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 skills add JSchOBL/agentic-ai-learning-journey --skill run-testsgit clone --depth 1 https://github.com/JSchOBL/agentic-ai-learning-journeyWrote 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/jschobl/agentic-ai-learning-journey/run-tests)<a href="https://agentmods.dev/skills/jschobl/agentic-ai-learning-journey/run-tests"><img src="https://agentmods.dev/badge/skills/jschobl/agentic-ai-learning-journey/run-tests.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 5.1 | $0.00043 | $0.00532 |
| Opus 5 | $0.00022 | $0.00266 |
| Sonnet 5 | $0.00009 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
run-tests 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.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Tests
Run the project's test suite and report results honestly, including failures.
Steps
-
Determine scope from the request:
- A specific file was mentioned → run just that file
- "the tests" or no scope → the whole suite
- "coverage" mentioned → include the coverage flags in step 3
-
Verify pytest is available:
python -m pytest --versionIf missing, tell the user to run
pip install pytest pytest-covand stop. -
Run:
python -m pytest <scope> -v --tb=shortWith coverage:
python -m pytest <scope> --cov=src --cov-report=term-missing --cov-report=jsonFor a threshold summary, run
coverage_summary.pyfrom this skill directory against the generatedcoverage.json. -
Report:
- Pass / fail / skip counts, separately
- For each failure: the test name, the assertion that failed, the file and line
- Coverage percentage and which files fall below 80%, if coverage was run
-
If tests failed, do not fix them unless asked. Report first — the user may want to see the failure before it disappears.
Interpreting failures
Read the actual assertion before theorising. Common patterns, in rough order of frequency:
- Import errors — a dependency is missing, or the package isn't installed in editable mode. Check that before assuming the test is wrong.
- Fixture errors — the fixture is out of scope or missing from
conftest.py. - Assertion failures — a genuine behavioural difference. Report expected and actual verbatim; do not summarize them.
- Collection errors — a syntax error in the test file. Not a test failure at all.
Constraints
- Never modify a test to make it pass. If a test looks wrong, say so and explain why, then let the user decide.
- Never report success when tests were skipped. Skipped is not passed. Report the skip count separately and say why they skipped.
- Run the full suite before calling a change safe. A passing subset proves nothing about the rest.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 61 lines · 43 tokens per session scan A e744e16c654d
run-tests is a skill published in the GitHub repository JSchOBL/agentic-ai-learning-journey (3 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 532 once invoked, about $0.0002 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 2026-08-31.
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mvp-readiness
Runs a structured MVP quality-gate audit covering stability, security, logging, docs, and implementation integrity. Reports pass/fail with evidence.
requirements-generator
Generates structured requirements documents with functional and non-functional requirements, Gherkin acceptance criteria, edge cases, and out-of-scope items.