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ArtDirection

Assess the visual quality of images, films, slides, and site captures against a brief and craft criteria. Two vision judges score the work and rank defects, guiding bounded revision; delivery is gated on a passing critique, with human calibration labels recorded separately.

OriginalShow the original description

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/LifeOS19,635 stars(GitHub 19,635 + cccskills 0)
Works withOpenAI

Run score Not tested

See how to install

Good for

  • Reviewing a render and listing the most important visual defects
  • Iterating on a film or slide deck until it meets a defined bar
  • Checking whether an image reads as professional
  • Delivering an artifact with a passing critique report

Overview

What it does

ArtDirection evaluates images, films, slides, and site captures against a brief that records the audience, references, visual direction, and quality bar. A direction council resolves the visual intent into Direction.md. The skill then uses vision judges to score the work against craft criteria and list fixable defects in priority order. If revisions are needed, the critique can run for up to four rounds and report whether the work meets the bar or what remains. Only work with a passing critique can proceed to delivery. Human scores and comments can also be recorded as labels for later judge calibration.

When it helps

Use it to review the appearance of an image or video before publication, turn a visual critique into a prioritized list of changes, or hand over approved work together with its evaluation record.

Example requests

  • “Evaluate this render and tell me what to fix first.”
  • “Repeat the film critique and revision cycle until it meets the bar.”
  • “Score this work 7 and record that the text feels cramped.”

Notes

The judge vendors must be configured. First-time setup for films also requires ffmpeg. ArtDirection supports quality evaluation and delivery after production; it does not create the image, film, slide, or site capture. It is not for writing audits or evaluating and rendering an ISA.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Document check

AI checked the SKILL.md for operations that need care and for setup requirements. The score comes only from actually running the skill (hands-on test).

SafetyAre sending data, deleting files, and running commands disclosed?
AGoodDelivery requires a pass, and credentials are prohibited in briefs and logs.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingJudge vendor access and `ffmpeg` are required; setup is deferred to `Setup.md`.

Not counted in the score. Rated by AI (OpenAI Codex) as of Oct 28, 2026. Treat it as a guide and read the original before installing.

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Files included (12)

  • SKILL.md7.4 KB
  • Intents/Calibrate.md964 B
  • Intents/Critique.md4.7 KB
  • Intents/Deliver.md1.4 KB
  • Intents/Direction.md1.4 KB
  • Intents/Loop.md2.0 KB
  • References/BriefExample.json982 B
  • References/Rubric.md7.3 KB
  • Setup.md3.7 KB
  • Tools/Calibrate.ts4.5 KB
  • Tools/Critique.ts73.0 KB
  • Tools/Deliver.ts15.9 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

ArtDirection

A shared quality loop for artistic deliverables: the Brief defines audience, register, references and bar; a Direction council resolves the visual intent into Direction.md; the domain skill produces the artifact against it; Critique yields two vendors' scores and ranked defects; iteration reaches the bar or reports the shortfall at four rounds; only a passing result reaches Deliver; and Calibrate records the principal's own verdict as evidence for later judge calibration. The domain skill makes the artifact; this skill supplies the quality evidence.

Ideal State

  • Nothing artistic reaches the principal or a public surface without a current passing critique from the judge panel bound to the exact bytes delivered. The panel is cross-vendor whenever two approved vendors are reachable; a one-vendor panel may still pass, and its report carries the vendor_missing flag as weaker evidence.
  • Every verdict is honest: pass only when the pooled median reaches the bar and each vendor median reaches bar minus 0.5, with no UNKNOWN score or critical defect, a cap hit reported with its shortfall, and an unreachable vendor reported as unavailable rather than as a quality failure.
  • Each round names ranked, located defects a producer can fix, judged against the concept-first rubric rather than adjectives.
  • A figure's anatomy and mechanics are judged as a defect class: extra or missing limbs, a grip or pose that does not match the object held, or physically impossible motion is a critical defect located by image region or frame, so the critique fails it; the judges read this rule from References/Rubric.md.
  • The principal's own scores are kept verbatim as labels, and judges change only through a deliberate calibration, never automatically.

