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engineering-foundations

Writing Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Writing
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
88 / 100 · community maintained
Author / version / license
@tomevault-io · no license declared
Token usage
Lean
Setup complexity
Plug-and-play
External API key
Not required
Operating systems
Unspecified (assume cross-platform)
Runtime requirements
No special requirements
Permissions
  • Read-only
  • Write / modify
Network behavior
Local-only
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview engineering-foundations.preview 
---
name: engineering-foundations
description: Language-agnostic engineering methodology — DDD, Hexagonal Architecture, TDD, architecture plann…
category: writing
runtime: no special runtime
---

# engineering-foundations output preview

## PART A: Task fit
- Use case: Language-agnostic engineering methodology — DDD, Hexagonal Architecture, TDD, architecture planning, requirements gathering, code review, and ADR writing. Use when the user asks HOW to approach something at the pattern/theory level, regardless of framework. This is the source of truth for DDD concepts (aggregates, domain events, rich models), architecture layers (presentation, application, domain, infrastructure), and testing methodology. Framework-specific implementations live in separate plugins (nestjs-toolkit, django-toolkit, nextjs-toolkit). Triggers for 'how should I test this', 'what is DDD', 'explain domain modeling', 'write an ADR', 'plan this feature', 'what are aggregates', 'hexagonal architecture'. DO NOT use for executing development work (use ai-dlc) or framework-specific questions (use the relevant framework plugin). Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Topics / Gotchas / General Principles” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Language-agnostic engineering methodology — DDD, Hexagonal Architecture, TDD, architecture planning, requirements gathering, code review, and ADR writing. Use when the user asks HOW to approach something at the pattern/theory level, regardless of framework. This is the source of truth for DDD concepts (aggregates, domain events, rich models), architecture layers (presentation, application, domain, infrastructure), and testing methodology. Framework-specific implementations live in separate plugins (nestjs-toolkit, django-toolkit, nextjs-toolkit). Triggers for 'how should I test this', 'what is DDD', 'explain domain modeling', 'write an ADR', 'plan this feature', 'what are aggregates', 'hexagonal architecture'. DO NOT use for executing development work (use ai-dlc) or framework-specific questions (use the relevant framework plugin). Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “Topics / Gotchas / General Principles” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Language-agnostic engineering methodology — DDD, Hexagonal Architecture, TDD, architecture planning, requirements gathering, cod…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Topics”, “Gotchas”, “General Principles”, “Quick Decision Guide”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name engineering-foundations directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Topics / Gotchas / General Principles” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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