Content
78%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 advisory skill: executable commands with embedded samples, four clearly sequenced workflows with cross-skill checkpoints, and exemplary progressive disclosure verified against real bundle files. The main inefficiency is a keyword-stuffed section and duplicate reference descriptions that pad the token budget without adding action guidance.
Suggestions
Trim the ~60-term Keywords section to the handful of terms not already covered by the description's trigger clause (e.g., 'pooled CSM', 'book of business'), or drop it entirely — it duplicates discovery work the description already does.
Collapse the bottom References section into the inline 'See references/...' notes (or vice versa) so each reference is described once instead of twice with near-identical text.
Add an explicit input-validation step to the workflows (e.g., 'confirm cohort data covers 8 quarters before decomposing') to upgrade implicit checkpoints to explicit ones.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient — the tables carry genuinely non-obvious domain data (GRR ≥ 90%/95% thresholds, ARR-per-CSM ratio bands, role ownership distinctions) that Claude does not already know. But it could be tightened: the ~60-term Keywords paragraph ('CCO, chief customer officer, customer success, retention strategy, gross retention...') is trigger stuffing that largely duplicates the description, and each reference is described twice (inline 'See references/...' notes plus a bottom References section with near-identical descriptions). | 3 / 5 |
Actionability | Quick Start gives copy-paste commands that run with embedded sample data ('python scripts/retention_decomposition_analyzer.py # embedded B2B SaaS sample'), all three scripts exist in the bundle, and each of the four workflows specifies concrete inputs, steps, and outputs ('Build customers.json with ARR, tenure, ICP fit signals' → 'Output: new tier assignment + investment-per-tier + kill list'). The Output Standards template makes the deliverable format unambiguous. | 5 / 5 |
Workflow Clarity | Four workflows with clear numbered sequences, stated goals, and cross-check checkpoints ('Cross-check with cs-cro-advisor: does the expansion math add up?', 'Cross-check with cs-chro-advisor on comp + leveling') that serve as review gates. These are advisory analysis workflows, so the destructive/batch validation cap does not apply, but explicit input-data validation (e.g., verifying cohort coverage or data completeness before decomposing) is only implicit — anchor 4's 'minor validation gaps' rather than anchor 5's explicit validation steps. | 4 / 5 |
Progressive Disclosure | Verified against the actual bundle: all four referenced files exist (references/retention_decomposition.md, customer_segmentation_strategy.md, cs_coverage_model.md, cs_team_org_evolution.md) and all three scripts exist. The body keeps decision-level tables inline and pushes depth (7-category churn taxonomy, ramp curves, stage-to-role maps) to clearly signaled one-level-deep references, with each section naming what the reference contains. No nested content references within the bundle. | 5 / 5 |
Total | 17 / 20 Passed |