Conciseness

The body is lean with zero concept explanations: every section (Setup, Workflow, Safety Rules, Output) adds information Claude does not already know, such as 'Do not include model; the CLI inserts the selected model ID after validation' and 'Treat model availability and prices as live data'. Not 4: there is no padding that could be trimmed — even the stdin alternative example demonstrates a distinct, useful invocation form.

Actionability

All commands are copy-paste executable and verified against the bundled script (models/describe/generate with --type, --query, --params-file, --confirm-paid flags all exist in scripts/atlas_media.py), including a concrete params JSON example and a stdin variant. Not 4: the examples cover the common cases end-to-end and deliberately defer field enumeration to the live 'describe' output, which is the correct executable design rather than a gap.

Workflow Clarity

The four-step workflow (discover → inspect schema → prepare params → confirm cost and generate once) has explicit validation checkpoints for a paid, risky operation: mandatory schema inspection before payload building ('Build the payload from this output'), the mandatory --confirm-paid gate, bounded polling ('stop at --max-polls'), and an error-recovery rule ('If submission fails, report the error and require a new explicit confirmation before another paid request'). Not 4: checkpoints are explicit, not implicit, and failure handling is spelled out.

Progressive Disclosure

The SKILL.md is a clear overview with all implementation detail (a 359-line CLI) appropriately split into the single, real, one-level-deep bundle file scripts/atlas_media.py, referenced by name in every command, and the body itself is organized into well-labeled sections. Not 4: nothing that belongs in a separate file is inlined and navigation is trivial; the single reference is real and clearly signaled.

Specificity

The description lists multiple concrete actions — 'Discover Atlas Cloud image and video models, inspect their live schemas, and submit one confirmed media generation request with bounded GET polling' — giving comprehensive coverage of the skill's capabilities with no filler. Not 4: there are no minor gaps; discovery, schema inspection, submission, and polling are all enumerated.

Completeness

Both 'what' (discover models, inspect live schemas, submit one confirmed request, bounded GET polling) and an explicit 'Use when...' clause with two concrete triggers ('integrating Atlas Cloud media APIs', 'generating images and videos without hard-coding stale model parameters') are present. Not 4: the 'when' is explicit and specific, not merely implied or vague.

Trigger Term Quality

Good natural-term coverage: 'Atlas Cloud', 'image and video models', 'media APIs', 'generating images and videos', 'model parameters' are phrases a user would plausibly say. Not 5: a few common synonyms are missing (e.g. 'text-to-image', 'video generation', model-name triggers like 'flux' or 'seedance'); not 3: coverage goes well beyond a single generic keyword.

Distinctiveness Conflict Risk

It occupies a clear niche — Atlas Cloud's media generation API specifically — with triggers ('Atlas Cloud media APIs', 'Atlas Cloud image and video models') that would not fire for generic image/video or other-provider skills. Not 4: no meaningful overlap with closely related skills exists since the provider name disambiguates.