factory-worklog

Enforce per-feature JSONL audit trail logging for agent actions in SDLC workflows.

1|Updated Apr 8, 2026
One-click install
npx skills add https://github.com/e2its/myrmion-AI-factory --skill factory-worklog
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: factory-worklog
Source: https://github.com/e2its/myrmion-AI-factory/tree/main/.claude/skills/factory-worklog
Command: npx skills add https://github.com/e2its/myrmion-AI-factory --skill factory-worklog

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unenforced action logging in multi-agent software development workflows leads to gaps in traceability, making it difficult to audit changes, debug issues, or track progress across features and increments.

Core Features & Use Cases

  • Per-Feature JSONL Audit Trails: Segregates action logs by feature ID for isolated, easy-to-query tracking of all work done on a specific feature.
  • Mandatory Agent Action Registration: Requires all agents to log every action with timestamps, phase context, status, and attribution to the executing agent for full accountability.
  • Use Case: For a team building a customer management feature, this skill ensures every action from blueprinting to QA is logged, so you can trace exactly which agent implemented a specific increment, when, and with what result.

Quick Start

Use the factory-worklog skill to register your latest implementation action for feature USR-001 with status COMPLETED and observation "Fixed login validation bug".

Frequently Asked Questions about factory-worklog

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce audit trail logging for agent actions in software development workflows?

Mandatory audit trail logging for agent actions is enforced by requiring every agent to log actions with timestamps, phase context, status, and attribution in segregated per-feature JSONL files. This ensures full traceability and accountability across governed SDLC processes.

Why does unenforced action logging create traceability gaps in multi-agent development?

Unenforced action logging creates traceability gaps because agents may skip recording their actions, making it impossible to audit changes, debug issues, or track progress accurately across features and increments. Mandatory per-feature JSONL logging eliminates these blind spots.

What is the best way to track progress for individual features across end-to-end SDLC phases?

The best way to track feature progress across SDLC phases is by segregating JSONL audit trails by feature ID, mapping phase context to status, and maintaining a global index. This isolates logs for easy querying of all work done on a specific feature.

Can I use JSONL audit logs for agent attribution and compliance debugging?

Yes, JSONL audit logs capture agent attribution by requiring all agents to register every action with timestamps and executing agent identity. This satisfies compliance requirements for immutable action attribution and provides a reliable record for debugging.

Does per-feature action logging work for isolated progress tracking in governed workflows?

Per-feature action logging works for isolated progress tracking by segregating logs by feature ID within governed workflows. This allows teams to query a specific feature's audit trail independently to trace exactly which agent implemented an increment and when.

When do I need per-feature JSONL logging for software development governance?

You need per-feature JSONL logging when your SDLC governance requires immutable action attribution, phase-to-status mapping, and increment-level progress tracking for compliance and debugging across multi-agent software development workflows.