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BC ArtDirection

Directs visual quality across images, films, slides, and site captures through a direction council, two vendors' vision judgments against anchored craft criteria, ranked defects, bounded iteration, gated delivery, and recorded human calibration labels. USE WHEN critique this, art direction, quality loop, rate the visuals, is this pro, iterate until it's great, deliver the render, art council. NOT FOR producing the artifact itself (use the appropriate image, motion, story-film, video-essay, web-design, or slide-production capability), for text writing audits, or for rating and rendering an ISA (ISA).

danielmiessler/Personal_AI_Infrastructure Agent Skills author: danielmiessler 12 files · 3 scripts body ≈ 1 566 tokens Open the sourcegithub.com↗ analyzed 28 h ago

Directs visual quality across images, films, slides, and site captures through a direction council, two vendors' vision judgments against anchored craft…

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerMedia and videoWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "evals"

    Process rating: all ten parameters 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 12 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1566 tokens
    • low The response is described with custom markup (10 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 606: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.