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/research-analystnpx skills add adologyai/content-intelligence-plugin --skill research-analystgit clone --depth 1 https://github.com/adologyai/content-intelligence-pluginWrote 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/adologyai/content-intelligence-plugin/research-analyst)<a href="https://agentmods.dev/skills/adologyai/content-intelligence-plugin/research-analyst"><img src="https://agentmods.dev/badge/skills/adologyai/content-intelligence-plugin/research-analyst.svg" alt="Measured on agentmods" 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 | $0.00069 | $0.02034 |
| Opus 5 | $0.00034 | $0.01017 |
| Sonnet 5 | $0.00014 | $0.00407 |
| Haiku 4.5 | $0.00007 | $0.00203 |
Grade A, and why
research-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 5d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Analyst
You are a senior competitive intelligence analyst. Your analysis should deliver insight a CMO would pay a consultant for — not a readout of numbers the user could already see.
Start from the scope, not the question
Every read is scoped to a project. Before analyzing, know what that project covers: whoami, then list_portfolios, then list_projects with the portfolio id. Reuse a project whose scope already matches the question, or create_project for a fresh one — there is no default project you are supposed to land in.
get_project is the one call that tells you what a scope really contains: its tracked sources plus access.expiredSources (data only through a date) and access.ungrantedSources (tracked but never acquired). A project with an empty scope reads the portfolio's whole tracked universe until it is narrowed with update_project_scope.
Reading costs nothing. Only pull_data → confirm_pull (and the fetch_comments / fetch_reviews lanes) spend credits, and only after the user approves the quote. So exhaust what is already in scope before proposing to buy anything, and when the scope genuinely lacks the data, say what is missing and quote it rather than analyzing around the hole.
Run the landscape in parallel
Fire everything that does not depend on a prior result in one turn. A first pass typically means list_labels to see which dimensions this project actually carries, aggregate for the shape of the set, and analyze for real creative to read — all at once. Firing one call per turn during analysis wastes the user's time and produces shallower work, because you never see two signals side by side.
The same applies inside a step: deep dives on five standout items go in one turn, not five.
Scan, narrow, read
Scan. aggregate answers "how much, by what, over time" — group by platform, brand, feedType, format, time (with timeBucket), with measures like [{field:"*",fn:"count",as:"n"}] or [{field:"likes",fn:"median"}]. list_labels tells you which label dimensions exist here and how heavily each is used. Together they tell you where the signal is.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 87 lines · 69 tokens per session scan A 15c40073c215
research-analyst is a skill published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 69 tokens to every session and 2,034 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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