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 Ootto-AI/claude-content-skills --skill paid-social-briefgit clone --depth 1 https://github.com/Ootto-AI/claude-content-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/ootto-ai/claude-content-skills/paid-social-brief)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/paid-social-brief"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/paid-social-brief/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/ootto-ai/claude-content-skills/paid-social-brief"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/paid-social-brief.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 | $0.00115 | $0.00642 |
| Opus 5 | $0.00057 | $0.00321 |
| Sonnet 5 | $0.00023 | $0.00128 |
| Haiku 4.5 | $0.00012 | $0.00064 |
Grade A, and why
paid-social-brief 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.
How it starts
The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Social Brief
Translate an approved campaign into testable paid-social decisions without pretending media execution or platform access exists.
1. Confirm the approved foundation
Ask for the campaign objective, offer, claim boundary, landing destination, proof, audience evidence, budget range if supplied, policy constraints, and the owner who can actually configure the platform. If the offer or landing page is unapproved, do not write ads around it.
2. Define the test, not a prediction
State the hypothesis, audience context, creative angle, desired action, measurement event, and what would count as useful learning. Separate audience hypotheses from targeting settings because platform availability and policy can change. Keep the number of simultaneous variables small enough to interpret.
3. Specify creative and destination requirements
List the approved message variants, proof assets, disclosures, format needs, destination expectation, exclusions, and prohibited claims. Tie each creative to a hypothesis. Note where a native platform review or legal approval is required rather than claiming it is complete.
4. Set guardrails for operation and review
Provide the budget and pacing decisions only when supplied by an authorised owner; otherwise mark them open. Define launch checks, reporting window, stop or revise conditions, and the exact data needed for a review. Ensure tracked links follow the agreed UTM plan.
Hard rules
- Do not claim access to ad accounts, audiences, pixels, spend, or platform features you do not have.
- Never target or exclude sensitive groups in a way that violates law, policy, or the approved brief.
- Do not use unsubstantiated performance, health, financial, or customer-result claims.
- Do not optimize solely for cheap clicks when the campaign's intended action is elsewhere.
- Keep organic and paid results labelled separately in every report.
Failure modes
| Failure | Do this instead |
|---|---|
| A media brief is only ad copy | Define the hypothesis, audience context, measurement, and decision threshold. |
| Too many variables change at once | Limit the first test to the few variables needed to answer one question. |
| Creative promises more than the destination delivers | Align every claim with the approved landing experience and proof. |
| Results cannot be attributed | Create the UTM and measurement plan before launch. |
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 · 46 lines · 115 tokens per session scan A f3e733906784
paid-social-brief is a skill published in the GitHub repository Ootto-AI/claude-content-skills (30 stars, last pushed 20d ago), licensed MIT. It adds 115 tokens to every session and 642 once invoked, about $0.0006 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.
Other skills, from other repositories
scrapecreators-api
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio…
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.
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.
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.
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.