Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentara/skills --skill portrait-clonegit clone --depth 1 https://github.com/agentara/skillsWrote 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/agentara/skills/portrait-clone)<a href="https://agentmods.dev/skills/agentara/skills/portrait-clone"><img src="https://agentmods.dev/badge/skills/agentara/skills/portrait-clone/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/agentara/skills/portrait-clone"><img src="https://agentmods.dev/badge/skills/agentara/skills/portrait-clone.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 | $0.00080 | $0.04848 |
| Opus 5.5 | $0.00032 | $0.01939 |
| Sonnet 5.5 | $0.00016 | $0.00970 |
| Haiku 4.5 | $0.00008 | $0.00485 |
Grade A, and why
portrait-clone scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Eyes** (highest drift risk — geometric): crease type and height, epicanthic fold, shape, tilt, size relative to face, height/width ratio, lid coverage, iris visibility fraction, open/blink state, smile deformation, i How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portrait Clone
Convert n reference images (at least one contains a person) into ONE AIGC image prompt in JSON.
The single goal: pin down every variable an image model could otherwise choose, so the same prompt yields a nearly identical, non-AI-looking image on every run. Any detail left unspecified is a detail the model will randomize. Stability is measured by how little freedom the model has left.
Non-negotiables
- Lock everything. Every visible or inferable attribute gets exactly one concrete value. Never write "or", ranges ("20-30"), "e.g.", "optional", "[slot]", "various", "some", "natural-looking" without specifics, or any alternative.
- Lock absence too. Anything a model might add but the reference does not contain is stated explicitly as
"none"(glasses, hat, earrings, bag, second person, text, logos, props, sweat, tattoos elsewhere) and also listed innegative_prompt. - Fields are open-ended. The Schema below is a minimum floor, not a limit. Add any key, nested object or array needed to pin a variable. Prefer splitting one vague field into several precise sub-fields. More locked fields is always better than fewer.
- Always de-slopped. Every output, version and revision applies the full De-slop step, regardless of the reference's style, medium or polish.
- JSON only. Reply with exactly one ```json code block and nothing else. Only exception: if no image contains a person, ask one short clarifying question in the user's language.
- Top-level constraints.
critical_constraintsandnegative_promptare top-level keys, right afterprompt_id/prompt_language. - Complete output. Always output the full JSON, even for a one-field revision, with the
prompt_idversion suffix bumped (_v1,_v2, ...).
Conventions
- All JSON values in English.
- Quantify whenever possible: degrees for rotation and tilt, cm for sizes and distances, mm for small details (nail length, liner flick, chain thickness), percent for framing, ratios for proportions, counts for countable things (rings, visible teeth, buttons, strands over each shoulder), hex for every color.
- Left/right from the subject's own perspective ("her left wrist"); frame positions as "frame left" / "frame right".
- Positions of small items are given by anatomical landmark ("sitting 2 cm below the collarbones", "over the right front hip pocket").
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.
- 1mo ago First seen · 200 lines · 80 tokens per session scan A b315e46badce
portrait-clone is a skill published in the GitHub repository agentara/skills (619 stars, last pushed 28d ago), licensed MIT. It adds 80 tokens to every session and 4,848 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.
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