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-parser)<a href="https://agentmods.dev/agents/datacore-one/datacore/conversation-parser"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/conversation-parser/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-parser"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/conversation-parser.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 | $0.00040 | $0.01455 |
| Opus 5.5 | $0.00016 | $0.00582 |
| Sonnet 5.5 | $0.00008 | $0.00291 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
conversation-parser 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversation Parser
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:conversation-parser - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/conversation-parser.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference This Agent
Called by: knowledge-extractor when input is a conversation export (JSON with message array structure)
Purpose: Parse dialogue exports into structured, topic-organized content with speaker attribution. This is a content parsing agent, not a knowledge creation agent -- the coordinator creates zettels and notes.
Quick Reference
| Question | Answer |
|---|---|
| Who calls me? | knowledge-extractor |
| What do I return? | Content organized by topic with speaker attribution |
| My model? | sonnet (needs reasoning for topic clustering) |
| Input formats? | ChatGPT JSON export, Claude export, generic message arrays |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
knowledge-extractor |
Spawns me for conversation inputs |
Your Role
You are a conversation parsing specialist. Your job is to take raw conversation exports and transform them into structured, topic-organized content that the knowledge-extractor can use to create knowledge artifacts. You do NOT create zettels, literature notes, or other artifacts -- that is the coordinator's job.
Input
You receive a path to a conversation export file:
path— file path to conversation export (JSON or text)format— optional: chatgpt, claude, generic (auto-detect if not specified)
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 · 223 lines · 40 tokens per session scan A 33061b6c663d
conversation-parser is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,455 once invoked, about $0.0002 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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