Content
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is well-structured and domain-aware with named tools and a meaningful honesty guardrail, but it is verbose with re-taught concepts, light on executable code, and lacks validation feedback loops and external reference files. It sits solidly at the mid-level anchors across dimensions.
Suggestions
Trim sections that re-explain well-known concepts (I² definition, fixed/random-effects, winner's curse, interpretation thresholds) to lean operational guidance that assumes Claude's statistics knowledge.
Add a copy-paste-ready Python snippet for inverse-variance meta-analysis (beta + 95% CI pooling, Cochran's Q / I²) so the core computation is executable rather than described.
Introduce explicit validation checkpoints in the meta-analysis and replication workflows (e.g. 'verify effect sizes present before pooling; if absent, fall back to descriptive mode and stop') and move deep reference material into referenced files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Much of the body is efficient operational guidance, but lengthy sections restate domain knowledge Claude already knows — e.g. defining I², fixed- vs random-effects models, winner's curse, and interpretation thresholds — that could be trimmed. | 2 / 3 |
Actionability | It names concrete tools and gives one executable formula block, but the meta-analysis workflow lacks copy-paste code (no Python snippet for inverse-variance pooling) and relies on prose/pseudocode-level steps for the actual computation. | 2 / 3 |
Workflow Clarity | Use cases are sequenced as ordered steps, but there are no explicit validation checkpoints or feedback loops for the risky meta-analysis operations; the 'Honesty rule' is an honesty caveat rather than a validate-then-proceed loop. | 2 / 3 |
Progressive Disclosure | Sections are well organized with a clear TOC-like structure, but it is a single monolithic SKILL.md with no references/ bundle files and no signaled pointers to deeper material, so reference depth is not exercised. | 2 / 3 |
Total | 8 / 12 Passed |