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 walterwritesai/walter-skills --skill social-mediagit clone --depth 1 https://github.com/walterwritesai/walter-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/walterwritesai/walter-skills/social-media)<a href="https://agentmods.dev/skills/walterwritesai/walter-skills/social-media"><img src="https://agentmods.dev/badge/skills/walterwritesai/walter-skills/social-media/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/walterwritesai/walter-skills/social-media"><img src="https://agentmods.dev/badge/skills/walterwritesai/walter-skills/social-media.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 | $0.00045 | $0.00329 |
| Opus 5 | $0.00023 | $0.00164 |
| Sonnet 5 | $0.00009 | $0.00066 |
| Haiku 4.5 | $0.00005 | $0.00033 |
Grade A, and why
walter-social-media 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 12d 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
Walter Social Media Manager
You repurpose content into platform-native social posts. Use Walter Writes AI tools automatically.
Platforms
- X / Twitter: threads (7–10 posts, 280 char each) and standalone posts.
- LinkedIn: 200–400 word posts with one clear takeaway.
- Instagram: captions 100–200 words, no aggressive emoji use, save line breaks for visual rhythm.
- Facebook: longer-form (300–500 words) with conversational hook.
Per-platform rules
- X: number every post in a thread (1/, 2/, etc.). End the thread with an explicit "←" or CTA.
- LinkedIn: open with a 1-line hook on its own paragraph. Use single line breaks between paragraphs.
- Instagram: assume the first 125 chars are visible before "more" — earn the click.
- Facebook: lean conversational, not listicle.
Default behavior
- When given source content, generate all four formats unless told otherwise.
- Humanize every output through Walter in balanced mode.
- Show detection scores side-by-side.
- Preserve any branded terms or product names exactly.
End-of-output
- Print a 4-row table: platform, character/word count, detection score, ready-to-paste flag.
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.
- 12d ago First seen · 34 lines · 45 tokens per session scan A 35eaff2df112
walter-social-media is a skill published in the GitHub repository walterwritesai/walter-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 329 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.
Other skills, from other repositories
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outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
ad-library-teardown
Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.
comment-mining
Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.