Content
63%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.
A well-structured, information-dense reference for attribution modeling with genuinely useful platform-specific data and concrete workflows. Its weaknesses are token efficiency (textbook model definitions and a promotional footer) and the absence of any progressive disclosure — everything is inlined in one long file with no executable tooling for the computational steps.
Suggestions
Move the Attribution Models Reference, Reference Data tables (lookback windows, platform defaults, typical credit shifts), and Examples into separate files under references/ with clearly signaled links, keeping SKILL.md as a concise overview with the workflows.
Trim the per-model 'Strengths/Weaknesses' textbook commentary for standard models (first-touch, last-touch, linear, time-decay, position-based) that Claude already knows, and remove the promotional footer links.
Add an executable script or concrete calculation method for the Markov chain workflow (transition matrix construction and absorption computation) and include explicit validation checkpoints (e.g., verify normalized attribution weights sum to 100%, verify reconciled totals match GA4).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body spends ~80 lines restating textbook definitions of standard attribution models ("100% credit → first interaction", "Strengths: Simple, highlights discovery channels") that Claude already knows, and the promotional footer ("Try Synter — AI Agent Media Buyers...") is pure padding. The platform-specific data (lookback defaults, DDA thresholds, GA4 navigation) does earn its place, keeping this at 'mostly efficient with some unnecessary explanation' rather than 'noticeably verbose'. | 3 / 5 |
Actionability | Guidance is concrete: exact GA4 paths ("Admin → Attribution Settings"), formulas, numeric thresholds ("3,000 ad interactions + 300 conversions in 30 days"), and interpretation tables (assist ratio bands). Not a 5 because steps like "Apply each model to the same dataset" and "recalculate conversion probability using matrix absorption" give no executable method or tooling to actually compute the results. | 4 / 5 |
Workflow Clarity | Three workflows with clear numbered sequences and useful interpretation rules ("If Channel A gets 40% on last-touch but 15% on first-touch → it's a closer"), plus mild checkpoints ("Monitor for 2-4 weeks", "Total matches GA4: 350"). Not a 5 because no explicit validate-then-proceed loops exist; not a 3 because the sequences are well-defined and operations are read-only analysis, so the destructive/batch cap does not apply. | 4 / 5 |
Progressive Disclosure | A single ~300-line monolithic file with no bundle files at all; the Attribution Models Reference, Reference Data tables (lookback windows, platform defaults), and Examples sections are inline content that would fit better in separate reference files. Section headers are clear, matching 'some structure but could be better organized; content that should be separate is inline'. | 3 / 5 |
Total | 14 / 20 Passed |