or IDEA-NNN (legacy): The identifier of the idea to re-score (from user arguments)

Workflow

Step 1: Parse Input

Extract the idea identifier from user arguments. Accept three formats:

  • NNN (bare number, e.g., 21): Direct GitHub Issue lookup
  • #NNN (hash-prefixed, e.g., #21): Strip # prefix, direct lookup
  • IDEA-NNN (legacy, e.g., IDEA-009): Search issue titles for [IDEA-NNN] bracket tag

If invalid or missing, display usage: Usage: /aod.score NNN (or #NNN or IDEA-NNN)

Step 2: Find GitHub Issue

Search for the matching GitHub Issue:

For numeric input (NNN or #NNN):

source .aod/scripts/bash/github-lifecycle.sh && aod_gh_find_issue NNN

For legacy IDEA-NNN input:

source .aod/scripts/bash/github-lifecycle.sh && aod_gh_find_issue "[IDEA-NNN]"

If no issue is found, display an error and exit:

Error: No GitHub Issue found for {identifier}

Step 3: Read Current Scores

Read the GitHub Issue body using gh issue view {number} --json body,title. Parse the structured body to extract:

  • Description (from ## Idea section or title)
  • ICE scores (from ## ICE Score section)
  • Source (from ## Metadata section)
  • Status (from ## Metadata section)
  • Evidence (from ## Evidence section)

Step 4: Display Current Scores

Show the existing idea details:

CURRENT SCORES — #{issue_number}

GitHub Issue: #{issue_number}
Idea: {description}
Source: {source}
Date: {date}
Status: {status}

ICE Score: {total} (I:{impact} C:{confidence} E:{effort})
Priority Tier: {tier}

Read the full file on GitHub · 208 lines

Changes

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.

  1. 2d ago First seen · 208 lines · 44 tokens per session scan A 1f2edfe26bd1

Subscribe to this mod's changes

~aod-score is a skill published in the GitHub repository davidmatousek/agentic-oriented-development-kit (22 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 1,971 once invoked, about $0.0002 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 2026-08-30.

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