jtbd-patterns

Preamble

# Detect .jtbd/ directory
_JTBD_DIR=".jtbd"
_HAS_JTBD="no"
[ -d "$_JTBD_DIR" ] && _HAS_JTBD="yes"
echo "JTBD_DIR: $_HAS_JTBD"

# Detect gstack (optional integration)
_HAS_GSTACK="no"
[ -d "$HOME/.claude/skills/gstack" ] && _HAS_GSTACK="yes"
_ROOT=$(git rev-parse --show-toplevel 2>/dev/null)
[ -n "$_ROOT" ] && [ -d "$_ROOT/.claude/skills/gstack" ] && _HAS_GSTACK="yes"
echo "GSTACK: $_HAS_GSTACK"

# Detect git
_HAS_GIT="no"
git rev-parse --is-inside-work-tree 2>/dev/null && _HAS_GIT="yes"
echo "GIT: $_HAS_GIT"

# Check for python3 (YAML validation)
_HAS_PYTHON="no"
command -v python3 >/dev/null 2>&1 && _HAS_PYTHON="yes"
echo "PYTHON3: $_HAS_PYTHON"

# Read manifest if .jtbd/ exists
if [ "$_HAS_JTBD" = "yes" ] && [ -f "$_JTBD_DIR/manifest.yml" ]; then
  echo "--- MANIFEST ---"
  cat "$_JTBD_DIR/manifest.yml"
  echo "--- END MANIFEST ---"
fi

# Count existing switch analyses
if [ "$_HAS_JTBD" = "yes" ] && [ -d "$_JTBD_DIR/switches" ]; then
  _SWITCH_COUNT=$(ls "$_JTBD_DIR/switches/"*.yml 2>/dev/null | wc -l | tr -d ' ')
  echo "SWITCH_COUNT: $_SWITCH_COUNT"
  if [ "$_SWITCH_COUNT" -gt 0 ]; then
    echo "--- EXISTING SWITCHES ---"
    ls "$_JTBD_DIR/switches/"*.yml
    echo "--- END SWITCHES ---"
  fi
else
  echo "SWITCH_COUNT: 0"
fi

# Check for existing patterns
if [ "$_HAS_JTBD" = "yes" ] && [ -d "$_JTBD_DIR/patterns" ]; then
  _PATTERN_COUNT=$(ls "$_JTBD_DIR/patterns/"*.yml 2>/dev/null | wc -l | tr -d ' ')
  echo "PATTERN_COUNT: $_PATTERN_COUNT"
  if [ "$_PATTERN_COUNT" -gt 0 ]; then
    echo "--- EXISTING PATTERNS ---"
    ls -t "$_JTBD_DIR/patterns/"*.yml
    echo "--- END PATTERNS ---"
  fi
else
  echo "PATTERN_COUNT: 0"
fi

Gate: Minimum Data

If JTBD_DIR is no: Tell the user: "No .jtbd/ directory found. Run /jtbd-switch on an interview transcript first to create one." Stop.

If SWITCH_COUNT < 3: Tell the user: "Found {SWITCH_COUNT} switch analysis file(s). Pattern analysis needs at least 3 interviews to find meaningful signal. Run /jtbd-switch on {3 - SWITCH_COUNT} more transcript(s) first." Stop.

If SWITCH_COUNT is exactly 3: Proceed, but note: "Running with the minimum 3 interviews. Patterns will sharpen with more data."

Read All Switch Analyses

Read every .yml file in .jtbd/switches/ using the Read tool. For each file, extract and hold:

1. Interviewee profile: name, role, company, date
2. Timeline: all stages with triggers, durations, confidence levels
3. Forces: all push, pull, anxiety, habit entries with statements, quotes, intensity, confidence
4. Job story: the full job story string
5. Evidence strength: all scores

If a file has invalid YAML or missing required fields, skip it and warn: "Skipped {filename}: invalid format."

Analyze Patterns

Step 1: Cluster by Job

Compare all job stories and force patterns across interviews. Group interviews that share the same underlying job, even if the surface-level language differs.

