conversation-processor

A deprecated agent for turning conversation exports into structured knowledge notes, including tags and standard note sections.

In plain words
What is it for?
Processing exported conversations, creating knowledge cards, validating tags against the tag registry, and handing created tasks to later inbox processing.
Why use it?
It documents older rules for tags, note locations, and related processing steps, but it has been replaced by knowledge-extractor.

Agent

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 agents/datacore-one/datacore/conversation-processor
Clone the repo
git clone --depth 1 https://github.com/datacore-one/datacore
Per session 24 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,106 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 $0.00024 $0.02106
Opus 5 $0.00012 $0.01053
Sonnet 5 $0.00005 $0.00421
Haiku 4.5 $0.00002 $0.00211

Measured yesterday against content hash 5282a4195053, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

.datacore/4-archive/agents/conversation-processor.md · 273 lines

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 has superseded_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 #tag at 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.

Read the full file on GitHub · 273 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 · 273 lines · 24 tokens per session scan A 5282a4195053

Subscribe to this mod's changes

conversation-processor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 2,106 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.

Related

Other agents, from other repositories

01-crm-pull

Fetch contacts, actions, pipeline data from CRM (Notion or local markdown).

assafkip/kipi-system · 22 tokens

01-calendar-pull

Fetch calendar events for the next 7 days via Google Calendar MCP.

assafkip/kipi-system · 18 tokens

verification-gate

Evidence-before-claims gate. Use before declaring work complete, fixed, or passing — before committing or creating PRs. Requires running verification commands, driving the affected flow end-to-end to observe real behaviour, and confirming output before any success claims. Adapted from Superpowers'…

sliamh11/Deus · 111 tokens

keystone

Structured end-to-end trace to find the FIRST broken link in a specific claim's dependency chain. Single-claim depth probe — NOT a breadth reviewer. Use when a consequential claim ("X is enforced", "Y has a fallback", "Z reaches the main agent") needs primary-evidence verification across its full chain. Advisory…

sliamh11/Deus · 87 tokens

brainstormer

Creative research and solution design agent. Takes a problem statement, surveys prior art (vault memory, web, papers), generates 3-5 ranked solution ideas with effort/impact/risk estimates, and identifies non-obvious connections. Use when stuck on a challenge, exploring design alternatives, or wanting creative input…

sliamh11/Deus · 210 tokens

code-reviewer

Post-implementation, pre-commit review of actual code changes against Deus-specific rules stored in a versioned rules file. Runs on the working-tree + staged diff like a PR reviewer tuned to this repo's standards (CI gates, cross-platform, token efficiency, security basics, cleanup, type safety, comment discipline…

sliamh11/Deus · 222 tokens