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 social-analytics-reportgit 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/social-analytics-report)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/social-analytics-report"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/social-analytics-report/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/social-analytics-report"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/social-analytics-report.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 | $0.00112 | $0.00632 |
| Opus 5 | $0.00056 | $0.00316 |
| Sonnet 5 | $0.00022 | $0.00126 |
| Haiku 4.5 | $0.00011 | $0.00063 |
Grade A, and why
social-analytics-report 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.
Social Analytics Report
Make an honest operational report from the metrics the team actually has, with context, uncertainty, and a next decision.
1. Establish what the data can answer
Ask for the date range, channels, posts or campaigns, metric definitions, collection method, target or baseline, and any known changes in spend, audience, format, or distribution. If definitions differ across channels, do not silently compare the raw totals.
2. Check the data before interpreting it
Identify missing rows, partial reporting windows, duplicate posts, changed tracking, or metrics collected at different ages. Label the report's data boundary. Separate direct observations from hypotheses about why something happened.
3. Read outcomes against the intended job
Group results by the job each post or channel was assigned: awareness, education, discussion, qualified traffic, conversion assistance, or creator partnership learning. Compare like with like. Look for repeatable patterns in message, format, audience context, and distribution rather than choosing one winner by impressions.
4. Recommend the next smallest test
Return a decision table: observation, confidence, likely explanation, action, owner, and evidence needed next. State what not to conclude. Recommend a bounded test that preserves the campaign claim and measurement method so the next report can actually compare it.
Hard rules
- Never claim causation from correlation or a single post.
- Do not compare unlike metrics, date windows, paid and organic activity, or posts at different maturity without saying so.
- Keep raw data, calculated rates, and interpretation distinguishable.
- Do not hide low-performing or incomplete data to make a narrative look cleaner.
- Never fabricate access to native analytics, ad accounts, or conversion systems.
Failure modes
| Failure | Do this instead |
|---|---|
| A high-impression post is declared successful | Judge it against the job and downstream evidence it was intended to influence. |
| A report lists metrics but makes no decision | End with one keep, one test, one change, and their owners. |
| Platform totals are compared as if identical | Normalize the question or report each channel separately. |
| The team changes five things at once | Choose the smallest observable test and hold other conditions steady where possible. |
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 · 112 tokens per session scan A d07217557320
social-analytics-report is a skill published in the GitHub repository Ootto-AI/claude-content-skills (30 stars, last pushed 19d ago), licensed MIT. It adds 112 tokens to every session and 632 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.
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