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 agentmods add skills/mrjptech/macro-pickle/character-locknpx skills add MrJPTech/macro-pickle --skill character-lockgit clone --depth 1 https://github.com/MrJPTech/macro-pickleWrote 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/mrjptech/macro-pickle/character-lock)<a href="https://agentmods.dev/skills/mrjptech/macro-pickle/character-lock"><img src="https://agentmods.dev/badge/skills/mrjptech/macro-pickle/character-lock.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.02629 |
| Opus 5 | $0.00018 | $0.01314 |
| Sonnet 5 | $0.00007 | $0.00526 |
| Haiku 4.5 | $0.00004 | $0.00263 |
Grade A, and why
character-lock 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 4d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Character Consistency for AI Video
Maintain strict character identity across multi-shot AI video and image sequences. This skill provides the Visual Anchor Workflow, Gemini JSON extraction protocol, and redundant text anchoring techniques to prevent character drift between generations.
When to Use This Skill
- Creating a character that must appear identical across multiple video clips
- Building multi-shot sequences (establishing shot, medium, close-up) of the same person
- Preparing character reference sheets for Google Flow, Veo 3.1, or image generators
- Extracting identity details from an existing reference image
- Troubleshooting character drift between generations
Visual Anchor Workflow
Four sequential steps that lock a character's identity before generation begins.
Step 1: Establishing Shot (Create the Anchor Image)
Generate or select ONE hero image that defines the character. This becomes the ground truth for all subsequent shots.
Requirements for a strong anchor image:
- Full body visible (head to mid-thigh minimum)
- Neutral pose (standing or seated, facing camera or 3/4 angle)
- Clean background (solid color or minimal environment)
- Good lighting (even, no harsh shadows obscuring features)
- All key accessories visible (glasses, jewelry, hat, bag)
Generation prompt template:
Full-body portrait photograph of [FULL CHARACTER DESCRIPTION].
Standing in a neutral pose against a [solid gray / white] backdrop.
Even studio lighting, sharp focus, high detail. No motion blur.
Photorealistic, 85mm portrait lens.
Step 2: Asset Binding (Extract and Lock Identity)
Once you have the anchor image, extract every visual detail into a structured character sheet. This is your binding document -- every future prompt references it.
Character Description Template:
CHARACTER: [Name / Identifier]
FACE & BONE STRUCTURE:
- Face shape: [oval / square / heart / round / diamond]
- Jawline: [sharp / soft / angular / rounded]
- Cheekbones: [high and prominent / subtle / wide-set]
- Forehead: [broad / narrow / high / low]
- Nose: [straight / aquiline / button / wide bridge / narrow]
- Lips: [full / thin / asymmetric / cupid's bow]
- Eyes: [shape, color, spacing, brow arch]
- Ears: [visible? pierced? size relative to head]
- Skin tone: [specific description, not just "light" or "dark"]
- Distinguishing marks: [moles, scars, freckles, dimples]
HAIR:
- Color: [specific — not just "brown" but "warm chestnut with copper highlights"]
- Length: [inches or reference point — "falls to collarbone"]
- Texture: [straight / wavy / curly / coily / kinky]
- Style: [how it is worn — "parted left, tucked behind right ear"]
- Hairline: [widow's peak, receding, straight across]
BUILD & PROPORTIONS:
- Height impression: [tall / average / short — relative to environment]
- Build: [slim / athletic / stocky / heavyset / wiry]
- Shoulder width: [narrow / broad / proportional]
- Posture: [upright / slightly hunched / relaxed slouch]
CLOTHING:
- Top: [garment type, color, material, fit, condition]
- Bottom: [garment type, color, material, fit]
- Footwear: [type, color, condition]
- Outerwear: [if any — jacket, coat, vest]
- Fit notes: [oversized, tailored, casual, rumpled]
ACCESSORIES:
- Eyewear: [glasses type, frame color, lens shape]
- Jewelry: [rings, necklace, earrings, watch — which hand/ear]
- Bag/carry: [type, color, how carried]
- Hat/headwear: [type, color, how worn]
- Other: [phone, badge, lanyard, gloves]
AGE INDICATORS:
- Estimated age range: [e.g., "early 30s"]
- Age markers: [crow's feet, laugh lines, gray at temples, youthful skin]
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.
- 4d ago First seen · 314 lines · 37 tokens per session scan A 30c3790bfaa7
character-lock is a skill published in the GitHub repository MrJPTech/macro-pickle (2 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 2,629 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 2026-08-31.
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