Search past conversations and memory files
Scanned 9/3/2026
Install to Claude Code
npx -y skills add istota-project/istota --skill memory_search --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: memory_search
description: Search past conversations and memory files
always_include: true
cli: true
---
# Memory Search
Search across past conversations and memory files using hybrid BM25 + semantic search.
## Usage
Run `istota-skill memory_search --help` (or `istota-skill memory_search <subcommand> --help`) to see the live argument list.
```bash
# Search for relevant memories
istota-skill memory_search search "query text" [--limit 10] [--source-type TYPE]
# Index a specific conversation
istota-skill memory_search index conversation TASK_ID
# Index a specific file
istota-skill memory_search index file /path/to/file.md [--source-type TYPE]
# Reindex all conversations and memory files
istota-skill memory_search reindex [--lookback-days 90]
# Show indexing stats
istota-skill memory_search stats
```
## Knowledge graph (read-only)
Structured facts (entity-relationship triples) extracted by the nightly sleep cycle. Current facts are loaded into your prompt automatically as "Known facts."
```bash
# Query current facts (optionally filter by subject or predicate)
istota-skill memory_search facts [--subject ENTITY] [--predicate TYPE]
# Query facts as of a past date
istota-skill memory_search facts --as-of 2026-01-15 [--subject ENTITY]
# Get full timeline for an entity (current + historical facts)
istota-skill memory_search timeline ENTITY
```
**To write facts** — `add-fact`, `invalidate`, `delete-fact` — see the `memory` skill. The classification gate there decides when a piece of information goes to USER.md vs the knowledge graph; consult it before writing either.
## Source Types
- `conversation` — past task prompts and results
- `memory_file` — dated memory files from sleep cycle
- `user_memory` — USER.md persistent memory
- `channel_memory` — CHANNEL.md persistent memory
## Output
```json
{
"status": "ok",
"query": "project alpha",
"count": 3,
"results": [
{
"chunk_id": 42,
"content": "User: What's the status of project alpha?\n\nBot: Project alpha is...",
"score": 0.032,
"source_type": "conversation",
"source_id": "156",
"bm25_rank": 1,
"vec_rank": 3
}
]
}
```
## When to Use
**Always search when:**
- User references past conversations, decisions, or agreements
- User mentions preferences or patterns you should already know
- Topic involves a project, person, or system discussed before
- User says "remember," "we talked about," "last time," or similar
**Also search when:**
- Starting work on a project that may have prior context
- User asks about established patterns, conventions, or preferences
- You want to ensure consistency with previous advice or decisions
- A related topic was likely discussed even if user doesn't reference it
**Don't search when:**
- Query is entirely self-contained with no historical dimension
- You already have sufficient context from the current conversation
- Topic is clearly new with no prior discussion possible
### Search tips
- Use specific terms: "project alpha deployment" not "that project"
- Try multiple queries if first results aren't relevant
- Search for both the topic and related concepts
- Check `--source-type memory_file` for curated memories vs raw conversations
## Notes
- Conversations are automatically indexed after completion
- Memory files are indexed after sleep cycle writes them
- Use `reindex` to backfill if memory search was enabled after conversations already occurred
- Results are scoped to the current user — no cross-user leakage
- Falls back to keyword search if semantic search is unavailable
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