domain-modeling
Clarify domain concepts, language, boundaries, and durable architectural decisions. Use when terminology is ambiguous, the user is shaping a domain model, or a design depends on resolving what concepts mean.
- License 许可
- MIT
- 所属仓库
- stevesolun/ctx
- 最近更新
- 2026-10-15
- Stars
- ★ 593
- Forks
- 69
01 前言 Front Matter
- name
- domain-modeling
- description
- Clarify domain concepts, language, boundaries, and durable architectural decisions. Use when terminology is ambiguous, the user is shaping a domain model, or a design depends on resolving what concepts mean.
02 资源文件 3
- 参考文档ADR-FORMAT.md802 B
- 参考文档agents/openai.yaml101 B
- 参考文档CONTEXT-FORMAT.md1.1 KB
03 SKILL.md 正文
Model the domain
Build a precise shared understanding of the concepts that matter to the current decision. Read existing glossaries, context maps, ADRs, code, and tests when they provide relevant evidence.
Sharpen the model
- Identify overloaded terms, hidden distinctions, and conflicting definitions.
- Use concrete scenarios and edge cases to test whether concepts and boundaries hold.
- Compare the stated model with behavior in the code and surface material contradictions.
- Propose clear language when ambiguity is blocking progress, while respecting established repository terminology that remains accurate.
Ask the user to resolve a term only when their intent cannot be inferred safely and the distinction affects the outcome.
Record durable knowledge proportionally
Update a glossary or context map when the user requests it or when a resolved term is durable, project-specific, and useful beyond the current conversation. Follow an existing repository format first; otherwise use the context format as a lightweight starting point.
Record an ADR only when a decision is costly to reverse, surprising without its context, and based on a meaningful tradeoff. Follow existing ADR conventions or use the ADR format. Do not create artifacts merely to complete the workflow.
Keep domain definitions separate from implementation plans. Report unresolved ambiguity and artifact changes explicitly.
04 同仓库更多 Skills
toolbox
Pre/post dev toolbox — named bundles of skills/agents loaded before development work and councils of experts invoked after. Run /toolbox or the toolbox.py CLI to list, activate, initialize, export, import, and validate toolboxes. Invoke at the start or end of a dev session, when setting up a new repo, or when sharing toolboxes with a team.
skill-router
Alive skill router — reads the current project's stack and loads/unloads skills dynamically. Invoke at session start or when project context changes.
repo-stats-autoupdate
Keeps README badge + inline counts in sync with the real number of skills, agents, graph nodes/edges, communities, converted pipelines, and test inventory. Runs automatically on every commit via a git pre-commit hook. Use when the README drifts from reality or before publishing a release.
find-skills
Discover installable agent skills from ctx's shipped Skills.sh catalog, the Skills.sh search API, and the npx skills CLI. Use when a user asks whether a skill exists, wants to add/update a skill, or needs a repeatable procedure for finding candidate skills safely.
no-mistakes
Validate committed feature-branch changes through the no-mistakes pipeline: intent, rebase, review, test, docs, lint, push, PR, and CI. Use when the user asks to run no-mistakes, ship safely, validate before pushing, or gate a change before it reaches upstream.
zoom-out
Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.