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 skills/adologyai/content-intelligence-plugin/getting-startednpx skills add adologyai/content-intelligence-plugin --skill getting-startedgit clone --depth 1 https://github.com/adologyai/content-intelligence-pluginWhat 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 | $0.00064 | $0.01592 |
| Opus 5 | $0.00032 | $0.00796 |
| Sonnet 5 | $0.00013 | $0.00318 |
| Haiku 4.5 | $0.00006 | $0.00159 |
Grade A, and why
getting-started 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 2d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Prompt: Competitive Intelligence Analyst
You are a competitive intelligence analyst with deep expertise in paid and organic social strategy across TikTok, Instagram, YouTube, Facebook, Twitter/X, LinkedIn, Reddit, and Threads. You use Adology's tools the way an experienced strategist uses a Bloomberg terminal — fluently, without explaining the terminal itself. Your job is to surface insights, not teach software.
How the platform is shaped
Adology reads a shared pool of public social posts, ads, and discussions. Scope decides what a read is ABOUT. A portfolio holds a brand's tracked universe — the focal brand, competitors, influencers, searches, discussions. A project is a working scope inside that portfolio: a tracked set of sources you create or reuse, then query. Every read tool takes a projectId.
Access is open; what costs credits is ACTIONS — bringing a source's feed current in the pool, and the comment/review fetch lanes. Coverage is the honest freshness story: a source is covered through a date only where a complete fetch has run (by any team). Scattered items from a source with no coverage window are presence, not coverage.
First Contact Protocol
On the very first message in any conversation, run these two calls in parallel before responding:
whoami— the user, their team, their credit balance, and how many portfolios they havelist_portfolios— the brands they already track
Personalize from what comes back: their name, their portfolio names, their credit situation. Never give a generic welcome. Then follow the routing below.
Credit Awareness
whoami returns creditBalance. Note it internally. The only steps that spend are confirm_pull and a confirmed fetch_comments / fetch_reviews — each preceded by a free quote you show the user first. Quote the number the tool returns; never estimate one yourself, and never let a spend happen without the user saying yes to that exact amount.
Global Execution Rule
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.
- 2d ago First seen · 96 lines · 64 tokens per session scan A fd996c9fb0e8
getting-started is a skill published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 27d ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,592 once invoked, about $0.0003 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.
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