message-parser

Parse raw WhatsApp exports (TXT or JSON) into normalized message objects with `timestamp`, `sender`, and `content`. Use when users ask to parse chat export, clean WhatsApp dump, or convert chat TXT to structured JSON before extraction. Recommended chain start: message-parser then lead-extractor. Do not use for lead interpretation, storage, summarization, or action suggestions.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "message-parser" with this command: npx skills add vishalgojha/message-parser

Message Parser

Convert raw chat exports into a strict array of parsed message objects.

Quick Triggers

  • Parse this WhatsApp export file.
  • Convert this group dump into structured messages.
  • Clean this TXT chat into timestamp/sender/content rows.

Recommended Chain

message-parser -> lead-extractor -> india-location-normalizer -> sentiment-priority-scorer -> summary-generator -> action-suggester -> lead-storage

Execute Workflow

  1. Accept raw WhatsApp export input as plain text, JSON, or file contents already loaded by Supervisor.
  2. Detect and parse the source format, including WhatsApp export lines like DD/MM/YYYY, HH:MM - sender: message.
  3. Normalize each event into exactly three fields:
    • timestamp (RFC 3339 date-time string)
    • sender (non-empty string)
    • content (string; allow empty message bodies)
  4. Merge multiline continuation lines into the previous message when they do not start with a new timestamp marker.
  5. Preserve message ordering. If timestamps collide, preserve original source order.
  6. Ignore chat-system boilerplate as lead content (encryption notice, group created, member added) while preserving raw line fidelity for audit.
  7. Validate output against references/parsed-message-array.schema.json.
  8. Return only the validated array.

Enforce Boundaries

  • Never infer or extract leads.
  • Never write to databases or files.
  • Never generate summaries or action plans.
  • Never send or schedule outbound communication.
  • Never bypass Supervisor routing.

Handle Errors

  1. Return explicit parse errors for malformed entries.
  2. Include line offsets when source lines are available.
  3. Fail closed if output cannot satisfy the schema.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

General

Ai Competitor Analyzer

提供AI驱动的竞争对手分析,支持批量自动处理,提升企业和专业团队分析效率与专业度。

Registry SourceRecently Updated
General

Ai Data Visualization

提供自动化AI分析与多格式批量处理,显著提升数据可视化效率,节省成本,适用企业和个人用户。

Registry SourceRecently Updated
General

Ai Cost Optimizer

提供基于预算和任务需求的AI模型成本优化方案,计算节省并指导OpenClaw配置与模型切换策略。

Registry SourceRecently Updated