Content
70%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.
This is a strong, expert-level instruction body: an unambiguous mode-selection table, a fully sequenced nine-step workflow with genuine statistical and governance guardrails, concrete commands and numbers, and explicit failure paths (NEEDS_INPUT, UNDECIDED, underpowered fallbacks). Its weaknesses are systematic internal repetition that inflates token cost, and a reference architecture that reaches outside its own bundle through multi-level cross-skill paths that cannot be verified from the skill as shipped.
Suggestions
Define the four modes once — keep the opening table and reduce step 3's mode bullets to only the binding/contract specifics not already stated in the table.
State the next-skill routing and the never-authorizes-a-send boundary once each (Next Best Skill and the scope guard respectively) instead of repeating them across the Skill Contract, Instructions, and Termination sections.
Ship the shared references inside the skill's own references/ directory (or summarize their operative rules inline) so the skill is self-contained when the surrounding repo layout is absent, and document the argument format for the `samplesize` and `continuous` subcommands.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and domain-specific, but the same content is repeated: the four modes are fully defined in the opening table and again in step 3's mode-specific bullets; the email-quality-auditor handoff is stated in the scope guard, the Skill Contract, and the Next Best Skill section; the never-authorizes-a-send boundary and the NEEDS_INPUT condition each appear three-plus times. Fits anchor 3 ("mostly efficient but could be tightened") rather than 4 because the duplication is systematic, not isolated; not 2 because there is no explanation of concepts Claude already knows and the domain policy content earns its tokens. | 3 / 5 |
Actionability | Gives a copy-paste-ready keyless command ("python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/experiment.py" proportion --control <events> <n> --variant <events> <n> --alpha <alpha> --min-lift <relative-bar>"), a fill-in hypothesis template, a numeric sample-size table, and an explicit duration formula. Not 5: the `samplesize` and `continuous` subcommands are named without argument formats, and the referenced script cannot be verified in this bundle; not 3: the primary workflows are fully executable with concrete numbers. | 4 / 5 |
Workflow Clarity | Nine numbered steps in a clear design→read-out sequence with explicit validation gates: stop-and-return NEEDS_INPUT when inputs are missing, the no-peeking rule fixing sample and read date at design time, the ordered underpowered fallback ladder in step 6 (widen MDE → run longer → fewer cells → don't test), receipt/hash binding before calculating, and the UNDECIDED decision rule absent a precommitted owner-approved rule. Not 4: checkpoints are explicit and include error-recovery paths, not implicit. | 5 / 5 |
Progressive Disclosure | Sections are well-organized and references are clearly signaled in a Reference Materials list with one-line descriptions, but no bundle files exist in this skill directory while the body depends on ~10 files reached via multi-level relative paths into shared repo space and other skills' bundles ("../../../references/skill-contract.md", "../../nurture/email-sequence-designer/references/send-control.md", "../../../influencer/report/roi-calculator/SKILL.md"), none verifiable here. Fits anchor 3 (structure present, organization could be better) rather than 4 because the skill's discoverability depends on an external repo layout with cross-skill hops rather than a one-level-deep local bundle. | 3 / 5 |
Total | 15 / 20 Passed |