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implementation-execution

Convert frozen brief.md and acceptance.feature specs into code with deviation reports.

5|1|Updated May 9, 2026
Search Tags:#workflow#documentation#implementation#branching#code-generation#spec-backflow
One-click install
npx skills add https://github.com/ql-link/LinkRag --skill implementation-execution
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: implementation-execution
Source: https://github.com/ql-link/LinkRag/tree/main/.ai/skills/implementation-execution
Command: npx skills add https://github.com/ql-link/LinkRag --skill implementation-execution

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill serves as the coding execution station in the "requirements → delivery" chain. It translates frozen artifacts like brief.md and acceptance.feature (and, when present, technical_design.md) into real code, and it outputs an implementation_report.md whenever the implementation diverges from the spec. It ensures that any gaps discovered during coding are back-propagated to the spec before continuing, preventing drift between documentation and implementation.

Core Features & Use Cases

  • Specification-to-code execution: converts confirmed specs into working implementation, with mandatory backflow of changes to spec when gaps are found.
  • Deviation reporting: generates implementation_report.md to document divergence from technical design or acceptance criteria.
  • Process governance: enforces the spec-backflow rules and records decisions to maintain alignment across branches and reviews.

Quick Start

Activate this skill when a feature has frozen specs and start converting brief.md / acceptance.feature into production-ready code, generating an implementation_report.md for any deviations.

Frequently Asked Questions about implementation-execution

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

FAQPage Schema
How do I convert frozen specifications into production-ready code?▼

To convert frozen specifications into production-ready code, execute defined brief.md and acceptance.feature workflows. This process translates confirmed specs into working implementation while enforcing strict spec-backflow policies for gap resolution.

What is spec-backflow and when do I need it during implementation?▼

Spec-backflow is a governance policy requiring that any gaps discovered during coding are back-propagated to frozen specifications before continuing. You need it to prevent drift between documentation and implementation during L3 feature development.

How do I document deviations from technical design during code generation?▼

You document deviations from technical design during code generation by producing an implementation_report.md. This report records divergence from technical_design.md or acceptance.feature criteria to maintain alignment across branches and reviews.

Do I need frozen specs before starting feature implementation?▼

Yes, you need frozen artifacts like brief.md and acceptance.feature before starting feature implementation. This coding execution station operates on confirmed specifications, ensuring coding follows strict spec-backflow rules rather than ad-hoc development.

What's the best way to maintain traceability between specs and code changes?▼

The best way to maintain traceability between specs and code changes is to align implementation with branch-pr-workflow during delivery while generating implementation_report.md for deviations. This enforces spec-backflow to back-propagate gaps to documentation.

Why does coding execution require back-propagating gaps to specifications?▼

Coding execution requires back-propagating gaps to specifications to prevent drift between documentation and implementation. The spec-backflow policy ensures discovered gaps are resolved in frozen specs before coding continues.

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