Image Analyser

image-analyser

Read a character image, extract every feature (face marks, per-location tattoos incl. lettered text, exact hair split, per-eye colour, jewelry, asymmetry), and diff it against the character's canon so drift is caught before it ships. Output feeds image-creator and the fidelity loop. Schema + rubric + loop: canon-spec.md.

When to use

• "Analyse this image / character", "does this match the canon", "check character accuracy", "find what's wrong with this render".
• As the verify step of the fidelity loop (after image-creator generates).
• To bootstrap a Canon Spec from an authoritative portrait (the image wins over the text).

NOT for: scene/motion review (→ video-director), non-character art (→ canvas-design), cross-scene token locking (→ character-consistency, which consumes this skill's output).

Input

• Image path or public URL (per the vision-analyze shape).
• Optional: a reference Canon Spec / character id (e.g. agents/reference/ai-video/<project>/characters/<id>.json) to diff against.
• Input gate (per the image-ocr contract): refuse blurry / sub-resolution / unreadable inputs with a clear reason rather than guessing.

Procedure

1. Read the image. A vision-capable model views it directly. No new dependency; if a cloud-vision/OCR backend is wanted, ask first (missing-tool-handling).
2. Section-by-section extraction (the "down to the smallest mole" pass) — one pass per section: physique, face (+ marks/scars/moles), hair (colour, split line, length, braids, shaved areas), eyes (per-eye colour, heterochromia, ring, kohl), tattoos (per body location: motif, style, and text if lettered), jewelry, outfit, cross-feature asymmetry.
3. OCR sub-pass for lettered tattoos — read runic/block text exactly (knuckle runes, S-U-S-I, scalp runes, mic glyph), never approximate.
4. Hard-feature enhancement — for a faint mole, an unclear hair-split line, or heterochromia in shadow: re-pass on a crop/zoom of that region before marking it. Only then mark genuinely unresolvable features unverifiable.
5. Emit the observation layer (Layer 2 in canon-spec.md): observed value
   • confidence (high|medium|low) per feature + unverifiable[]. Confidence lives here, never written back onto the canon (Layer 1).
6. If a reference is given — diff + score per the rubric: per-feature match|partial|miss, the canon-breaking hard gate, per-section scores, advisory roll-up, and low-confidence misses flagged needs-better-image (not a hard fail). Emit concrete correction directives per miss.

The one rule that overrides everything

The image wins over the text. When extracting from an authoritative portrait and the canon text disagrees, record what is visible. When verifying a candidate against the canon, the canon's identity is the truth. Never invent a feature the image does not show (per direct-answers — no invented facts); mark it unverifiable instead.

Output format

1. Observation JSON (Layer 2) — observed features + per-feature confidence + unverifiable[].
2. Diff table (only if a reference was given): feature · severity · expected · observed · verdict (match/partial/miss) · confidence · fix.
3. Verdict line: GATE: pass|FAIL (canon-breaking misses: …) + per-section scores + advisory roll-up.

Example (safe vs unsafe)

• Safe: eyes — canon-breaking — expected blue-left/green-right — observed both blue — MISS (high) — fix: regenerate with heterochromia anchor front-loaded.
• Unsafe: reporting eyes — match when the green eye is out of frame. If unseen → unverifiable, not match.

Gotchas

• Hands/knuckles often out of frame → tattoo text unverifiable, not a miss.
• A strong face must not mask a broken hair split — that is why scores are per-section, not one number.
• Symmetric characters (Sigrún, Bjørn) vs the asymmetric one (Veikko): check the left/right invariant explicitly for the Loki-marked character.

Do NOT

• Do NOT score an unseen feature as match — if it is out of frame or unresolvable, mark it unverifiable (per direct-answers, no invented facts).
• Do NOT write confidence / unverifiable back onto the canon (Layer 1) — they are the analyser's epistemic state (Layer 2) and never mutate the truth layer.
• Do NOT collapse the rubric to a single number — a strong face must never mask a canon-breaking hair/eye miss; scores stay per-section with a hard gate.
• Do NOT approximate lettered tattoo text — OCR it exactly or mark it unverifiable.
• Do NOT analyse a real-person likeness without routing through media-governance-routing first.

Policies

Character images can carry a real person's likeness. Before analysing a real-person likeness, route through media-governance-routing and consult agents/settings/policies/media/likeness.md + public-figures.md. Fictional characters (e.g. the odins-beard trio) are exempt; the routing decision is the agent's, in-session.

Related skills

• image-creator — consumes the diff; the loop partner.
• character-consistency — consumes the load-bearing token subset of the identity layer.
• canon-spec.md — schema, rubric, fidelity loop.
• screenshot-hygiene — reuses this skill's OCR text-read to detect sensitive data in a documentation screenshot before it ships.