phasespace-labs Palinode Memory
Palinode stores typed memory as git-versioned markdown files with hybrid search (BM25 + vector). This skill provides procedural guidance for using Palinode's tools effectively.
Core Tools
| Tool | When to Use |
|---|---|
palinode_search |
Find relevant memories by meaning or keyword |
palinode_save |
Store a new typed memory (person, project, decision, insight) |
palinode_status |
Show file counts, chunk counts, entity graph, system health |
palinode_trigger |
Register an intention — auto-surfaces a memory file when context matches |
palinode_diff |
See what changed in a memory file recently |
palinode_consolidate |
Run or check the weekly compaction job |
Memory Types
Use the right type when saving:
PersonMemory— facts about a person (name, role, preferences, context)Decision— architectural/product decisions with rationaleProjectSnapshot— current state of a project (milestone, blockers, next)Insight— generalizable lessons learned (applies beyond current project)ActionItem— follow-up task with owner and deadline
Search Effectively
Palinode uses 4-phase injection — you don't always need to search manually:
- Core files (
core: true) are always injected at session start - Topic search runs on every turn automatically
- Associative recall expands entity mentions automatically
- Triggers fire when registered contexts match
Only call palinode_search explicitly when you need something specific not surfaced by injection, or when the user asks "what do I know about X?"
Saving Memories
Guidelines:
- Save decisions with rationale — not just what was decided, but why
- Save people context when you learn preferences, constraints, or relationships
- Save insights when a lesson applies broadly (not just to one task)
- Don't save raw conversation transcript — save the distilled fact
Example save calls:
palinode_save(
content="Alice and Bob are co-founders, 3-year gap in joining. CEO role is shared.",
type="PersonMemory",
entities=["person/alice", "project/my-app"]
)
palinode_save(
content="Curation > volume for training data. 90 curated samples >> 1,623 raw.",
type="Insight",
entities=["project/my-app"]
)
Triggers
Register an intention when you want a memory to auto-surface in future turns:
palinode_trigger(action="add", description="training data curation",
memory_file="insights/curation-over-volume.md", threshold=0.72)
Triggers fire when the user's message semantically matches description. Good for:
- "whenever we talk about training data, surface the curation insight"
- "when Alice's design decisions come up, surface the architecture relationships"
Git Tools
Use these when the user asks about memory history:
palinode_diff— what changed in the last N commits for a filepalinode_blame— who/when each section was writtenpalinode_history— file change history with diff stats and rename trackingpalinode_rollback— revert a file to a previous state
Consolidation
Weekly compaction runs Sunday 3am UTC via cron. To run manually:
palinode_consolidate(dry_run=True) # preview what would change
palinode_consolidate(dry_run=False) # apply operations
Operations: KEEP (no change), UPDATE (merge new info), MERGE (combine duplicates), SUPERSEDE (replace fact), ARCHIVE (move to history).
Setup Reference
See references/setup.md for installation, config options, and troubleshooting.