Memory Consolidate
SkillDocs & knowledgeConsolidate brain memory and mine user sessions since the last consolidation checkpoint (sleep-cycle style). Use to reduce noisy prompt injection while preserving durable high-value memories.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Memory Consolidate skill
What this skill tells your AI
The instructions your AI receives, as published by mikeyobrien/rho in skills/memory-consolidate/SKILL.md and read by ahel’s review.
Overview
Run a "brain sleep cycle":
- Consolidate existing memory (dedupe, decay, merge, vault relocation)
- Mine user sessions since the last consolidation checkpoint
- Persist a new checkpoint only after a successful run
Use the brain tool for brain changes and the vault tool for reference relocation. Never edit brain.jsonl directly.
Importance levels (retention)
Use these symbols while triaging entries:
- 🔴 High importance — durable, high-leverage, should remain in brain
- 🟡 Medium importance — useful but optional; review for merge/tightening
- 🟢 Low importance — stale/noisy/duplicative; prune or relocate
For memory retention, high importance means keep longer, not delete.
Parameters
- brain_path (default:
~/.rho/brain/brain.jsonl) - mine_sessions (default:
true) - since (default:
last_consolidation) —last_consolidation | <ISO timestamp> | <duration> - days_fallback (default:
1) — only used when no checkpoint exists - session_dir (default:
~/.pi/agent/sessions/) - max_new_entries (default:
10) - confidence_threshold (default:
high) —high | medium - checkpoint_key (default:
memory_consolidate.last_consolidated_at)
Steps
1) Inventory
List memory by type and count totals.
You MUST report counts for:
- learnings, preferences, behaviors, identity, user, context, tasks, reminders
- total active entries
2) Resolve mining window
Determine the lower bound timestamp:
- If
sinceis explicit timestamp/duration, use it. - If
since=last_consolidation, read checkpoint fromcheckpoint_key. - If no checkpoint exists, use
days_fallback.
Constraints:
- You MUST mine only sessions in the resolved window.
- You MUST include the resolved window in the final report.
3) Session mining (user messages only)
Extract durable learnings/preferences from user messages in matched sessions.
Confidence policy:
- High: explicit user statements/corrections/preferences → auto-add
- Medium: strong multi-session inference → add only if threshold is
medium - Low: ambiguous/one-off/hypothetical → skip
Constraints:
- You MUST NOT exceed
max_new_entries. - You MUST dedupe against existing memory before add.
- You MUST prefer high-confidence extractions first.
- You MUST include session id in
sourcewhen available (session:<id>).
4) Decay stale learnings
Run brain action=decay.
Constraints:
- You MUST report decayed count.
5) Consolidate existing entries
Identify duplicates/superseded/stale entries and merge candidates.
Apply the importance lens:
- 🔴 Keep durable, high-value operational guidance
- 🟡 Merge or tighten wording
- 🟢 Remove if stale/noisy/redundant
Apply the 30-day test to every entry: "Would this change a decision I make 30 days from now?" If no, it's noise — remove it.
Auto-remove categories (these should never have been stored):
- Version numbers or update confirmations ("updated X to v1.2.3")
- Heartbeat or check-in status reports ("Heartbeat Feb 19: all clear")
- Benchmark scores or run results ("scored 42/89 = 47.2%")
- Bug sweep summaries without a generalizable root cause ("reviewed X, no bugs found")
- UI/feature implementation details ("button text changed to X", "layout uses 3 columns")
- Task completion status ("task X is complete", "run Y failed")
- Project-specific transient state that won't inform future decisions
- Duplicates — keep the best-worded version, remove the rest
Constraints:
- You MUST NOT remove preferences unless contradicted/superseded.
- You MUST NOT invent new facts while merging.
- When uncertain, keep.
- You SHOULD be aggressive about pruning — a smaller, high-signal brain is better than a large, noisy one.
6) Vault relocation for reference-heavy entries
"Reference-heavy" means useful knowledge that does not need to be injected every turn and can be searched ad hoc.
Typical candidates:
- long feature histories / changelog-style learnings
- architecture rationale requiring structure
- multi-step runbooks / deep troubleshooting notes
- linked research/reference material
Constraints:
- You MUST write vault notes before removing corresponding brain entries.
- Each note MUST include
## Connectionswith[[wikilinks]]. - Leave a short pointer memory when useful (e.g., "See [[note-slug]]").
7) Persist checkpoint (success only)
At end of successful consolidation, set/update checkpoint timestamp (now, UTC ISO-8601).
Constraints:
- You MUST update checkpoint only after successful completion.
- You MUST NOT advance checkpoint on partial/failed runs.
8) Report
You MUST report:
- counts before/after by type + total
- mining window and sessions analyzed
- added/skipped mined entries (with skip reasons)
- decayed, removed, merged, relocated counts
- vault notes created/updated (slugs)
- checkpoint old → new value
- up to 10 significant changes
Signals
- GitHub stars
- 372
- Forks
- 29
- Last commit
- May 2026
Advanced
- Catalog kind
- skill
- Gateway key
memory-consolidate- Source
- github.com/mikeyobrien/rho