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/davidmatousek/agentic-oriented-development-kit/aod-scorenpx skills add davidmatousek/agentic-oriented-development-kit --skill aod-scoregit clone --depth 1 https://github.com/davidmatousek/agentic-oriented-development-kitWhat 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 | $0.00044 | $0.01971 |
| Opus 5 | $0.00022 | $0.00986 |
| Sonnet 5 | $0.00009 | $0.00394 |
| Haiku 4.5 | $0.00004 | $0.00197 |
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.
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 lookupIDEA-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
## Ideasection or title) - ICE scores (from
## ICE Scoresection) - Source (from
## Metadatasection) - Status (from
## Metadatasection) - Evidence (from
## Evidencesection)
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}
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.
- 2d ago First seen · 208 lines · 44 tokens per session scan A 1f2edfe26bd1
~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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chat-perf
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Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
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