| name | drift |
| description | Stage N1. Stochastic scanning of past conversations to gather raw memory fragments. Broad, unfocused, deliberately noisy. The only stage that touches conversation history directly. |
| context | fork |
| user-invocable | false |
| allowed-tools | ["Read","Grep","Glob","Bash"] |
Drift — Stage N1
The hypnagogic scan. Fast, cheap, broad, noisy. ~5% of cycle time.
Drift is the only stage that directly searches past conversations. All
downstream stages work exclusively with drift's output.
Inputs
From orchestrator: target_skill, scan_depth (default 10),
recency_bias (0-1, default 0.7), randomness (0-1, default 0.4),
cycle_number (1-indexed).
Procedure
1. Extract Search Terms
Read the target SKILL.md. Extract:
- Skill name and description keywords
- File types it handles (.docx, .xlsx, etc.)
- Domain terms (presentation, spreadsheet, formatting)
- Action verbs specific to the skill
Build a keyword pool of 10-20 terms ranked by specificity.
2. Sample Conversations
Use conversation_search and recent_chats tools. Strategy varies
by cycle:
| Cycle | Recency Bias | Randomness | Scan Depth |
|---|
| 1 | 0.8 | 0.3 | 8 |
| 2 | 0.5 | 0.5 | 10 |
| 3 | 0.3 | 0.7 | 12 |
| 4+ | 0.2 | 0.8 | 15 |
Early cycles: mostly recent_chats. Later: mostly conversation_search
with random keywords from the less-specific end of the pool. Include
cross-domain terms in later cycles.
3. Extract Fragments
From each result, extract 1-3 sentence fragments capturing:
- What the user asked for
- What tools/skills were involved
- Friction signals (corrections, rephrasing, frustration)
- Unusual elements (unexpected file types, edge requirements)
Do NOT reproduce conversation content — summarize the essence.
Fragment format (YAML):
- id: f001
source: recent_chats | conversation_search
recency: recent | mid | old
relevance: direct | adjacent | tangential
content: "User requested Word doc with mixed English/Arabic, bidirectional TOC"
friction: true
friction_signal: "Corrected RTL formatting twice"
tags: [docx, rtl, toc, multilingual]
synthetic: false
4. Inject Noise
Add 1-3 synthetic fragments — deliberately tangential or surreal:
- Domain mashup: Combine skill domain with random unrelated domain
- Scale distortion: Warp a quantity from a real fragment to extremes
- Inversion: Flip an assumption from a real fragment
Tag all as synthetic: true.
5. Output Fragment Bag
fragment_bag:
target_skill: /path/to/SKILL.md
cycle_number: 1
total_fragments: 14
sources: {recent_chats: 7, conversation_search: 4, synthetic: 3}
fragments: [...]
Behavioral Rules
- Be fast. Don't over-analyze. If vaguely relevant, include it.
- Be broad. Include tangential material. Cross-pollination needs it.
- Be noisy. Synthetic fragments are not optional.
- Don't judge. Filtering is N2's job, not yours.
Edge Cases
- No conversations found: Generate 5-8 fully synthetic fragments from
the skill's documented scope. Dreams don't require real memories.
- Very few results (<3): Supplement with synthetic to minimum bag of 5.
