Content
77%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 actionable, operationally validated skill body with explicit workflows, checkpoints, and error-recovery loops for a fragile paid operation. Its weaknesses are length and redundancy (including dated pricing and contradictory guidance on AI-gen video input) and a monolithic layout where the prompt template and failure tables should live in bundled reference files.
Suggestions
Reconcile the direct contradiction between Decision Rule 1 ('NEVER pass AI-generated video as video_urls... rejects with partner_validation_failed') and Rule 0e ('FAL DOES accept AI-gen video as input'), or fold the superseded rule into a clearly marked 'deprecated patterns' section.
Move the full prompt template (and optionally the long failure-mode table) into a bundled references/ file (e.g. references/prompt-template.md) and verify cited paths like prompt-example.md and DELIVERY_STYLES.md exist relative to the skill.
Trim repeated validation-date stamps and 'Memory:' citations to a single line each, and mark the dated pricing table as versioned/as-of info to reduce token load.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with validated, non-obvious operational knowledge Claude would not know, so most tokens earn their place — but at 263 lines it could be tightened: dated time-sensitive info ('Pricing (2026-05)', many 'Validated 2026-05-23' stamps), repeated 'Memory: feedback_*.md' citations, very long failure-mode table rows, and an internal contradiction (Rule 1 'NEVER pass AI-generated video as video_urls' vs Rule 0e 'FAL DOES accept AI-gen video as input... the old assumption is outdated'). This is 'mostly efficient but includes some unnecessary material and could be tightened' rather than efficient. | 3 / 5 |
Actionability | Fully executable throughout: a copy-paste-ready CLI invocation with concrete values, the exact JSON payload passed to the endpoint, exit codes enumerated, a complete fill-in prompt template, per-failure recovery actions, and a concrete curl command for recovery ('curl -sSL -o <output>.mp4'). Specific examples cover the common cases (identity lock via image_urls, NSFW surfacing, timeout recovery). | 5 / 5 |
Workflow Clarity | Multi-step processes are explicitly sequenced with validation checkpoints and feedback loops: a preflight requiring user review before the paid call, the script's numbered behavior with exit-code semantics, a Quality Checks checklist for output verification, a failure-mode table mapping failure to cause to recovery, and a 6-step recovery workflow with a prevention step. Not a level below: checkpoints are explicit, error recovery is looped ('DO NOT re-fire', 'retry once'), and the risky paid operation has approval gates. | 5 / 5 |
Progressive Disclosure | Structure and headers are good, but content that clearly belongs in separate reference files is inlined: the ~50-line full prompt template sits in SKILL.md while simultaneously pointing to 'prompt-example.md' at the repo root (not present in the bundle), and the very long failure-mode and decision-rule sections are candidates for reference files. The actual bundle contains only scripts/generate.py and scripts/media_proxy.py — there is no references/ directory — so several cited paths (prompt-example.md, beauty-by-earth/... working scripts, DELIVERY_STYLES.md) resolve outside or not at all in the bundle. This matches 'some structure; content that should be separate is inline; references present but not clearly signaled'. | 3 / 5 |
Total | 16 / 20 Passed |