Intent Routing

IntentTriggerFile
LoopFull quality loop or iterate to the barIntents/Loop.md
DirectionArt council or new visual directionIntents/Direction.md
CritiqueRate the visuals or inspect a renderIntents/Critique.md
DeliverHand over a passing artifactIntents/Deliver.md
CalibrateRecord the principal's score and noteIntents/Calibrate.md
FileRead when
References/Rubric.mdwriting a brief or reading a critique: the anchored axes, document and figure rules, defect shape and the Brief.json schema (References/BriefExample.json is a filled brief); Critique reads it at runtime
Setup.mdfirst-time setup: the two judge vendors' access, ffmpeg for films, and a no-cost proof

Tool contract

Run with bun skills/ArtDirection/ prefixed to each tool path, from the config root.

ToolCLI contract
CritiqueTools/Critique.ts --video <mp4> | --image <png> [--images <glob>] --brief <Brief.json> [--bar 8.5] [--samples 12] [--concurrency N] [--vendors openai,anthropic] [--sample-seed <n>] [--out <dir>] [--json] [--allow-identity]
DeliverTools/Deliver.ts --artifact <file> [--critique <Critique.json>] --to <dir>
CalibrateTools/Calibrate.ts --artifact <file> --verdict <1-10> --note "<text>"

Each intent file carries its tool's defaults, refusals and exit codes.

Data & Config

User customizations live at <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ArtDirection/ (configRoot is $CLAUDE_CONFIG_DIR when set, else ~/.claude). When it exists, its PREFERENCES.md, configurations and resources override the defaults here; when absent, the defaults stand. First-time setup (accounts, keys, config, a proof it works) is in Setup.md.

  • A brief may name "calibration": "<target>". Critique then adds that Calibrate target's Taste.json (the principal's labeled likes and dislikes) to the judge prompt and records its labels_hash in the report; a target with no profile yet is noted under outstanding. Briefs without the field produce the prompt they always did.
  • Calibration labels append to ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ArtDirection/labels.jsonl (fields in Intents/Calibrate.md).
  • The panel is the vision vendors named by art_judges in <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ArtDirection/Config.md (OpenAI, Anthropic and Google have seats; a missing file or key stops the critique with an error naming both). That folder's PREFERENCES.md, when present, is appended to References/Rubric.md as install-specific judging rules, and check_set.version hashes the two together. OpenAI vision runs as codex exec -i through the codex CLI signed in to ChatGPT (Assay/Tools/OpenAIVision.ts; no API key is read); Anthropic vision runs through LIFEOS/TOOLS/Inference.ts image input (the claude CLI on the subscription), never falling back to another vendor. Panel selection (--vendors) is in Intents/Critique.md. No credentials belong in briefs or logs.
  • Models come from LIFEOS/TOOLS/models.ts: CROSS_VENDOR.openaiJudge for OpenAI and the high rung of EFFORT_MODEL for Anthropic, never pinned here.
  • Preferences use configRoot above; the current credential and label implementations use the home .claude location.

Gotchas

  • Hand-over is gated by Deliver.ts because a first-draft video reached the principal with no critique on 2026-09-17, while the rule lived only in prose (the story is in the USER extension's History.md).
  • The rubric leads with concept (does the artifact carry its idea as one metaphor a stranger reads at a glance), and overall follows concept most. Changed 2026-09-28 after the principal's first Calibrate scores showed the old craft-first rubric ranking the scored artifacts almost at random (the measurement is in the USER extension's History.md).

Examples

User: "iterate until it's great" on a render → Loop intent → Brief.json and Direction.md exist, critique rounds run until the artifact passes the bar, or the four-round cap is reported with its shortfall.

User: "rate the visuals" → Critique intent → Critique.json with both vendors' scores, ranked defects and a pass, fail or unavailable verdict.

User: "that one's a 6, the type is cramped" → Calibrate intent → one label appended with verdict 6 and the note in the principal's words.

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