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 anycap-ai/anycap --skill anycap-social-meme-workflowsgit clone --depth 1 https://github.com/anycap-ai/anycapWrote 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/anycap-ai/anycap/anycap-social-meme-workflows)<a href="https://agentmods.dev/skills/anycap-ai/anycap/anycap-social-meme-workflows"><img src="https://agentmods.dev/badge/skills/anycap-ai/anycap/anycap-social-meme-workflows/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/anycap-ai/anycap/anycap-social-meme-workflows"><img src="https://agentmods.dev/badge/skills/anycap-ai/anycap/anycap-social-meme-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00094 | $0.01724 |
| Opus 5 | $0.00047 | $0.00862 |
| Sonnet 5 | $0.00019 | $0.00345 |
| Haiku 4.5 | $0.00009 | $0.00172 |
Grade A, and why
anycap-social-meme-workflows 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 2d 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AnyCap Social Meme Workflows
Use this skill when the output needs to feel like a meme, captioned social post, or reaction visual, but still has to be reproducible.
Do not rely on image generation alone for exact caption text. Use AnyCap to create or edit the base visual, then render the final caption locally so the text is exact.
Read First
Read these files before acting:
- This file for the workflow
- references/workflows.md for pattern selection, prompt formulas, and article mapping
For detailed CLI syntax, authentication, and capability reference, use the anycap-cli skill.
Best Fit
Use this skill for:
- meme-style hero images with exact top or bottom text
- funny meme drawings with doodle-style internet humor
- captioned photos for blog posts or social posts
- reaction visuals from an existing screenshot or photo
- short meme-video concepts where the still or base frame comes first
- use-case demos that need both generated media and a repeatable workflow
Do not use this skill for:
- large meme-template databases
- highly specific internet meme lore pages
- exact brand or copyrighted character recreation requests
- production subtitle pipelines with timing-heavy caption editing
Core Rule
Split the task into two layers:
- Base visual layer with AnyCap
- Exact text layer with deterministic local rendering
Why:
- image models are good at style, composition, and fast variation
- image models are not dependable for long exact caption text
- deterministic overlay keeps the final meme readable and repeatable
Workflow
graph LR
A[Classify request] --> B[Choose model]
B --> C[Generate or edit base visual with AnyCap]
C --> D[Overlay exact text locally]
D --> E[QA readability and punchline]
E --> F[Deliver locally, via Drive, or via Page]
1. Classify the request
Choose one workflow first:
- Text-first meme: the joke or caption exists; the visual supports it
- Funny meme drawing: the humor mostly lives in the drawing style, pose, or absurd scene
- Reaction remix: user supplies an image and wants meme treatment
- Captioned photo: exact line of text on top of an image
- Meme-video concept: still image, caption, then optional short video
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.
- 2d ago Changed 0e746c86baf1
- 11d ago First seen · 215 lines · 94 tokens per session scan A 8f20222a8ce7
anycap-social-meme-workflows is a skill published in the GitHub repository anycap-ai/anycap (43 stars, last pushed 2d ago), licensed MIT. It adds 94 tokens to every session and 1,724 once invoked, about $0.0005 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 2026-08-30.
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