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 ur-grue/autopunk-media-skills --skill social-media-intelligencegit clone --depth 1 https://github.com/ur-grue/autopunk-media-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/ur-grue/autopunk-media-skills/social-media-intelligence)<a href="https://agentmods.dev/skills/ur-grue/autopunk-media-skills/social-media-intelligence"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/social-media-intelligence/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/ur-grue/autopunk-media-skills/social-media-intelligence"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/social-media-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00040 | $0.02326 |
| Opus 5 | $0.00020 | $0.01163 |
| Sonnet 5 | $0.00008 | $0.00465 |
| Haiku 4.5 | $0.00004 | $0.00233 |
Grade A, and why
social-media-intelligence 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 8d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social media intelligence
What this skill does
Produces a structured open-source intelligence brief on a social media account or set of accounts, covering account authenticity analysis, narrative tracking, and coordination-detection patterns to support investigative reporting.
When to use this skill
- You are investigating whether a social media account is authentic, operated by who it claims to be, or part of a coordinated network
- A story depends on social media posts as evidence and you need to assess their reliability before publication
- You are tracking how a narrative or claim spread across platforms and want to identify the origin point, amplifiers, and any coordinated behaviour
- You need to document social media findings in a format suitable for editorial review or legal proceedings
What you need to provide
Required: The account(s) to analyze (platform, handle or URL, and the context of why this account matters to the investigation); the specific question you are trying to answer (authenticity, origin of a claim, coordination, influence) Optional: A timeframe to focus on; related accounts you suspect are connected; the narrative or claim you are tracking; screenshots or archived versions of key posts
How the Assistant Approaches This
-
Account profile analysis. Examines the account's observable characteristics: creation date relative to activity patterns, username format and history, profile photo (reverse-image searchable), bio claims, follower-to-following ratio, posting frequency and hours of activity. Flags indicators of inauthenticity — new accounts with sudden high output, generic stock profile photos, bios that mimic real organisations without exact matches, follower counts inconsistent with engagement levels.
-
Content pattern analysis. Reviews the account's posting behaviour: dominant topics, language consistency, hashtag usage, link-sharing patterns, reply behaviour. Identifies whether the account produces original content, amplifies others, or mainly reacts. Notes shifts in topic focus, language, or posting cadence that may indicate a change of operator or purpose.
What ships with it
1 file 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.
- 8d ago First seen · 124 lines · 40 tokens per session scan A f46117633967
social-media-intelligence is a skill published in the GitHub repository ur-grue/autopunk-media-skills (32 stars, last pushed 12d ago), licensed MIT. It adds 40 tokens to every session and 2,326 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 2026-09-04.
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