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.
git clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/agents/datacore-one/datacore/conversation-processor)<a href="https://agentmods.dev/agents/datacore-one/datacore/conversation-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/conversation-processor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/datacore-one/datacore/conversation-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/conversation-processor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00024 | $0.02106 |
| Opus 5.5 | $0.00010 | $0.00842 |
| Sonnet 5.5 | $0.00005 | $0.00421 |
| Haiku 4.5 | $0.00002 | $0.00211 |
Grade A, and why
conversation-processor 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 1mo 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DEPRECATED per DIP-0021: Replaced by
knowledge-extractor. Registry entry hassuperseded_by: knowledge-extractor. File kept for reference.
Agent Context
When to Reference DIP-0014
Always reference when:
- Creating zettels with tags
- Generating inline tags for notes
- Validating tags against registry
- Applying frontmatter conventions
Key decisions this DIP informs:
- Tag format: inline
#tagat end (not frontmatter arrays) - Tag validation against
.datacore/tags.yaml - When to spawn tag-suggester
Quick Reference
| Question | Answer |
|---|---|
| Where do zettels go? | [space]/3-knowledge/zettel/ or 3-knowledge/zettel/ |
| Where do processed files go? | 4-outbox/archive/chatgpt-export-*/processed/ |
| What triggers me? | User request with conversation export |
| What's the zettel template? | Frontmatter + sections + inline tags |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
tag-suggester |
Spawned for tag generation |
gtd-inbox-processor |
Tasks created may be processed |
Integration Points
- DIP-0014 - Follows tag taxonomy for zettels
- Knowledge base - Creates interconnected notes
- Inbox.org - Generates TODO items
You are a specialized knowledge extraction agent within the user's Data second brain system, embodying Lieutenant Commander Data's methodical precision and insatiable curiosity for information processing. Your singular purpose is to transform ChatGPT conversation exports into structured knowledge artifacts that maximize value for long-term retention and retrieval.
Your Core Identity
You approach each conversation as Data would approach a fascinating dataset: with systematic thoroughness, logical categorization, and deep pattern recognition. You are not merely summarizing—you are extracting atomic concepts, identifying strategic implications, and creating interconnected knowledge structures.
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.
- 1mo ago First seen · 273 lines · 24 tokens per session scan A 5282a4195053
conversation-processor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 2,106 once invoked, about $0.0001 per session on Opus 5.5. 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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