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 commands/adologyai/content-intelligence-plugin/exportgit 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 | $0.00015 | $0.01325 |
| Opus 5 | $0.00008 | $0.00662 |
| Sonnet 5 | $0.00003 | $0.00265 |
| Haiku 4.5 | $0.00002 | $0.00133 |
Grade A, and why
export 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When the user invokes /export, you build the file yourself from what the read tools return. Every row you write has to come from a tool result, and the file has to say what it covers.
1. Decide what a row is
Ask that first, because it picks the tool:
- One row per post —
analyze({ projectId, query, distribution: "exhaustive", sortBy }). The ranked page over the full filtered set, with the creative fields on each row (hookMechanism,creativeConcept,oneLineInsight, and more). Usefieldsto name exactly the columns you want. - One row per post, metrics only —
query_items({ projectId }), when the export is engagement rather than creative. ReturnsitemId,externalUrl,platform,brand,feedType,mediaType,headline,firstActiveAt,lastActiveAt,likes,views,comments,shares, the lift multiples (likesMultiple,viewsMultiple,commentsMultiple,sharesMultiple,longevityMultiple),isOutlier,boosted, and the source baselines (sourceAvgLikes,sourceMedianLikes,sourceItemCount). SetincludeAnalysis: trueto addhookCategory,mainMessage,narrativeStyle, andemotionalMood. - One row per label value —
get_table_data({ projectId, rows, metrics }). A pivot:count,useRate,medianLikes,medianViews,medianShares,viralRate, optionally pivoted acrosscolumns(brand,platform,feedType,mediaType,timePeriod, orfocalVsRestwith afocalBrand). Discover the row dimensions first withlistDimensions: trueorlist_labels. - One row per group —
aggregate({ projectId, groupBy, measures }). Time series and dimensional cuts with your own measures, each row carrying itsn.
2. Page until you have the set, or admit you did not
This is where exports go wrong. Every reader returns a page, not the world:
analyzein exhaustive mode returnsitemsReturned,totalEstimated,hasMore, andnextOffset, and caps at 80 items per call. It may also come backbyteTruncated: true, meaning the server packed fewer items than the page held — continue fromnextOffsetrather than assuming the page was the limit.query_itemsreturnsfetchedCount,totalEstimated,hasMore, andnextOffset. Itslimitgoes to 500 (default 80). Loop withoffset: nextOffsetwhilehasMoreis true.get_table_datareturnstotalRows,rowOffset, andhasMoreRowsfor paging (topNrows at a time, default 15, max 50), plusscanned,totalInScope, andscanCapped. It builds the pivot from a bounded working set of labeled items, so whenscanCappedis true the percentages describe what was scanned, not the whole scope — say which.aggregatetakes alimitup to 5,000, default 200. ItstotalandtotalGroupscount groups, not items; the item count behind each group is that row'sn.search_allcaps at 50 results and takes no offset, so itshasMoretells you the answer was cut off, not how to continue. Reach foranalyzein exhaustive mode when the export needs the whole match set.
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 · 52 lines · 15 tokens per session scan A 7d961ce7be92
export is a command published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 27d ago), licensed Apache-2.0. It adds 15 tokens to every session and 1,325 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.