Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/event4u-app/agent-confignpx agentmods add skills/event4u-app/agent-config/image-analyserWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/event4u-app/agent-config/image-analyser)<a href="https://agentmods.dev/skills/event4u-app/agent-config/image-analyser"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/image-analyser/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/event4u-app/agent-config/image-analyser"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/image-analyser.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.01533 |
| Opus 5 | $0.00023 | $0.00766 |
| Sonnet 5 | $0.00009 | $0.00307 |
| Haiku 4.5 | $0.00005 | $0.00153 |
Grade A, and why
image-analyser scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-creatorand 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-creatorgenerates). - 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-analyzeshape). - 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-ocrcontract): refuse blurry / sub-resolution / unreadable inputs with a clear reason rather than guessing.
Procedure
- 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). - 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-featureasymmetry. - OCR sub-pass for lettered tattoos — read runic/block text exactly
(knuckle runes,
S-U-S-I, scalp runes, mic glyph), never approximate. - 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. - Emit the
observationlayer (Layer 2 incanon-spec.md): observed valueconfidence(high|medium|low) per feature +unverifiable[]. Confidence lives here, never written back onto the canon (Layer 1).
- 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, andlow-confidence misses flaggedneeds-better-image(not a hard fail). Emit concrete correction directives per miss.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 124 lines · 46 tokens per session scan A d1d8ebccd3fd
image-analyser is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 1,533 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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