Voice Learning Skill
Learn from user edits to improve the voice profile over time.
When NOT To Use
- Building the first profile (use
scribe:voice-extract)
- Reviewing text without changing the profile (use
scribe:voice-review)
Method: Three-Stage Comparison
Every piece flows through three stages:
- Pre-review: Raw generation output (before review agents)
- Post-review: After user accepts/rejects advisory fixes
- Post-edit: User's manually edited final version
The learning agent compares stages 2 and 3 (post-review vs
post-edit) to identify patterns in what the user changed.
These patterns inform register and rule updates.
Core Rules
- Sharpen, don't add: Modify existing rules to cover new
patterns. Rule bloat degrades output.
- Tag specificity: Register-specific patterns go to
registers. Universal patterns go to craft rules or agents.
- Flag contradictions: Opposite patterns across pieces
require user resolution.
- Evidence threshold: Patterns need 3+ instances (or 1-2
matching existing accumulator entries) before becoming rules.
- Detection surface: Structural changes increase AI
detectability. Craft-level changes are neutral. Prefer
craft-level updates.
- Rule count check: Suggest consolidation if any section
has 8+ rules.
Required TodoWrite Items
voice-learn:snapshots-loaded - All three stages readvoice-learn:diff-analyzed - Changes categorizedvoice-learn:accumulator-checked - Prior patterns reviewedvoice-learn:proposals-generated - Updates proposedvoice-learn:user-approved - Changes accepted by user
Step 1: Load Snapshots
Load: @modules/snapshot-management
PROFILE_DIR="$HOME/.claude/voice-profiles/{name}"
SNAP_DIR="$PROFILE_DIR/learning/snapshots"
Read all three stages for the target piece.
Step 2: Diff Analysis
Load: @modules/pattern-analysis
Compare post-review vs post-edit. Categorize every change:
| Category | Example |
|---|
| Tone adjustment | Softened a claim, added hedge |
| Voice insertion | Added parenthetical, aside, humor |
| Structure change | Broke paragraph, reordered |
| Precision edit | Replaced vague with specific |
| Deletion | Removed fluff or decoration |
| Addition | Added context, example, anchor |
Step 3: Check Accumulator
Read learning/accumulator.json:
{
"patterns": [
{
"id": "pat-001",
"category": "tone_adjustment",
"description": "Softens confident claims about tool capabilities",
"instances": [
{"piece": "blog-post-1", "date": "2026-04-08", "diff": "..."}
],
"target": "register",
"status": "accumulating",
"first_seen": "2026-04-08",
"last_seen": "2026-04-08"
}
],
"staleness_threshold_days": 30
}
Match new changes against existing patterns:
- Semantic similarity (same category + similar description)
- If match found: merge instance, check if threshold reached
- If no match: create new accumulator entry
Step 4: Generate Proposals
For patterns that reach threshold (3+ instances or 1-2
matching prior accumulator entries with 2+ instances):
Apply (strong evidence)
## Proposed Update
**Pattern**: {description}
**Target**: {register file or craft-rules.md}
**Evidence**: {N instances across M pieces}
| Piece | Date | Change Made |
|-------|------|-------------|
| ... | ... | ... |
**Proposed edit**:
- File: {path}
- Section: {section name}
- Current: "{current text or 'new addition'}"
- Proposed: "{new text}"
Hold (insufficient evidence)
Add to accumulator with current instances. Report:
Holding: "{pattern description}" (N instances, need 3+)
Contradictions
If a new pattern contradicts an existing accumulator entry:
Contradiction detected:
- Existing: "{accumulator pattern}"
- New: "{contradicting pattern}"
- Resolution required: user must choose
Step 5: User Approval
Present proposals to user:
Learning found N patterns ready to apply:
[1] {pattern}: {proposed change}
Evidence: {N instances}
[a]pply / [s]kip / [v]iew evidence?
[2] ...
Apply approved changes to the target files.
Staleness
Patterns in the accumulator expire after staleness_threshold_days
(default 30). If a pattern hasn't recurred within that window,
it was likely a one-off preference rather than a voice trait.
On each learning pass, prune stale entries:
Snapshot Capture
The learning system captures snapshots automatically when
voice-review completes. Snapshot naming:
{piece-filename}-{YYYYMMDD-HHMMSS}-pre-review.md
{piece-filename}-{YYYYMMDD-HHMMSS}-post-review.md
{piece-filename}-{YYYYMMDD-HHMMSS}-post-edit.md
The post-edit snapshot is captured when the user runs
/voice-learn after finishing their manual edits.
Exit Criteria
- Snapshots loaded and compared
- Changes categorized
- Accumulator checked and updated
- Proposals generated for threshold patterns
- User approved/rejected proposals
- Approved changes applied to profile files
- Stale accumulator entries pruned