Clustering rules:

• Two interviews share a job if they have overlapping push forces AND overlapping pull forces (similar pain, similar desired outcome)
• The job label should describe the outcome the person wants, not the product feature. "Get reliable numbers to leadership" not "use a dashboard"
• A single interview can belong to multiple clusters if it reveals multiple distinct jobs
• A cluster needs at least 2 interviews to be meaningful. Single-interview "clusters" go into an "emerging signals" section

For each cluster, identify:

• Frequency: How many of the total interviews show this job (e.g., 3/5)
• Common push: The shared push force across interviews in this cluster
• Common pull: The shared pull force across interviews in this cluster
• Key quotes: The strongest direct quote from each interview supporting this cluster (use verbatim quotes only, attribute with # Name)

Step 2: Force Pattern Analysis

Across ALL interviews (not per-cluster), identify:

• Strongest push: The push force that appears with highest intensity across the most interviews
• Strongest pull: Same for pull
• Strongest anxiety: Same for anxiety
• Strongest habit: Same for habit

For each, include the pattern description and the supporting evidence (which interviews, what intensity).

Step 3: Timeline Pattern Analysis

Look for patterns in the switching timeline across interviews:

• Average passive looking duration: How long do people sit with the pain before acting?
• Common active-looking trigger: What typically escalates from passive to active? (e.g., "leadership pressure" appeared in 3/3 interviews)
• Common deciding factors: What tips the decision? Features? People? Events?
• Common onboarding friction: What's hard about the actual switch?

Only include patterns that appear in 2+ interviews. Single-occurrence timeline details are noise, not signal.

Step 4: Evidence Gaps

Identify what's MISSING from the evidence base:

• Thin forces: Which force category has the fewest entries or lowest confidence across interviews? (Habit is commonly underprobed.)
• Missing perspectives: What roles, company sizes, or use cases are NOT represented? Compare against manifest.yml target_user if available.
• Low-confidence areas: Fields frequently marked confidence: low or inferred: true
• Missing timeline stages: Stages frequently marked not_discussed
• Score patterns: Which evidence_strength dimension is consistently lowest?

Step 5: Generate Recommendations

Based on the patterns and gaps, generate 3-5 actionable recommendations. Each recommendation should be:

• Specific enough to act on immediately
• Connected to evidence from the analysis
• Prioritized: most impactful first

Recommendation categories:

• Positioning: How to describe the product based on what the job actually is
• Onboarding: How to reduce switching anxiety based on common fears
• Next interviews: Who to interview next and what to probe based on evidence gaps
• Product: What to build or prioritize based on force intensity patterns

Output Format

Generate a YAML file following this structure:

# .jtbd/patterns/patterns-{YYYYMMDD}.yml
# Generated by /jtbd-patterns on {date}
# Cross-interview pattern analysis from {N} switch analyses

schema_version: 1
analysis_date: {YYYY-MM-DD}
switch_files_analyzed: {N}
files_included:
  - {filename1.yml}
  - {filename2.yml}
  - {filename3.yml}

clusters:
  - job: "{outcome-oriented job description}"
    frequency: "{N}/{total}"
    common_push: "{shared push pattern}"
    common_pull: "{shared pull pattern}"
    key_quotes:
      - "{verbatim quote}" # {Name}
      - "{verbatim quote}" # {Name}

  - job: "{second job cluster if found}"
    frequency: "{N}/{total}"
    common_push: "{shared push pattern}"
    common_pull: "{shared pull pattern}"
    key_quotes:
      - "{verbatim quote}" # {Name}

emerging_signals:
  - signal: "{pattern seen in only 1 interview but worth watching}"
    source: "{filename}"
    note: "{why this might matter with more data}"

force_patterns:
  strongest_push: "{description with evidence}"
  strongest_pull: "{description with evidence}"
  strongest_anxiety: "{description with evidence}"
  strongest_habit: "{description with evidence}"

timeline_patterns:
  avg_passive_duration: "{approximate average}"
  common_active_trigger: "{pattern}"
  common_deciding_factors:
    - "{factor 1}"
    - "{factor 2}"
  common_onboarding_friction: "{pattern}"

evidence_gaps:
  - "{specific gap with recommendation}"
  - "{specific gap with recommendation}"

recommendations:
  - "{actionable recommendation connected to evidence}"
  - "{actionable recommendation connected to evidence}"
  - "{actionable recommendation connected to evidence}"

Filename Convention

Generate the filename as: patterns-{YYYYMMDD}.yml using today's date.

If a patterns file already exists for today, append -2, -3, etc.

Validate Output

After gen