Borrowing it
Nothing to install: this file belongs to doncheli/don-cheli-sdd. 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/doncheli/don-cheli-sdd/main/.agent/skills/doncheli-tea/SKILL.mdgit clone --depth 1 https://github.com/doncheli/don-cheli-sddWrote 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/doncheli/don-cheli-sdd/doncheli-tea)<a href="https://agentmods.dev/skills/doncheli/don-cheli-sdd/doncheli-tea"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-tea/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/doncheli/don-cheli-sdd/doncheli-tea"><img src="https://agentmods.dev/badge/skills/doncheli/don-cheli-sdd/doncheli-tea.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 | $0.00054 | $0.00435 |
| Opus 5 | $0.00027 | $0.00217 |
| Sonnet 5 | $0.00011 | $0.00087 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
doncheli-tea 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 11d 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
Don Cheli: Test End-to-End Autonomous (TEA)
Instructions
- Detect the test runner from
package.json,pyproject.toml,Makefile, or.dc/config.yaml - Run tests in this order: unit → integration → E2E (stop on catastrophic failure at each tier)
- Capture stdout/stderr; do not swallow errors silently
- Categorize results: passed, failed, skipped, flaky (failed then passed on retry)
- For each failing test, provide:
- Test name and file path
- Error message (full, not truncated)
- Likely root cause (one sentence)
- Suggested fix (if deterministic)
- If the same test fails twice in a row with the same error, escalate to the user instead of retrying
- Report coverage if the runner supports it; flag if below the 85% threshold
- Save the full report to
.dc/test-report-<timestamp>.md - Exit with a clear pass/fail summary — no ambiguous "some tests failed" messages
Output Format
## TEA Report — 2026-03-28T14:32Z
### Summary
- Passed: 142
- Failed: 3
- Skipped: 7
- Coverage: 88% ✅
### Failures
#### 1. UserService.createUser — duplicate email
File: src/user/user.service.spec.ts:88
Error: Expected status 409, got 500
Root cause: Missing unique constraint handler in UserRepository
Fix: Add try/catch for PG error code 23505 and throw ConflictException
### Coverage Warning
- src/payments/refund.ts — 61% (below 85% threshold)
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.
- 11d ago First seen · 46 lines · 54 tokens per session scan A 498492eb92c7
doncheli-tea is a skill published in the GitHub repository doncheli/don-cheli-sdd (57 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 54 tokens to every session and 435 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.
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browser-screenshot-diff
Visual + DOM diff between two recorded sessions at matching trajectory step ids; used for visual regression and replay verification.