Content
50%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body has genuinely executable CLI guidance that matches the packaged script, but it is buried in a padded, repetitive document with circular cross-references and duplicated sections. Consolidating to one coherent workflow and moving config/format details into the existing reference file would substantially improve it.
Suggestions
Remove the boilerplate and duplication: the circular 'See ## X above' pointers, the repeated py_compile commands (Quick Check vs Audit-Ready Commands vs Example Usage), the two References sections, and the generic Output Requirements/Response Template filler — this could cut the body roughly in half.
Consolidate Example run plan, Workflow, Implementation Details, and Error Handling into a single sequenced workflow with explicit validation checkpoints (e.g. verify results.json is written and parses, check pass counts against thresholds before reporting).
Fix inaccurate examples: the `from semantic_consistency_auditor import ...` Python API snippet (the class lives in scripts/main.py), the hardcoded `cd "20260318/..."` path, and the `~/.openclaw/skills/...` config path versus the script's actual `--config` flag — and clearly signal references/audit-reference.md once, near the top.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The ~360-line body contains substantial padding: circular self-references ("See `## Prerequisites` above", "See `## Usage` above", "See `## Workflow` above" — pointing to sections that appear later), duplicated dependency listings, the same `py_compile` command repeated in three sections, two separate References sections, and generic filler (Output Requirements, Response Template, Input Validation) that adds no skill-specific value. This matches 'noticeably verbose; several unnecessary explanations or padded sections' rather than 1, since the algorithm/config/usage sections do carry real information. | 2 / 5 |
Actionability | The CLI examples (`python scripts/main.py --ai-generated ... --gold-standard ... --output results.json`, batch `--input-file`, model overrides) match the actual argparse definitions in scripts/main.py and are executable, as are the config YAML and documented input/output JSON formats. Minor gaps keep it below 5: the Python API example imports `semantic_consistency_auditor` as a package though only `scripts/main.py` exists, and the Example Usage hardcodes a nonexistent path (`cd "20260318/scientific-skills/..."`). | 4 / 5 |
Workflow Clarity | Steps exist (Example run plan, Workflow, Error Handling) but are scattered across three overlapping sections with circular cross-references, and the batch-evaluation workflow lacks result-level validation checkpoints. Per the rubric, a batch operation without validation is capped at 3, which is also where 'steps listed but validation gaps' fits; a 4 would require one coherent sequence with most checkpoints explicit. | 3 / 5 |
Progressive Disclosure | The body has section structure and one one-level-deep reference (references/audit-reference.md, which exists), but that reference is only linked at the end in a second 'References' section and duplicates SKILL.md boilerplate, while content that belongs in separate files (config docs, input/output JSON schemas, Python API details) is inlined in a 360-line monolithic body. This fits 'references present but not clearly signaled; content that should be separate is inline'. | 3 / 5 |
Total | 12 / 20 Passed |