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 commands/davidmatousek/tachi/aod.scoregit clone --depth 1 https://github.com/davidmatousek/tachiWrote 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/commands/davidmatousek/tachi/aod.score)<a href="https://agentmods.dev/commands/davidmatousek/tachi/aod.score"><img src="https://agentmods.dev/badge/commands/davidmatousek/tachi/aod.score.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 | $0.00011 | $0.00516 |
| Opus 5 | $0.00005 | $0.00258 |
| Sonnet 5 | $0.00002 | $0.00103 |
| Haiku 4.5 | $0.00001 | $0.00052 |
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- aod.score — 100% identical, 0 lines differ
What it actually says
User Input
$ARGUMENTS
Consider user input before proceeding (if not empty).
Overview
Updates an existing idea's ICE score when circumstances change, new information emerges, or priorities shift.
Source of truth: GitHub Issues. Reads and updates the idea's GitHub Issue body.
Flow: Parse identifier (NNN, #NNN, or IDEA-NNN) → Find GitHub Issue → Display current scores → New ICE scoring → Update status → Update GitHub Issue → Regenerate BACKLOG.md → Report comparison
Step 1: Validate Input
- Parse idea identifier from
$ARGUMENTS - Accept three formats:
NNN(bare number),#NNN(hash-prefixed), orIDEA-NNN(legacy) - If invalid or missing: Display usage
Usage: /aod.score NNN(or#NNNorIDEA-NNN)
Step 2: Execute Re-Scoring
Follow the workflow defined in the ~aod-score skill (.claude/skills/~aod-score/SKILL.md):
- Find GitHub Issue: For numeric input, call
aod_gh_find_issue NNN; for legacy IDEA-NNN, callaod_gh_find_issue "[IDEA-NNN]" - Read issue body to extract current ICE scores and status
- Display current ICE scores and status
- Present new ICE scoring via AskUserQuestion (Impact, Confidence, Effort — each H9/M6/L3 or custom 1-10)
- Compute new total, apply status transitions (threshold crossings, Validated preserved, Rejected re-opens)
- Update GitHub Issue body with new scores and status
- Add re-score comment to issue
- Regenerate BACKLOG.md via
.aod/scripts/bash/backlog-regenerate.sh - Report old vs new comparison with tier change if applicable
Quality Checklist
- Idea identifier validated (NNN, #NNN, or IDEA-NNN)
- GitHub Issue found for the idea
- Current scores displayed
- New ICE score computed correctly
- Status transitions applied correctly
- GitHub Issue updated (body + comment)
- BACKLOG.md regenerated
- Comparison reported
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 · 51 lines · 11 tokens per session scan A 0cbc910fc7bb
aod.score is a command published in the GitHub repository davidmatousek/tachi (90 stars, last pushed 22d ago), licensed Apache-2.0. It adds 11 tokens to every session and 516 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 2026-08-30.
Other commands, from other repositories
fortigate
Run cs-security-analyst (AT + CA workflows) against the Fintech FortiGate zero-day scenario and produce a 7-task scorecard.
README
Claude Code slash commands for the Unified Security Agent Platform (USAP). These commands let you load any USAP skill as a live LLM persona and run structured compliance tests — all inside a Claude Code session.
challenge
Run cs-security-analyst (AT workflow) against the Perfect Storm 8-vector crisis scenario and produce a 12-check mock comparison scorecard.
compare
Before/after comparison of zero-day-response SKILL.md v1 (broken) vs v2 (fixed) against the FortiGate zero-day scenario. Outputs a scored table.
run
Load a USAP skill SKILL.md and activate it as your operating persona. Argument: skill slug.
test
Run a USAP skill against the FortiGate zero-day test scenario and produce a 6-problem compliance scorecard.