CD carousel
How to make a multi-style multi-slide social carousel (5-10 slides × N styles) where the deliverable is the baked-text image itself — no HyperFrames compose. A domain overlay on the standard pipeline: supplies the cover-first checkpoint (one cover per style → user review → fill the rest), the dual-ref cohesion rule (`--ref mascot --ref {style}-01` on every fill slide), the mascot-fit rule (a clean cute mascot does NOT survive gritty/grunge styles — build a distressed character variant for those), the JSON prompt schema (reusable STYLE + QUALITY blocks per aesthetic, only scene + ui_elements varies per slide), and the `gpt-5.4-image-2` serialize-with-transient-retry batch shape. Works for ANY mascot / brand + ANY set of aesthetics — zine / club / swiss / riso / punk / acid / vaporwave / clean DTC. USE WHEN the user asks for an Instagram / LinkedIn / TikTok carousel, "5-slide deck", "swipe-through", "multi-slide post", "story-style series", "N-style carousel". Multi-slide stills, not video. This is a niche SKILL (generalized), not a remix TEMPLATE. For "remix this exact carousel but swap X", use the remix path in docs/skills-vs-templates.md. For a single still, use /poster.
How to make a multi-style multi-slide social carousel (5-10 slides × N styles) where the deliverable is the baked-text image itself — no HyperFrames compose.
As a process D 44/100 · Unfinished process — References files that are not bundled: ../poster/SKILL.md, ../fb-creatives/SKILL.md, ../../../docs/skills-vs-templates.md
The same skill appears in 1 more place: ralphy
How to improve
- Shorten the description to 1024 characters.
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1190 chars, limit 1024 - warning
missing-refreference to a missing file: ../poster/SKILL.md - warning
missing-refreference to a missing file: ../fb-creatives/SKILL.md - warning
missing-refreference to a missing file: ../../../docs/skills-vs-templates.md - warning
missing-refreference to a missing file: ../../../.agents/skills/art-director/SKILL.md - warning
missing-refreference to a missing file: ../../../.agents/skills/intake/SKILL.md - note
frontmatter-keyunknown frontmatter key "namespace"
Process rating: all ten parameters 44/100
- 0Tools and files. 5 referenced file(s) missing: ../poster/SKILL.md, ../fb-creatives/SKILL.md, ../../../docs/skills-vs-templates.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 7 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 70When it triggers. States when to use, but not when not to
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2849 tokens
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (8 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1190: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +4Structure: 14 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 48.