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v1.0.0
GitHub

About this Skill

Perfect for Python-based AI Agents needing advanced document workflow management with Docling and DOCX project-docling-engineer is a skill that enables developers to optimize Python + Docling + DOCX workflows with a focus on architecture clarity, testability, and staged delivery.

Features

Proposes minimal viable architecture changes with explicit tradeoffs
Implements changes in thin vertical slices using Python
Utilizes commands like `tree` and `rg` for inspection
Supports staged delivery for production-grade changes
Prioritizes testability and architecture clarity
Works with DOCX file formats for document generation

# Core Topics

qcdeveloper3-cmd qcdeveloper3-cmd
[0]
[0]
Updated: 2/21/2026

Quality Score

Top 5%
44
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
> npx killer-skills add qcdeveloper3-cmd/new-chapter/project-docling-engineer
Supports 18+ Platforms
Cursor
Windsurf
VS Code
Trae
Claude
OpenClaw
+12 more

Agent Capability Analysis

The project-docling-engineer MCP Server by qcdeveloper3-cmd is an open-source Community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use project-docling-engineer, project-docling-engineer setup guide, python docling workflow optimization.

Ideal Agent Persona

Perfect for Python-based AI Agents needing advanced document workflow management with Docling and DOCX

Core Value

Empowers agents to plan and implement production-grade changes for Python + Docling + DOCX workflows, prioritizing architecture clarity and testability through staged delivery and minimal viable architecture changes

Capabilities Granted for project-docling-engineer MCP Server

Implementing production-grade DOCX document generation
Optimizing Python workflows for Docling integration
Debugging and testing DOCX document rendering issues

! Prerequisites & Limits

  • Requires Python programming knowledge
  • Limited to DOCX file format
  • Dependent on Docling library
Project
SKILL.md
2.3 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
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Project Docling Engineer

Overview

Plan and implement production-grade changes for Python + Docling + DOCX workflows in this repo. Prioritize architecture clarity, testability, and staged delivery over quick but fragile code.

Workflow

  1. Confirm target outcomes and acceptance criteria before writing code.
  2. Inspect current state first (tree, rg, config, tests, CI).
  3. Propose minimal viable architecture changes with explicit tradeoffs.
  4. Implement in thin vertical slices:
    • keep stage boundaries clean (preprocess, analyze, render-docx, validate)
    • use interfaces/adapters for engines and fallbacks
    • keep IR stable and version-aware
  5. Add verification with every slice:
    • unit tests for pure logic and schema
    • CLI smoke tests for orchestration
    • artifact checks for deterministic outputs
  6. Report residual risks and clear next steps.

Implementation Rules

  • Keep modules cohesive; avoid monolithic stage files.
  • Preserve editability in DOCX output: prefer native paragraphs/tables/checkbox-like symbols before raster overlays.
  • Preserve geometry explicitly in IR; avoid lossy implicit conversions.
  • Treat mixed-direction text as first-class: store direction metadata on lines/spans/cells.
  • Make fallback behavior explicit and observable in logs/metadata.
  • Avoid hidden global state; pass context/config through stage boundaries.

Quality Gates

  • Run lint/format/type/test before finalizing:
    • ruff check src tests
    • ruff format --check src tests
    • mypy src
    • pytest
  • Run CLI smoke checks:
    • python -m docmirror --help
    • python -m docmirror run-all <sample.jpg> -o out
  • Validate that logs and debug artifacts are generated in configured paths.

Use Bundled References

  • For Docling integration details and option selection: read references/docling-implementation-guide.md.
  • For engineering behavior and delivery checks: read references/engineering-checklist.md.
  • For DOCX/RTL implementation notes: read references/docx-rtl-notes.md.

Use Bundled Script

  • scripts/run_quality_gate.py runs the standard local quality checks in one command.

Output Expectations

  • Deliver concrete code changes, validation evidence, and a short risk list.
  • Do not stop at abstract advice when implementation is feasible.

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