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 skills add markmhendrickson/neotoma --skill remember-conversationsgit clone --depth 1 https://github.com/markmhendrickson/neotomaWrote 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/skills/markmhendrickson/neotoma/remember-conversations)<a href="https://agentmods.dev/skills/markmhendrickson/neotoma/remember-conversations"><img src="https://agentmods.dev/badge/skills/markmhendrickson/neotoma/remember-conversations/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/skills/markmhendrickson/neotoma/remember-conversations"><img src="https://agentmods.dev/badge/skills/markmhendrickson/neotoma/remember-conversations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.00734 |
| Opus 5 | $0.00013 | $0.00367 |
| Sonnet 5 | $0.00005 | $0.00147 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
remember-conversations 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 7d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remember Conversations
Import conversation history into Neotoma memory. Supports ChatGPT JSON exports, Claude history files, Slack archive exports, and shared conversation URLs.
When to use
When the user wants to persist past AI conversations, chat transcripts, or messaging archives into durable memory — preserving decisions, commitments, and context that would otherwise be lost.
Prerequisites
Run the ensure-neotoma skill first if Neotoma is not yet installed or configured in your current harness.
Supported sources
| Source | Format | Method |
|---|---|---|
| ChatGPT | JSON export (conversations.json) |
File read |
| Claude | Conversation history files | File read |
| Slack | Archive export (ZIP with JSON channels) | File read |
| Shared URLs | ChatGPT/Claude share links | Web scraper MCP or fetch |
| Meeting transcripts | VTT, SRT, TXT, MD files | File read |
Workflow
Phase 0: Verify Neotoma
Confirm Neotoma MCP is connected (call get_session_identity).
Phase 1: Identify source
- Ask the user what to import:
- ChatGPT export file path
- Claude conversation history directory
- Slack export archive path
- A shared conversation URL
- A transcript file
- Detect the format from the file extension or content structure.
Phase 2: Parse and preview
- Read the file or fetch the URL.
- Parse conversations into a structured list: title, date, message count, participants.
- Present a preview: total conversations, date range, highlights.
- Ask the user to confirm which conversations to import (all, or selected by title/date).
Phase 3: Extract entities
For each conversation:
- Store the conversation as a
conversationentity with title and date. - Store each substantive message as a
conversation_messageentity linked via PART_OF. - Extract embedded entities:
- Decisions: conclusions, choices, architectural decisions
- Tasks: commitments, action items, follow-ups
- Contacts: people mentioned or participating
- Events: scheduled meetings, deadlines
- Set
source_fileto the original filename for file-based imports. - Use the combined store path (entities + file) so the raw export is preserved as a source.
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.
- 7d ago First seen · 96 lines · 26 tokens per session scan A bdb0cfd0e577
remember-conversations is a skill published in the GitHub repository markmhendrickson/neotoma (32 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 734 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 2026-09-03.
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