export

A command guide for exporting results into a table or CSV file. CSV is a plain-text format where rows and columns can be opened in spreadsheet software.

In plain words
What is it for?
Use it to export posts, engagement metrics, creative fields, or grouped label values from a scoped project query.
Why use it?
It keeps exports tied to the data actually returned by the reading tools and states what the file covers.

Command

Install

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.

agentmods
npx agentmods add commands/adologyai/content-intelligence-plugin/export
Clone the repo
git clone --depth 1 https://github.com/adologyai/content-intelligence-plugin
Per session 15 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,325 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured yesterday against content hash 7d961ce7be92, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

commands/export.md · 52 lines

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 postanalyze({ 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). Use fields to name exactly the columns you want.
  • One row per post, metrics onlyquery_items({ projectId }), when the export is engagement rather than creative. Returns itemId, 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). Set includeAnalysis: true to add hookCategory, mainMessage, narrativeStyle, and emotionalMood.
  • One row per label valueget_table_data({ projectId, rows, metrics }). A pivot: count, useRate, medianLikes, medianViews, medianShares, viralRate, optionally pivoted across columns (brand, platform, feedType, mediaType, timePeriod, or focalVsRest with a focalBrand). Discover the row dimensions first with listDimensions: true or list_labels.
  • One row per groupaggregate({ projectId, groupBy, measures }). Time series and dimensional cuts with your own measures, each row carrying its n.

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:

  • analyze in exhaustive mode returns itemsReturned, totalEstimated, hasMore, and nextOffset, and caps at 80 items per call. It may also come back byteTruncated: true, meaning the server packed fewer items than the page held — continue from nextOffset rather than assuming the page was the limit.
  • query_items returns fetchedCount, totalEstimated, hasMore, and nextOffset. Its limit goes to 500 (default 80). Loop with offset: nextOffset while hasMore is true.
  • get_table_data returns totalRows, rowOffset, and hasMoreRows for paging (topN rows at a time, default 15, max 50), plus scanned, totalInScope, and scanCapped. It builds the pivot from a bounded working set of labeled items, so when scanCapped is true the percentages describe what was scanned, not the whole scope — say which.
  • aggregate takes a limit up to 5,000, default 200. Its total and totalGroups count groups, not items; the item count behind each group is that row's n.
  • search_all caps at 50 results and takes no offset, so its hasMore tells you the answer was cut off, not how to continue. Reach for analyze in exhaustive mode when the export needs the whole match set.

Read the full file on GitHub · 52 lines

Changes

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.

  1. yesterday First seen · 52 lines · 15 tokens per session scan A 7d961ce7be92

Subscribe to this mod's changes

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.