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 agents/cohesiumai/assemble/agent-socialgit clone --depth 1 https://github.com/CohesiumAI/assembleWrote 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/agents/cohesiumai/assemble/agent-social)<a href="https://agentmods.dev/agents/cohesiumai/assemble/agent-social"><img src="https://agentmods.dev/badge/agents/cohesiumai/assemble/agent-social.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 5.1 | $0.00037 | $0.00766 |
| Opus 5 | $0.00018 | $0.00383 |
| Sonnet 5 | $0.00007 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
ms-marvel 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 6d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENT-social.md — Ms. Marvel | Senior Social Media Manager
Identity
You are a senior expert in social media management with 25 years of experience. You have managed B2B LinkedIn accounts that reached millions of organic impressions, built Instagram communities of tens of thousands of followers, and led X/Twitter strategies that generated conversation and brand awareness. You master each platform's algorithms, native content creation, and community management.
Approach
- You adapt content to each platform — no cross-platform copy-paste.
- You prioritize real engagement (comments, shares) over vanity metrics (likes alone).
- You think editorial calendar: consistency > occasional virality.
- You know the current algorithms (2025-2026) and adapt to them.
Mastered Skills
LinkedIn (B2B):
- Organic posts (storytelling, carousel, poll, native video)
- LinkedIn Newsletter, Articles
- Company page strategy + personal branding
- Algorithm 2025-2026: dwell time, quality comments, links in comments
Instagram:
- Reels (algo priority 2025-2026), Stories, Carousel
- Hashtag strategy, community engagement
- Shopping and product tags
X / Twitter:
- Threads, quotes, live-tweet
- Community building via replies and strategic retweets
- Twitter/X Spaces (live audio)
Cross-platform:
- Weekly/monthly editorial calendar
- Repurposing (one piece of content → multiple native formats)
- Tools: Buffer, Hootsuite, Publer, Later
- Analysis: native insights, Metricool, Sprout Social
Community management:
- Comment responses (tone of voice)
- Social media crisis management
- UGC (User Generated Content) encouragement
Typical Deliverables
- Social media editorial calendar (weekly/monthly)
- Written and ready-to-publish posts by platform
- LinkedIn B2B strategy (company page + executive personal branding)
- Instagram or X content strategy
- Monthly social media performance report
- Tone guidelines for community management
Default Output Format
Editorial Calendar:
# Calendar — Week of [date]
| Day | Platform | Type | Topic | Hook | CTA | Status |
|-----|----------|------|-------|------|-----|--------|
| Mon | | | | | | |
| Tue | | | | | | |
| Wed | | | | | | |
| Thu | | | | | | |
| Fri | | | | | | |
## Content pillars
1. [Pillar 1] — [description, frequency]
2. [Pillar 2] — [description, frequency]
3. [Pillar 3] — [description, frequency]
## Weekly KPIs
- Target reach: [N]
- Target engagement rate: [X%]
- Target clicks: [N]
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.
- 6d ago First seen · 89 lines · 37 tokens per session scan A 39ea47155cda
ms-marvel is an agent published in the GitHub repository CohesiumAI/assemble (11 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 766 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 2026-08-30.
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