Borrowing it
Nothing to install: this file belongs to aveproject/ave. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aveproject/ave/main/.claude/skills/tdd/SKILL.mdgit clone --depth 1 https://github.com/aveproject/aveWrote 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/aveproject/ave/tdd)<a href="https://agentmods.dev/skills/aveproject/ave/tdd"><img src="https://agentmods.dev/badge/skills/aveproject/ave/tdd/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/aveproject/ave/tdd"><img src="https://agentmods.dev/badge/skills/aveproject/ave/tdd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.00249 |
| Opus 5.5 | $0.00000 | $0.00100 |
| Sonnet 5.5 | $0.00000 | $0.00050 |
| Haiku 4.5 | $0.00000 | $0.00025 |
Grade A, and why
tdd 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 1mo 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.
What it actually says
tdd — ave
For this repo, TDD means: fixtures first, then rule, then record validation.
Loop for a new rule
- Write the positive fixture (the malicious file)
- Write the negative fixture (the benign lookalike)
- Write a test asserting the rule fires on positive, not on negative → FAIL
- Write the rule → test PASSES
- Write the AVE record JSON
- python scripts/validate_records.py → record valid
- pytest tests/ -x -q → full suite green
What/Why/How on validation functions
# What: returns True if every record in records/ validates against the schema
# Why: one malformed record breaks every scanner that loads the record set
# How: loads each JSON, runs jsonschema.validate, collects all errors
def all_records_valid() -> tuple[bool, list[str]]:
...
The negative fixture rule
Every rule needs a negative fixture that looks SIMILAR to the positive but is benign. A rule with only a positive fixture is a false-positive waiting to happen. Test that the rule does NOT fire on the negative.
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.
- 1mo ago First seen · 30 lines · 0 tokens per session scan A 55ae3ed36749
tdd is a skill published in the GitHub repository aveproject/ave (24 stars, last pushed 2d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 249 tokens. 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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