Content
85%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.
An exceptionally well-engineered skill body: concrete commands and thresholds, explicitly sequenced setup and audit flows with real validation discipline, and a verified, cleanly split reference bundle. The one real weakness is verbal economy — the chatty, editorial prose style and some repetition could cut the body's token cost substantially without losing any guidance value.
Suggestions
Tighten Non-negotiable #3 ('Say how you know'): keep the Observed/Reported/Inferred taxonomy and the no-fake-citations rule, but cut the digression about search summaries and the 'research's back' riff, saving roughly a third of the section.
State the partner-disclosure rule once (in 'Choosing tools') and reference it in one line from Step 3 / the description-side guidance, instead of restating the full wording and rationale in multiple places.
Trim rhetorical flourishes that add tokens but not instruction (e.g., 'a wizard, not an agent', 'is a perfectly good sentence', 'nobody can catch from reading your output') to short declarative equivalents.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body contains no basic-concept padding and is dense with genuine domain judgment, but the prose is consistently chatty and could be tightened significantly: editorial asides ("A skill that interrogates the user for facts it could have observed is a wizard, not an agent"), a long digression inside Non-negotiable #3 ("'search summaries' you never ran are as invented as links you never clicked... the honest version of 'research's back'"), and the partner-disclosure rule stated in full twice. This fits the 3 anchor ('mostly efficient but could be tightened') better than 4, since the over-writing is more than minor. | 3 / 5 |
Actionability | Guidance is concrete and executable throughout: an exact script invocation ("python3 scripts/build_recipe.py --tool {tool} --send {platform} --{ids}"), named verification checks with expected results ("open Meta Events Manager, go to Test Events, and you should see one Lead appear within a minute"), numeric decision thresholds (10-30% normal divergence, ~40% worth investigating, 72-hour conversion lag), and verbatim required wording for the partner disclosure. Specific examples cover the common cases, matching the 5 anchor. | 5 / 5 |
Workflow Clarity | Both flows are clearly sequenced with explicit validation: a six-step setup flow ending in a mandatory verification step and handover, a symptom-routing table for audits, an ordered evidence sweep, and a pass/fail/unknown/not-applicable scoring rule where unknown never counts against health. The Verification discipline section supplies feedback loops (fast surfaces like DebugView and Test Events, hygiene markers, close-with-status-not-verdict), matching the 5 anchor's explicit validation and error-recovery loops. | 5 / 5 |
Progressive Disclosure | The body is a decision-logic overview with a task-to-reference routing table ("Where everything is"), and every referenced path was verified to exist: all 9 references/*.md files, assets/snippets/ (26 .js files matching the claimed 22 lead-gen tools plus four universal patterns), assets/gtm-recipes/ with README.md and event-map.json, and scripts/build_recipe.py. References are one level deep, well-signaled, and loaded per task ("Load a reference when the task reaches it"), matching the 5 anchor. | 5 / 5 |
Total | 18 / 20 Passed |