Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with strong actionability and workflow clarity. The quick start provides a clear, validated sequence from installation through test generation and CI integration, with a concrete real-world example. Minor verbosity in some descriptions prevents a perfect conciseness score, but overall the content is efficient and well-organized with appropriate progressive disclosure to REFERENCE.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient but includes some unnecessary context like explaining what the demo dataset contains ('51 traces across 6 failure modes') and some verbose descriptions. The common errors section is useful but could be tighter. The example section earns its place but the surrounding prose has minor padding. | 2 / 3 |
Actionability | Provides fully executable commands at every step: pip install, env vars, ingestion script, pytest validation, uvicorn launch. The example shows concrete input/output/label and the resulting pytest code with real assertions. Copy-paste ready throughout. | 3 / 3 |
Workflow Clarity | Clear 7-step sequence with explicit validation checkpoints: step 4 sanity-checks the dataset before generation, step 6 explicitly validates the generated test by confirming it fails against bad output and passes after a fix. This is a proper feedback loop for a generative pipeline. | 3 / 3 |
Progressive Disclosure | Clean overview in SKILL.md with detailed content explicitly delegated to REFERENCE.md (pipeline architecture, schema reference, ingestion flags, web UI internals, Cloud Run deploy). References are one level deep and clearly signaled at both the top and bottom of the file. | 3 / 3 |
Total | 11 / 12 Passed |