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/ambient-code/agentready/test-assessnpx skills add ambient-code/agentready --skill test-assessgit clone --depth 1 https://github.com/ambient-code/agentreadyWhat 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 | $0.00075 | $0.00770 |
| Opus 5 | $0.00037 | $0.00385 |
| Sonnet 5 | $0.00015 | $0.00154 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
test-assess 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test agentready assess on real repositories
You are testing the agentready assessment tool against real GitHub repositories to validate that a change works correctly.
1. Determine what to test
The user will describe what assessor behavior they want to validate (e.g., "test Husky detection", "verify the new CI gates assessor"). If the description is unclear, ask a brief clarifying question.
2. Ensure the code under test is checked out
Check which branch is currently active. If the change being tested is on a
different branch or PR, check it out first. If it is a PR, use
gh pr checkout <number>.
3. Select repositories
Search GitHub for repositories that are relevant to the feature being tested. For example, if testing Husky hook detection, find repos that use Husky.
How many repos:
- Small or targeted changes: at least 1 repo
- Larger or riskier changes: 3 to 5 repos
Selection criteria (in priority order):
- Relevance: repos must exercise the specific behavior being tested. This is the most important criterion.
- Variety of maturity: when possible, mix large popular projects with smaller or newer ones. But never sacrifice relevance for variety.
Use gh search repos, gh api, or gh search code to find candidates.
Briefly confirm your repo selections with the user before cloning.
4. Clone repos to a temp directory
Create a unique temp directory for this test run:
TESTDIR=$(mktemp -d /tmp/agentready-test-XXXXXX)
Shallow clone each repo into that directory:
git clone --depth 1 <repo-url> $TESTDIR/<repo-name>
5. Run assessments
Run each assessment using the local checkout, not the globally installed package:
yes | PYTHONPATH=src python -m agentready assess $TESTDIR/<repo-name>
The yes | prefix auto-confirms the large-repo prompt if it appears.
6. Report results
After each assessment completes, report to the user:
- The repo name and the overall score/certification level
- The specific finding(s) relevant to what is being tested, including status, score, and evidence
- The exact paths to the JSON, HTML, and Markdown reports so the user can inspect them
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 · 99 lines · 75 tokens per session scan A a61600b8e53d
test-assess is a skill published in the GitHub repository ambient-code/agentready (151 stars, last pushed 6d ago), licensed MIT. It adds 75 tokens to every session and 770 once invoked, about $0.0004 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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