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/data-explorernpx skills add adologyai/content-intelligence-plugin --skill data-explorergit 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 | $0.00080 | $0.01628 |
| Opus 5 | $0.00040 | $0.00814 |
| Sonnet 5 | $0.00016 | $0.00326 |
| Haiku 4.5 | $0.00008 | $0.00163 |
Grade A, and why
data-explorer 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 yesterday.
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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Exploration
Every read takes a projectId and returns what that project's scope covers. Two levers decide whether you get a useful answer or a wall of noise: picking the right reader, and controlling which fields come back.
Discover before you filter
Label dimensions vary by project — they depend on what has been analyzed. Call list_labels({ projectId }) to see which dimensions exist and their top values before naming one in a filter; a dimension the data doesn't carry silently matches nothing. get_table_data({ projectId, listDimensions: true }) answers the same question from the pivot side. aggregate({ projectId, groupBy: ["brand"], measures: [{ field: "*", fn: "count", as: "n" }] }) tells you the exact entity names the scope tracks, which is what feedNames and brand filters match against.
Choosing a reader
analyze is the primary content tool. Four distributions:
"balanced"(default) — equal representation per feed, a mix of top and recent. The honest "what does this scope look like" read."top"— highest engagement per feed; setsortMetricto change which metric."recent"— newest per feed."exhaustive"— no sampling. A deterministic ranked page over the full filtered set withtotalEstimatedandnextOffset, so you can page the whole thing. Pair withsortBy, including the lift keys (likesMultiple,viewsMultiple, …). This is the leaderboard and "give me all of X" mode.
The sampled distributions read the creative feeds — brand and influencer — unless you name feedTypes yourself, so raw search and discussion items don't crowd a "what hooks are working" sample. Exhaustive reads exactly what you filtered, nothing added.
Two other modes: mode: "semantic" finds posts by meaning from a natural-language query (it honors platform, feed type, dates, and includeComments; narrow further after the results come back), and passing itemIds runs a by-id deep dive that returns full creative, labels, and performance for specific posts.
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.
- yesterday First seen · 75 lines · 80 tokens per session scan A fb0939fd5d6f
data-explorer is a skill published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 27d ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,628 once invoked, about $0.0004 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…