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 hogan-tech/brand-loom --skill captiongit clone --depth 1 https://github.com/hogan-tech/brand-loomWrote 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/hogan-tech/brand-loom/caption)<a href="https://agentmods.dev/skills/hogan-tech/brand-loom/caption"><img src="https://agentmods.dev/badge/skills/hogan-tech/brand-loom/caption/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/hogan-tech/brand-loom/caption"><img src="https://agentmods.dev/badge/skills/hogan-tech/brand-loom/caption.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 | $0.00043 | $0.00418 |
| Opus 5.5 | $0.00017 | $0.00167 |
| Sonnet 5.5 | $0.00009 | $0.00084 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
caption 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 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.
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.
What it actually says
Caption Writer
Use when you need a social media caption for any platform. Supports brand_context for voice/tone. Model-agnostic.
Quick start
- CLI:
brand-loom run caption --text "AI marketing trends" --platform twitter - Chain:
brand-loom chain hook,caption --text "AI marketing trends" --brand brand.json - Python:
from brand_loom.agent import run_skill from brand_loom.providers import use_provider use_provider("openai") # or "anthropic", "gemini", "ollama", "fake" result = run_skill("caption", "your topic here") print(result.text)
Install
pip install brand-loom # standalone (no coding agent needed)
npx skills add hogan-tech/brand-loom # via skills.sh
Going further
Want hooks auto-matched to your brand voice, across every platform, no setup? → neoxra.com
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 · 44 lines · 43 tokens per session scan A fa6d36a36eeb
caption is a skill published in the GitHub repository hogan-tech/brand-loom (25 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 418 once invoked, about $0.0002 per session on Opus 5.5. 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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