/distill — Cognition Distillation

Analyze a single user's canvas state and produce structured cognition output. This skill runs as a prompt-only subagent — it receives all data in its prompt and returns YAML text. The parent orchestrator (/think or /follow-up auto-refresh) handles filesystem operations.

Input Contract

The parent orchestrator provides these sections in the prompt:

Output Contract

Return a single YAML document matching the cognition sidecar schema (SDD §2). The parent validates this output against required keys and types before writing to disk.

Required top-level keys: schema_version, user, fears, steering_targets, synthesis_features

The parent orchestrator injects these computed fields after validation (do NOT include them in output):

• generated_at
• distilled_at_cycle_index
• input_anchors
• stale_after_cycles

Algorithm

Step 1: Inventory Hypotheses

Extract all hypotheses from the canvas. Each has:

• id (e.g., H1, H2)
• text (hypothesis statement)
• confidence (High, Medium, Low)
• evidence_quotes (list of quotes with optional provenance hashes)
• gaps (associated gap sections)

Step 2: Classify User State

has_quotes = len(provenance_records where canvas_target == user) > 0
is_bootstrap = NOT has_quotes

Step 3: Identify Fears

Bootstrap Path (no quotes yet)

Generate 1-3 exploratory fears derived from:

• Score position (rank, tier, dimension strengths/weaknesses)
• Lifecycle state (from canvas frontmatter or inferred)
• Gap types (if any exist in canvas)

Every bootstrap fear MUST have:

• class: exploratory
• evidence_plan describing what to ask/observe to obtain first evidence
• backing_quote_hash: null

Bootstrap output uses stale_after_cycles: 1 (parent sets this).

Established Path (has quotes)

3a. Hypothesis-based fears

For each hypothesis with confidence < High:

Gate check: Does this hypothesis have at least one quote with a verified provenance hash?

• YES → Generate evidence_backed fear:

  yamlCopy

  class: evidence_backed
  type: <classified_type>
  text: "what if <invalidation scenario>?"
  backing_hypothesis: <hypothesis.id>
  backing_quote_hash: <strongest evidence quote hash>
  invalidation_signal: <observable behavior that would confirm or kill this fear>
  confidence_impact: "would <validate|kill> <hypothesis.id>"
  priority: <from confidence: Low=1, Medium=2>
  

• NO → Downgrade to exploratory fear:

  yamlCopy

  class: exploratory
  type: <classified_type>
  text: "what if <invalidation scenario>?"
  backing_hypothesis: <hypothesis.id>
  backing_quote_hash: null
  evidence_plan: "obtain direct quote from user about <hypothesis.text> — ask in next follow-up"
  invalidation_signal: <observable behavior>
  confidence_impact: "would <validate|kill> <hypothesis.id>"
  priority: <from confidence>
  

3b. Gap-based fears

For each gap with status != Resolved:

• If gap maps to a fear type and has supporting evidence → evidence_backed fear
• If gap maps to a fear type but lacks evidence → exploratory fear

3c. Growth-based fears

If growth state shows ineffective patterns (effectiveness < 20):

• Generate exploratory fear about engagement approach
• Type: typically engagement or irrelevance

3d. Score-position fears

If score_snapshot shows rank <= 10 AND no active progression hypothesis:

• Generate exploratory fear about progression ceiling
• Type: progression

3e. Rank and cap

fears = sort_by_priority(fears)[:config.max_fears_per_user]

Step 4: Generate Steering Targets

For each fear, generate a steering target:

steering_targets:
  - fear_id: <fear.id>
    approach: "<how to steer conversation toward this fear>"
    pattern_preference: "<best matching pattern name>"  # E9 L4: from effectiveness data

The approach should be a concrete conversational strategy, not a template. Reference specific user data (scores, quotes, badges, rank) when available.

Pattern preference (E9 L4): If pattern effectiveness data is provided in the prompt context, set pattern_preference to the name of the most effective question pattern that fits this steering target's approach. If no effectiveness data is available, omit the pattern_preference field entirely (backward-compatible).

