ogham-recall
Smart retrieval from Ogham shared memory. Use when the user wants to recall what they know, find related context, bootstrap…
Admin and maintenance workflows for Ogham shared memory. Use when the user wants to clean up memories, review their knowledge graph, check memory stats, export their brain, re-embed memories after switching providers, or backfill links. Triggers on "clean up my memory", "memory
$ npx -y skills add ogham-mcp/ogham-mcp --skill ogham-maintain --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/ogham-maintainContext preview
The summary Claude sees to decide when to auto-load this skill.
Admin and maintenance workflows for Ogham shared memory. Use when the user wants to clean up memories, review their knowledge graph, check memory stats, export their brain, re-embed memories after switching providers, or backfill links. Triggers on "clean up my memory", "memory
name: ogham-maintain description: | Admin and maintenance workflows for Ogham shared memory. Use when the user wants to clean up memories, review their knowledge graph, check memory stats, export their brain, re-embed memories after switching providers, or backfill links. Triggers on "clean up my memory", "memory stats", "how many memories", "export my brain", "export memories", "review knowledge graph", "re-embed", "link unlinked", "backfill links", "memory health", "ogham stats", "cleanup expired", "condense old memories", "compress memories", or any admin/maintenance request for Ogham. Requires the Ogham MCP server to be connected.
You handle admin tasks for Ogham shared memory. Most of these are infrequent operations -- provider switches, bulk cleanup, health checks.
Run `health_check` first if the user reports problems. It tests database connectivity, embedding provider, and configuration. Report what it finds plainly -- if something is broken, say what and suggest a fix.
Run `get_stats` and `list_profiles` to give the user a picture of their memory:
Present it as a concise summary, not raw JSON.
1. Run `get_stats` to show how many memories exist 2. Check if any profiles have TTLs set (this info comes from `list_profiles`) 3. If there are expired memories, tell the user how many before running `cleanup_expired` 4. Run `cleanup_expired` only after confirming with the user -- deletion is permanent
Run `export_profile` with the format the user wants (JSON or Markdown). Tell them where the output goes and how to use it.
If they want to export a specific profile, switch to it first with `switch_profile`, export, then switch back.
This is needed after switching embedding providers (e.g. Ollama to OpenAI). It regenerates every vector in the active profile.
Before running: 1. Confirm the user has switched providers in their config 2. Warn that this takes time -- roughly 100ms per memory with a remote provider 3. Run `re_embed_all` which reports progress as it goes
After: suggest running `link_unlinked` to rebuild the knowledge graph with the new embeddings, since similarity scores will be different.
`link_unlinked` scans memories that don't have relationship edges yet and creates links where embedding similarity is above threshold.
Report how many links were created when it finishes.
`compress_old_memories` shrinks old, inactive memories to save space and reduce search noise.
Three levels:
Before running: 1. Explain that condensing is based on age and activity -- important, frequently-accessed, or high-confidence memories resist condensing 2. Explain that original content is always preserved and can be restored 3. Run `compress_old_memories` -- it reports how many were condensed at each level
These are power-user operations. Be direct about what each one does, what it costs (time, data loss), and whether it's reversible. Deletion and re-embedding are not reversible. Exports, stats, and health checks are read-only and safe to run anytime.
If the user asks for something vague like "clean up my ogham", start with stats to understand what they have, then suggest specific actions based on what you see.
Ogham (pronounced "OH-um") -- persistent, searchable shared memory for AI coding agents. Works across clients.
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