Content
81%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, highly actionable decision-support skill: a gated 5-step workflow with deterministic tier floors, validated GTM planning rules, executable stdlib scripts with working --sample paths, and a real one-level-deep reference bundle. Its weaknesses are mild: routing information is duplicated between 'Do not use for' and 'Distinct from', the forcing-question library is inlined rather than referenced, and the provenance of the gtm.json/revshare.json inputs is left implicit.
Suggestions
Merge 'Do not use for' and 'Distinct from' into a single routing section — they restate the same five re-routes (sales-engineer, channel-economics, cro-advisor, ma-playbook, deal-desk) and cost ~15 duplicated lines.
Specify the input handoff for Steps 3-4: where gtm.json and revshare.json come from (derived from the filled intake template?) and one short example invocation with sample input/output, or point explicitly at --sample output for each script.
Move the ~50-line forcing-question library into a references/ file (e.g., references/forcing_questions.md) and keep a one-line pointer plus the lock-1-3-before-4-7 rule in SKILL.md, slimming the always-loaded context.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Efficient and opinionated with no padding over concepts Claude already knows — every section (tier floors, anti-patterns, assumptions) adds domain-specific judgment. Minor over-explanation exists in the duplicated routing: "Do not use for" and "Distinct from" restate the same five re-routes (sales-engineer, channel-economics, cro-advisor, ma-playbook, deal-desk), and the 50-line forcing-question library runs long with per-question citation paragraphs. Not 5 because of that redundancy; not 3 because nothing is generic filler. | 4 / 5 |
Actionability | Concrete, runnable commands throughout: `scripts/partner_tier_classifier.py --input intake.json --profile saas --output markdown`, a copy-paste `python3 scripts/partner_tier_classifier.py --sample` quick example, and noted `--help`/`--sample` on all scripts, with all three scripts present in the bundle. Minor gaps keep it from 5: the intake.json → gtm.json → revshare.json handoff (where the second and third input files come from) is never specified, and no example input/output is shown inline. | 4 / 5 |
Workflow Clarity | A clearly sequenced 5-step workflow with explicit validation gates: Step 1 has a stop condition ("If the intake template can't be honestly filled out... Stop. Go back to them."), Step 2 has deterministic floors ("STRATEGIC requires named_accounts ≥ 5 AND multi-year commit AND dedicated resources"), Steps 3-4 enforce tier-consistency rules ("cannot plan channel-led GTM for REFERRAL tier"), and the forcing-question library mandates locking questions 1-3 before opening 4-6 before invoking the scripts in sequence, plus mandatory kill criteria. Not 4 because checkpoints are explicit, not implicit; the missing 'verify script output' loop is immaterial for a decision-support skill with no destructive or batch operations. | 5 / 5 |
Progressive Disclosure | Good structure: SKILL.md is an overview with a Scripts section, a References section where each of the three reference files gets a one-line content summary and exists as verified one-level-deep standalone content, plus an assets intake template. Minor gaps: the ~50-line forcing-question library is fully inlined in SKILL.md where the reference files' style would suit it, and reference descriptions are dense author-citation lists rather than clear per-topic navigation signposts. Not 5 because of the inlined question library; not 3 because all heavy content (canon, anti-patterns, script logic) is properly split out. | 4 / 5 |
Total | 17 / 20 Passed |