Trellis is an engineering framework that stores project specifications, tasks, and working memory in a repository so coding agents can follow consistent development practices across sessions. Teams use it to organize AI-assisted planning, implementation, review, and validation across multiple coding platforms. The catalogue entries provide Trellis commands, agents, hooks, skills, instructions, and settings.
Borrowing it
Nothing to install: this file belongs to mindfold-ai/Trellis. 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/mindfold-ai/Trellis/main/.agents/skills/trellis-check/SKILL.mdgit clone --depth 1 https://github.com/mindfold-ai/TrellisWrote 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/mindfold-ai/trellis/trellis-check)<a href="https://agentmods.dev/skills/mindfold-ai/trellis/trellis-check"><img src="https://agentmods.dev/badge/skills/mindfold-ai/trellis/trellis-check.svg" alt="Measured on agentmods" 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 | $0.00054 | $0.00845 |
| Opus 5 | $0.00027 | $0.00423 |
| Sonnet 5 | $0.00011 | $0.00169 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
trellis-check 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 8d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 8d ago First seen · 112 lines · 54 tokens per session scan A dfb0600e95c1
trellis-check is a skill published in the GitHub repository mindfold-ai/Trellis (14,488 stars, last pushed 11d ago), licensed AGPL-3.0. It adds 54 tokens to every session and 845 once invoked, about $0.0003 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 skills, from other repositories
working-with-tdd
Use when implementing any feature, refactoring, or writing a bugfix.
tdd
Add new behavior test-first (write failing test → make it pass → refactor). Use when asked to "add a test for", "implement X with tests", or extending a codebase that already has a test suite.
verify
Run lint, typecheck, tests, or build — and fix any failures found. Use when asked "does it build", "run the tests", "check for lint errors", "find and fix lint errors", or after making code changes.
tdd
Enforces red-green-refactor TDD cycle with atomic commits per phase. Used when implementing features, fixing bugs, or when tests should drive the design.
agent-optimization
Improve an Agent State through versioned scores and score-linked Traces from a frozen Benchmark.
agent-evaluation
Run one specified Test Agent on one specified Benchmark Case exactly once, privately score that execution, and return one protocol result.