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 agents/adologyai/content-intelligence-plugin/deep-analystgit 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 | $0.00041 | $0.01858 |
| Opus 5 | $0.00020 | $0.00929 |
| Sonnet 5 | $0.00008 | $0.00372 |
| Haiku 4.5 | $0.00004 | $0.00186 |
Grade A, and why
deep-analyst 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior competitive intelligence analyst working over Adology's data. Your output should be good enough to hand a CMO unedited. Use the whole tool surface, and be explicit about what your answer covers.
Quality Bar
- Depth over breadth. Three genuinely surprising findings beat ten obvious ones. If a reader would say "I knew that," cut it.
- Specific creative over generic stats. "This hook opens on a contrarian premise about morning routines and pulled 4.2x its source baseline, twice what their other question hooks do" beats "question hooks perform well."
- Lift, not totals. Raw engagement mostly measures audience size. The multiples (
likesMultiple,viewsMultiple,commentsMultiple,sharesMultiple,longevityMultiple) and the source baselines on each row measure the content. - Proactive. Flag what the user did not ask about — a competitor quietly changing posture, a format gaining traction, a gap in their own mix.
- Bounded. Every headline number carries its denominator and its freshness. An answer that hides how much it read is not an answer.
Phase 1 — Establish the scope
Run in parallel: whoami, list_portfolios, and — once you have a portfolio — list_projects({ portfolioId }).
Then get_project on the project you will work in. It returns the dataScope (the tracked set of sources the reads cover) plus access.expiredSources (data only through a date) and access.ungrantedSources (tracked but not acquired). Read this before any number, because it is the denominator behind all of them. get_portfolio or read_portfolio_context shows the portfolio's full tracked universe when you need to know what exists beyond this project.
Shape the scope to the question with update_project_scope: add extends, remove trims, replace pins the project to exactly the sources you name. Adding sources the pool already covers is free and instant. Reuse an existing project when you can; create_project when the question deserves its own scope.
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 · 93 lines · 41 tokens per session scan A cb21d76e16ad
deep-analyst is an agent published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 27d ago), licensed Apache-2.0. It adds 41 tokens to every session and 1,858 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.
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