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.
A well-structured orchestration skill: crisp read order, one-level-deep references, a fully sequenced 10-phase workflow with explicit validation and feedback loops, and concrete repo-specific details throughout. The two soft spots are mild editorial padding (KIE comparison, uni-1 nostalgia notes) and the absence of a fully spelled-out generator command for Phase 4.
Suggestions
Show one complete Phase 4 invocation per backend (e.g. `python skills/chatgpt-image-ad/scripts/generate_image.py --image-ref ref.png --aspect-ratio 1:1 ...`) so the core generation step is copy-paste ready instead of delegated to the sibling skill's docs.
Trim the KIE-repo comparison parenthetical and the uni-1-era narrative paragraph — both are historical context, not instructions; if kept at all, fold them into a short 'Deprecated / alternatives' note.
The two routing bullet lists (typography-heavy vs photoreal) overlap with Phase 1's auto-detect rule; consider compressing them into a single decision table keyed on the reference's typography-vs-photo balance.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with repo-specific facts Claude cannot infer (generator paths, aspect-ratio endpoint constraints, model-routing buckets, iteration layout) and explains no general concepts. Minor trim opportunities remain: the KIE-repo comparison parenthetical ("(The KIE per-API repo's...)") and the editorializing "The diff between a uni-1-era library and this one is this `Model notes` block. Don't skip it..." add tokens beyond instruction. This fits anchor 4 (efficient with minor instances that could be trimmed), not anchor 5 where every token earns its place. | 4 / 5 |
Actionability | Guidance is largely executable: exact script paths, env vars (`ARCADS_BASIC_AUTH`, `ARCADS_API_KEY`), the `--image-ref <reference_path>` flag, a concrete aspect-ratio mapping table, a copy-paste iteration directory layout, and fill-in markdown skeletons for `Model notes`. It stops short of anchor 5 because the actual generator invocation line (e.g. a full `python generate_image.py ...` command with the ratio flag) is never shown — Phase 4 says "using the matching generator script" and defers to the sibling skill — leaving a minor gap for the core execution step. | 4 / 5 |
Workflow Clarity | All 10 phases are explicitly sequenced with validation checkpoints and feedback loops: Phase 1 preflight ("Reference image file resolves; `.env` has Arcads creds; both generators detected" with a fail-and-fix-it path), Phase 5 iteration cap, Phase 7 re-testing the generalized template against a different brand with refinement if structure breaks, and Phase 8 cross-model validation with documented deltas. This matches anchor 5's clear sequence, explicit validation, and error-recovery loops; anchor 4 would leave checkpoints implicit. | 5 / 5 |
Progressive Disclosure | The body opens with an explicit numbered "Read order" placing this file as the Arcads-specific layer over the shared 10-phase guide, template format, and prompt library, and every reference is a well-signaled one-level-deep markdown link (shared guide, template-format.md, prompt-library.md, three sibling skills). Detail is appropriately split out — the body holds only routing rules, ratios, and layout, matching anchor 5's clear overview with well-signaled references; anchor 4 would have organization gaps or buried references. | 5 / 5 |
Total | 18 / 20 Passed |