domain-modeling
Build the project's shared language and bounded contexts before writing code, so names stay consistent and the agent stops paraphrasing domain concepts. Produces a CONTEXT.md glossary and decision records. Use at the start of a project or feature, or when the codebase and the people describing it speak different languages.
git clone --depth 1 https://github.com/rohitg00/pro-workflow /tmp/domain-modeling && cp -r /tmp/domain-modeling/skills/domain-modeling ~/.claude/skills/domain-modelingSKILL.md
# domain-modeling Most misbuilds start as a language gap: the agent is dropped into a project and left to infer the jargon, so it uses twenty words where the domain has one. A shared language closes the gap. When code, conversation, and the model all draw from the same vocabulary, names line up, navigation gets cheaper, and the model spends fewer tokens reasoning because it has a tighter language to reason in. ## Method 1. **Harvest the terms.** From the request, the codebase, and the user's own words, list the nouns and verbs that carry domain meaning - the concepts a newcomer would have to ask about. Prefer the user's word over a synonym you like better. 2. **Pin each one.** Write a one-line definition in the project's own language, not a dictionary definition. If two terms blur together, force the distinction or collapse them - ambiguity here becomes inconsistent names in code. 3. **Draw the boundaries.** Where the same word means different things in different parts of the system, that is a boundary. Name each context and note which terms belong to it. A term that means two things is two terms. 4. **Record the hard calls.** When a modeling choice was contested or will be questioned later, write a short decision record: context, choice, alternatives rejected, why. ## Outputs - **`CONTEXT.md`** - the shared-language glossary. One term per line: `term - what it means in this project`. Grouped by bounded context when there is more than one. Point every future session at this file. On re-run, add new terms and update definitions that changed; do not rewrite the file wholesale. - **Bounded-context sketch** - the contexts and which terms live in each, short enough to read in fifteen seconds. - **Decision records** in `docs/decisions/NNNN-slug.md` for the contested modeling calls only. Read the directory first and number from the highest existing record so two records never collide. Skip the obvious ones. ## Guardrails - The glossary is for the model as much as the human - write it to be loaded, not framed on a wall. - Do not invent terms the project does not use. Reflect the domain; do not rename it. - Keep it small and current. A glossary that lists everything and updates nothing is worse than none. Prune terms that fall out of use. ## Where it fits Run this before `plan-interrogate` on a new area, or let `plan-interrogate` call back here when it hits terms it cannot pin. The `CONTEXT.md` this produces is the same file `plan-interrogate` emits - one shared-language artifact, two ways in.
Coordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.
Auto-configure quality gates, hooks, and settings for a new project. Detects project type and sets up appropriate tooling. Use when onboarding a new codebase.
Decompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
Capture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".
Smart context compaction with state preservation. Saves critical files, task progress, and working state before compaction, restores after. Use before manual compact or when auto-compact triggers.
Analyzes and optimizes context window usage across sessions. Use when context feels bloated, sessions run slow, or approaching compaction limits.
Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
Optimize token usage and context management. Use when sessions feel slow, context is degraded, or you're running out of budget.