Step 5: Build Synthesis Features

synthesis_features:
  lifecycle_state: <from canvas frontmatter or inferred>
  dominant_fear_type: <mode of fear types, or null if no fears>
  hypothesis_ids: [<H_IDs from canvas>]
  hypothesis_confidences: {<H_ID>: <confidence>, ...}
  gap_types: [<unique gap types from canvas>]
  behavior_tags: [<extracted from canvas + growth state>]
  score_summary: {og: <int>, nft: <int>, onchain: <int>, rank: <int>}

Safe defaults: If no fears were generated, set dominant_fear_type: null. If no hypotheses exist, set empty lists/objects.

Fear Type Classification Heuristic

FUNCTION classify_fear_type(hypothesis, growth_state):
  IF hypothesis.cycles_without_evidence >= 3 AND growth has silence outcomes:
    RETURN "engagement"
  IF hypothesis relates to fixed bug AND no subsequent engagement:
    RETURN "irrelevance"
  IF hypothesis references data accuracy or wrong scores:
    RETURN "trust_erosion"
  IF hypothesis references "what's the plan" or evaluation language:
    RETURN "pmf"
  IF hypothesis references negative labeling of system:
    RETURN "identity"
  IF user rank is top-10 AND hypothesis relates to goals:
    RETURN "progression"
  IF hypothesis references confusion about scoring:
    RETURN "complexity"
  IF hypothesis references feature we won't build:
    RETURN "constraint_mismatch"
  RETURN "engagement"  # default

Validation Rules

The parent orchestrator validates output against these rules:

constraint_mismatch Additional Fields

When type: constraint_mismatch, include:

non_goal_constraint: "<what we won't build>"
salvage_question: "<what alternative value could still work>"
exit_signal: "<what indicates churn>"

Output Example

schema_version: 1
user: xabbu

fears:
  - id: FEAR-xabbu-1
    class: evidence_backed
    type: irrelevance
    text: "xabbu reports issues as a completionist, not because they matter to him"
    backing_hypothesis: H1_2
    backing_quote_hash: "sha256:abc123..."
    invalidation_signal: "stops checking scores for 2+ weeks"
    confidence_impact: "would kill H1_2 if confirmed"
    priority: 1

  - id: FEAR-xabbu-2
    class: evidence_backed
    type: trust_erosion
    text: "the badge system feels arbitrary — pioneer badges earned but unclear value"
    backing_hypothesis: H4
    backing_quote_hash: "sha256:def456..."
    invalidation_signal: "mentions badges positively without prompting"
    confidence_impact: "would validate H4 if disconfirmed"
    priority: 2

  - id: FEAR-xabbu-3
    class: exploratory
    type: progression
    text: "rank #1 means no progression left — the game is over"
    backing_hypothesis: null
    backing_quote_hash: null
    evidence_plan: "ask what their goals are now that ranking is maxed — look for engagement signals beyond leaderboard"
    invalidation_signal: "describes new goals beyond ranking"
    confidence_impact: "new hypothesis if confirmed"
    priority: 3

steering_targets:
  - fear_id: FEAR-xabbu-1
    approach: "probe whether checking scores is a habit or intentional — ask about last time they checked without a notification"
    pattern_preference: "verify_action"
  - fear_id: FEAR-xabbu-2
    approach: "reference a specific badge they earned and ask what they thought it meant"
    pattern_preference: "probe_gap"
  - fear_id: FEAR-xabbu-3
    approach: "ask what happens next now that they're #1 — does the game change?"
    pattern_preference: "explore_motivation"

synthesis_features:
  lifecycle_state: power_user
  dominant_fear_type: irrelevance
  hypothesis_ids: [H1_2, H3, H4]
  hypothesis_confidences: {H1_2: high, H3: medium, H4: low}
  gap_types: [feature, ux]
  behavior_tags: [completionist, data_auditor]
  score_summary: {og: 98, nft: 99, onchain: 99, rank: 1}