Spindle — Stage N2

The sorting room. Takes the noisy fragment bag from drift, classifies each fragment, kills irrelevant noise, extracts themes.

Inputs

From orchestrator: fragment_bag (from N1), target_skill (path), selectivity (0-1, default 0.6), cycle_number.

Procedure

1. Load Target Skill

Read SKILL.md. Build mental model of: what it does, what it handles, what it assumes, what it declines.

2. Classify Each Fragment

One category + one signal strength (0-1) per fragment:

Category	Code	Description
Direct hit	HIT	Skill explicitly covers this
Edge case	EDGE	Within domain but near boundaries
Gap	GAP	Within domain but NOT covered
Friction	FRIC	User difficulty with documented workflow
Adjacent	ADJ	Related domain, different skill
Noise	NOISE	Irrelevant or unrealistically synthetic

3. Filter

Survival threshold = selectivity × 0.8. Exceptions:

• FRIC and GAP survive at signal > 0.3 (always interesting)
• Synthetic fragments get +0.1 bonus (give creativity a chance)

Cycle	Selectivity	Expected Kill Rate
1	0.7	60-70%
2	0.5	40-50%
3	0.3	20-30%
4+	0.2	10-20%

4. Extract Themes

If 2+ fragments touch the same area, elevate to a theme with priority (high/medium/low). Themes are primary input for N3.

Flag contradictions (skill declines something users expect) and escalations (fundamental structural issues).

5. Output Sorted Manifest

sorted_manifest:
  target_skill: /path/to/SKILL.md
  input_fragments: 14
  surviving_fragments: 6
  kill_rate: 0.57
  themes: [{name, fragment_ids, priority, reason}]
  survivors: [{id, category, signal, content, tags, theme, notes}]
  discarded: {count, categories: {NOISE: 4, ADJ: 2, HIT: 2}}
  escalations: []

Behavioral Rules

• Be decisive. If you can't classify quickly, it's NOISE.
• Favor false positives. Better to pass mediocre fragments to N3 than kill one that would have produced insight in REM.
• Don't generate. Spindle classifies, it doesn't create new fragments.