What makes voice-learn notable?

959 downloads. Trust evidence available

How should voice-learn be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

voice-learn

Improves a voice profile by learning from manual edits. Use after editing generated text to refine registers and close voice drift over time Skill: voice-learn Owner: athola Summary: Improves a voice profile by learning from manual edits. Use after editing generated text to refine registers and close voice drift over time Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:22:35.086Z | user Release v1.9.19 v1.9.17 | 2026-07-30T05:42:28.143Z | user Release v1.9.17 v1.9.16 | 2026-07-14T19:59:18.245Z | user Release v1.9.16 v1.9.14 | 2026-06-30T18:06

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

959

Updated

Oct 12, 2026

Version

1.9.19

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 959 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 959 downloadsadoption · observed Oct 12, 2026
Latest release: 1.9.19release · observed Aug 26, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.9.19release
Release v1.9.19
Observed Aug 26, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17emme0e2m3cpf7k2jvp3a84984b8z9:nm-scribe-voice-learn

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-scribe-voice-learn/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

81,361 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: voice-learn
description: |
  Improves a voice profile by learning from manual edits. Use after editing generated text to refine registers and close voice drift over time
version: 1.9.8
triggers:
  - voice
  - learning
  - improvement
  - feedback
  - iteration
metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/scribe", "emoji": "\u270d\ufe0f", "requires": {"config": ["night-market.scribe:voice-extract", "night-market.scribe:voice-generate", "night-market.scribe:voice-review"]}}}
source: claude-night-market
source_plugin: scribe
---

> **Night Market Skill** — ported from [claude-night-market/scribe](https://github.com/athola/claude-night-market/tree/master/plugins/scribe). For the full experience with agents, hooks, and commands, install the Claude Code plugin.


# Voice Learning Skill

Learn from user edits to improve the voice profile over time.

## Method: Three-Stage Comparison

Every piece flows through three stages:
1. **Pre-review**: Raw generation output (before review agents)
2. **Post-review**: After user accepts/rejects advisory fixes
3. **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

1. **Sharpen, don't add**: Modify existing rules to cover new
   patterns. Rule bloat degrades output.
2. **Tag specificity**: Register-specific patterns go to
   registers. Universal patterns go to craft rules or agents.
3. **Flag contradictions**: Opposite patterns across pieces
   require user resolution.
4. **Evidence threshold**: Patterns need 3+ instances (or 1-2
   matching existing accumulator entries) before becoming rules.
5. **Detection surface**: Structural changes increase AI
   detectability. Craft-level changes are neutral. Prefer
   craft-level updates.
6. **Rule count check**: Suggest consolidation if any section
   has 8+ rules.

## Required TodoWrite Items

1. `voice-learn:snapshots-loaded` - All three stages read
2. `voice-learn:diff-analyzed` - Changes categorized
3. `voice-learn:accumulator-checked` - Prior patterns reviewed
4. `voice-learn:proposals-generated` - Updates proposed
5. `voice-learn:user-approved` - Changes accepted by user

## Step 1: Load Snapshots

Load: `@modules/snapshot-management`

```bash
PROFILE_DIR="$HOME/.claude/voice-profiles/{name}"
SNAP_DIR="$PROFILE_DIR/learning/snapshots"

# Find the most recent snapshot set
# Format: {piece-name}-{timestamp}-{stage}.md
```

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 s

_meta.json

{
  "ownerId": "kn7d107jg9jv602h9ytsegydq184a42s",
  "slug": "nm-scribe-voice-learn",
  "version": "1.9.19",
  "publishedAt": 1787750555086
}

modules/pattern-analysis.md

---
module: pattern-analysis
category: voice-learning
dependencies: [Read, Agent]
estimated_tokens: 500
---

# Pattern Analysis Module

Analyze diffs between post-review and post-edit snapshots
to extract recurring edit patterns.

## Diff Categories

| Category | Signal | Example |
|----------|--------|---------|
| tone_adjustment | Softened/strengthened claims | "This proves" -> "This suggests" |
| voice_insertion | Added writer-specific moves | Added parenthetical aside |
| structure_change | Reordered, split, merged | Broke long paragraph into two |
| precision_edit | Vague -> specific | "many people" -> "the 12 engineers" |
| deletion | Removed fluff | Cut "It's worth noting that" |
| addition | Added context | Added a concrete example |
| humor_or_irreverence | Added informal voice | Added "(fuck GPTZero)" |
| hedge_or_commit | Changed certainty level | "will" -> "should" or vice versa |

## Analysis Prompt

```
Compare these two versions of the same text:

VERSION A (post-review, before manual edits):
{post_review_text}

VERSION B (post-edit, after manual edits):
{post_edit_text}

For each change the writer made:
1. Identify the specific edit (what changed)
2. Categorize it (from the categories above)
3. Describe the pattern (what principle drove this edit?)
4. Assess specificity: is this a general writing preference
   or specific to this voice?

Focus on RECURRING patterns, not one-off fixes. If the same
type of edit appears 2+ times, that's a voice signal.

Output as a list of patterns with evidence.
```

