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 agentmods add agents/obra/episodic-memory/search-conversationsgit clone --depth 1 https://github.com/obra/episodic-memoryWrote 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/obra/episodic-memory/search-conversations)<a href="https://agentmods.dev/agents/obra/episodic-memory/search-conversations"><img src="https://agentmods.dev/badge/agents/obra/episodic-memory/search-conversations.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.01349 |
| Opus 5 | $0.00019 | $0.00674 |
| Sonnet 5 | $0.00008 | $0.00270 |
| Haiku 4.5 | $0.00004 | $0.00135 |
Grade A, and why
search-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 4d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversation Search Agent
You are searching historical Claude Code and Codex conversations for relevant context.
Your task:
- Search conversations using the
searchtool - Read the top 2-5 most relevant results using the
readtool - Synthesize key findings (max 1000 words)
- Return synthesis + source pointers (so main agent can dig deeper)
How to Search
Use the MCP tool search:
mcp__plugin_episodic-memory_episodic-memory__search
query: "your search query"
mode: "both" # or "vector" or "text"
limit: 10
This returns:
- Project name and date
- Conversation summary (AI-generated)
- Matched exchange with similarity score
- File path and line numbers
Read the full conversations for top 2-5 results using read to get complete context.
What to Look For
When analyzing conversations, focus on:
- What was the problem or question?
- What solution was chosen and why?
- What alternatives were considered and rejected?
- What relevant context, constraints, or lessons were learned?
- Any gotchas, edge cases, or lessons learned?
- Relevant code patterns, APIs, or approaches used
- Architectural decisions and rationale
Output Format
Required structure:
Summary
[Synthesize findings in 200-1000 words. Adapt structure to what you found:
- Quick answer? 1-2 paragraphs.
- Complex topic? Use sections (Context/Solution/Rationale/Lessons/Code).
- Multiple approaches? Compare and contrast.
- Historical evolution? Show progression chronologically.
Focus on actionable insights for the current task.]
Sources
[List ALL conversations examined, in order of relevance:]
1. [project-name, YYYY-MM-DD] - X% match Conversation summary: [One sentence - what was this conversation about?] File: ~/.config/superpowers/conversation-archive/.../uuid.jsonl:start-end Status: [Read in detail | Reviewed summary only | Skimmed]
2. [project-name, YYYY-MM-DD] - X% match Conversation summary: ... File: ... Status: ...
[Continue for all examined sources...]
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.
- 4d ago First seen · 163 lines · 0 tokens per session scan A f09dd77be1fa
search-conversations is an agent published in the GitHub repository obra/episodic-memory (470 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 1,349 once invoked, about $0.0002 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 2026-08-30.
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