mnemos
Skill708 starsChanged 5 months agoMITView on GitHub
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies
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- alinaqi/maggyword for word, first seen Jun 12, 2026
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We split this skill into the rules it gives an agent
5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
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Very specific
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- Common
- Distinctive
- Very specific
- Distinctive: line 53
When Claude Code compacts the context (~83% full), Mnemos uses three layers:
- Very specific: line 56
Layer 3 (PreToolUse fallback): If SessionStart doesn't fire (older versions, edge cases), the first tool call triggers …
- Distinctive: line 82
What's stored: only structural fields (roles, tool names, file paths, error flags, timestamps) plus a redacted, 200-char preview of each …
- Distinctive: line 112
When working on a task:
- Very specific: line 116
Check fatigue: before long operations: mnemos fatigue
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Mnemos — Task-Scoped Memory Lifecycle
What It Does
Mnemos prevents lossy context compaction from destroying the structured knowledge you need most. It treats your working memory as a typed graph (MnemoGraph) where different types of knowledge have different eviction policies:
- GoalNodes and ConstraintNodes are NEVER evicted — they survive all compaction
- ResultNodes are compressed (summary kept) before eviction
- ContextNodes are evictable when their activation weight drops
- CheckpointNodes persist to disk for session resume
Fatigue Model
Mnemos monitors 4 dimensions of "agent fatigue" — all passively observed from hook data, no manual input needed:
| Dimension | Weight | Signal Source | What It Measures |
| Token utilization | 0.40 | Statusline JSON | How full the context window is |
| Scope scatter | 0.25 | PreToolUse file paths | How many directories the agent is bouncing between |
| Re-read ratio | 0.20 | PreToolUse Read calls | How often the agent re-reads files it already read (context loss) |
| Error density | 0.15 | PostToolUse outcomes | What fraction of tool calls are failing (agent struggling) |
Fatigue states and actions:
| State | Score | Action |
| FLOW | 0.0–0.4 | Normal operation |
| COMPRESS | 0.4–0.6 | Micro-consolidation runs (compress 3 ResultNodes, evict 1 cold ContextNode) |
| PRE-SLEEP | 0.6–0.75 | Checkpoint written, consolidation runs |
| REM | 0.75–0.9 | Emergency checkpoint, consider wrapping up |
| EMERGENCY | 0.9+ | Checkpoint written, hand off immediately |
How To Use
Automatic (hooks handle everything):
- Statusline writes
fatigue.jsonon every API call - PreToolUse hook reads fatigue before every edit, auto-checkpoints at 0.60+
- PreCompact hook writes emergency checkpoint, compaction marker, and tells summarizer what to preserve
- SessionStart "compact" fires immediately after compaction, re-injects full checkpoint (primary restore)
- SessionStart "startup|resume" loads last checkpoint on new/resumed sessions
- PreToolUse fallback (no matcher) detects compaction marker if SessionStart didn't fire
- Stop hook writes final checkpoint for next session
Post-Compaction Recovery (Three-Layer Defense):
When Claude Code compacts the context (~83% full), Mnemos uses three layers:
- Layer 1 (PreCompact): Outputs strong preservation instructions with inline checkpoint content for the summarizer. Writes
.mnemos/just-compactedmarker. - Layer 2 (SessionStart "compact"): PRIMARY re-injection. Fires immediately when Claude resumes after compaction — before any agent action. Consumes the marker and injects the full checkpoint into the fresh context. This is the recommended approach per the RFC (Wake State Reconstruction).
- Layer 3 (PreToolUse fallback): If SessionStart doesn't fire (older versions, edge cases), the first tool call triggers
mnemos-post-compact-inject.shwhich detects the marker and injects. Safety net only.
The result: after compaction, you'll see a "CONTEXT RESTORED AFTER COMPACTION" block with your goal, constraints, what you were working on, and progress. Resume from there.
Manual CLI:
mnemos init # Initialize .mnemos/
mnemos status # Show node counts + fatigue
mnemos fatigue # Detailed fatigue breakdown
mnemos checkpoint --force # Write checkpoint now
mnemos resume # Output checkpoint for context
mnemos consolidate # Run micro-consolidation
mnemos nodes --type goal # List active GoalNodes
mnemos add goal "Build auth" # Add a GoalNode
mnemos bridge-icpg # Import iCPG ReasonNodes
mnemos ingest-claude --all # Ingest Claude Code transcripts (see below)
mnemos haze --recent 10 # Show per-session haziness scoresClaude Transcript Ingestion & Haziness
Mnemos can ingest Claude Code session transcripts (the per-session JSONL under
~/.claude/projects/) and score each session's haziness — a measure of how
much the agent struggled. The Stop hook does this automatically on session
exit; it is also available manually.
What's stored: only structural fields (roles, tool names, file paths, error flags, timestamps) plus a redacted, 200-char preview of each turn. Full content is never persisted, and secrets (API keys, tokens, PEM blocks, JWTs, credentials) are redacted before anything touches disk.
Haziness is a weighted score over five dimensions, each in [0,1]:
| Dimension | Weight | What it measures |
| correction_density | 0.30 | User corrections per eligible user turn |
| redo_ratio | 0.25 | Edits re-touched after an error |
| first_try_error_rate | 0.20 | Edits followed by errors within 3 turns |
| orphan_tool_use_rate | 0.15 | Tool calls with no matching result |
| backtrack_norm | 0.10 | git revert/reset --hard/restore calls |
The composite maps to a band: clear < 0.25 ≤ cloudy < 0.50 ≤ hazy < 0.75 ≤ lost.
mnemos ingest-claude --all # ingest every transcript + score
mnemos ingest-claude --session <id> # one session by id
mnemos ingest-claude --transcript <f> # a specific JSONL file
mnemos haze --recent 10 # table of recent sessions
mnemos haze --session <id> # per-dimension breakdownIngestion is idempotent (resumes via last_line_offset). Opt out per project
with touch .mnemos/claude-log.disabled.
Agent Instructions
When working on a task:
- Create a GoalNode at the start:
mnemos add goal "what you're trying to achieve" --task-id session-1 - Add ConstraintNodes for invariants:
mnemos add constraint "API backward compatibility" --scope src/api/ - Check fatigue before long operations:
mnemos fatigue - Checkpoint at sub-goal boundaries:
mnemos checkpoint - On session resume: the SessionStart hook automatically loads your checkpoint
iCPG Integration
Mnemos bridges with iCPG (Intent-Augmented Code Property Graph):
mnemos bridge-icpgimports active ReasonNodes as GoalNodes- Postconditions/invariants become ConstraintNodes
- Checkpoint includes iCPG state (active intent, unresolved drift)
Storage
Everything lives in .mnemos/ (gitignored):
mnemo.db— SQLite MnemoGraphfatigue.json— Live token metrics (updated per API call by statusline)signals.jsonl— Behavioral signal log (appended by PreToolUse + PostToolUse hooks)checkpoint-latest.json— Most recent checkpointcheckpoints/— Archived checkpoints
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