## Pattern Matching Against Accumulator

For each identified pattern, check the accumulator:

```python
def match_pattern(new_pattern, accumulator):
    """Match using category + semantic similarity."""
    for existing in accumulator["patterns"]:
        if existing["category"] == new_pattern["category"]:
            # Semantic similarity check (same intent?)
            if similar_description(existing, new_pattern):
                return existing  # Merge with this entry
    return None  # New pattern
```

## Evidence Requirements

| Action | Threshold |
|--------|-----------|
| Apply to register | 3+ instances across 2+ pieces |
| Apply with accumulator match | 1-2 new and 2 prior instances |
| Hold in accumulator | 1-2 instances, no prior match |
| Discard | Contradicts 3+ counter-examples |

## Output Format

```json
{
  "patterns_found": [
    {
      "category": "tone_adjustment",
      "description": "Softens confident claims about capabilities",
      "instances": [
        {"line": 12, "before": "...", "after": "..."},
        {"line": 34, "before": "...", "after": "..."}
      ],
      "recommendation": "apply|hold|discard",
      "target": "register|craft-rules|prose-reviewer",
      "proposed_edit": "..."
    }
  ]
}
```

modules/snapshot-management.md

---
module: snapshot-management
category: voice-learning
dependencies: [Bash, Write, Read]
estimated_tokens: 400
---

# Snapshot Management Module

Capture and organize text snapshots for the learning loop.

## Snapshot Stages

| Stage | When Captured | Purpose |
|-------|--------------|---------|
| pre-review | After generation, before review agents | Baseline output |
| post-review | After user accepts/rejects advisories | Review impact |
| post-edit | When user runs /voice-learn | Manual edit patterns |

## File Naming

```
{piece-name}-{YYYYMMDD-HHMMSS}-{stage}.md
```

Example:
```
blog-api-design-20260410-143022-pre-review.md
blog-api-design-20260410-143022-post-review.md
blog-api-design-20260410-145511-post-edit.md
```

## Capture Commands

### Pre-review (automatic)

Called by voice-generate after producing text:
```bash
SNAP_DIR="$HOME/.claude/voice-profiles/$PROFILE/learning/snapshots"
PIECE_NAME="$1"
TIMESTAMP=$(date +%Y%m%d-%H%M%S)
cp "$OUTPUT_FILE" "$SNAP_DIR/${PIECE_NAME}-${TIMESTAMP}-pre-review.md"
```

### Post-review (automatic)

Called by voice-review after user completes advisory decisions:
```bash
cp "$REVIEWED_FILE" "$SNAP_DIR/${PIECE_NAME}-${TIMESTAMP}-post-review.md"
```

### Post-edit (user-triggered)

User runs `/voice-learn` after making manual edits:
```bash
cp "$EDITED_FILE" "$SNAP_DIR/${PIECE_NAME}-${TIMESTAMP}-post-edit.md"
```

## Snapshot Sets

A complete set has all three stages with matching piece-name
and timestamp prefix. Incomplete sets (missing post-edit) are
ignored by the learning agent until completed.

## Manifest Tracking

Update `manifest.json` learning section:
```json
{
  "learning": {
    "snapshot_count": 12,
    "complete_sets": 4,
    "last_capture": "2026-04-10",
    "last_learning_pass": "2026-04-09"
  }
}
```

## Cleanup

Snapshots older than 90 days with completed learning passes
can be archived or deleted. The accumulator retains the
pattern evidence independent of raw snapshots.

skill-card.md

## Description:

Improves a voice profile by learning from manual edits to refine registers and close voice drift over time.

This skill is ready for commercial/non-commercial use.

## Publisher:

[athola](https://clawhub.ai/user/athola)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and writers use this skill after manually editing generated text to compare review-stage and final drafts, extract recurring voice patterns, and propose updates to voice profile rules.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill can persist edited content, review snapshots, and profile changes under a local voice profile directory.

Mitigation: Use it only for voice-profile workflows where snapshot retention is acceptable, and avoid sensitive drafts unless local retention has been reviewed.

Risk: Snapshot path handling is under-scoped for piece names used in snapshot commands.

Mitigation: Constrain piece names to safe filename characters before any snapshot command runs.

Risk: Learned patterns can introduce incorrect or unwanted voice rules if weak signals are applied too early.

Mitigation: Require user approval for proposed updates and keep insufficient evidence in the accumulator until the documented threshold is met.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-scribe-voice-learn)
- [ClawHub publisher profile](https://clawhub.ai/user/athola)
- [Scribe plugin homepage](https://github.com/athola/claude-night-market/tree/master/plugins/scribe)

## Skill Output:

**Output Type(s):** [text, markdown, json, shell commands, configuration, guidance]

**Output Format:** [Markdown guidance with JSON pattern summaries and shell command examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May propose edits to local voice profile files after user approval.]

## Skill Version(s):

1.9.19 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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