Clarify what domain terms mean in their specific contexts, using repository documentation, code, callers, types, and tests. Surface disagreements between intent and behavior, distinguish competing meanings with examples, and identify compatibility work needed for shared-name changes.
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Settle what domain terms mean per context, and which repository conventions or language standards (Go, Python) apply. Use when: names disagree or a rename is proposed.
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Two independent uses: vocabulary work makes a term, name or rule boundary precise enough for acceptance examples, code and conversation; a standards lookup finds the language, data-format, risk or test-design standard for a change. Neither needs the other. Plan owns unified discovery and resumption; Domain resolves only the needed vocabulary or rule boundary and returns it to the existing intent.
Return vocabulary work in this shape, then stop once the next change can be named and judged consistently:
| Term | Context | Meaning | Source | Distinguishing example | Open question |
|---|---|---|---|---|---|
| <term> | <bounded context> | <definition> | <path:line, observed in code, or proposed> | <Given/When/Then or concrete case> | <caller decision, or none> |
After the table, list the translations between contexts, each located code-versus-intent disagreement, the compatibility cost of any proposed rename, and the owner updated or the text proposed for it. See caller vocabulary examples.
The synonym smuggling failure substitutes a word that changes a term's authority: calling a verdict a closure quietly assigns a tracker transition to judgment. Keep the original term when a substitute would move responsibility.
Only when AgentOps itself is the subject, load its owners from AgentOps terms. Do not apply AgentOps vocabulary to an unrelated caller domain.
Load only the language or risk guidance needed for the current change, starting from the common standards. Repository contracts and the actual toolchain take precedence. These references do not create a second approval or validation lane.
Idea provenance: Matt Pocock's engineering skills (domain modeling), adapted for AgentOps.
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Plan worthwhile local tasks by checking dependencies, effects, resources, and platform boundaries, then execute the exact approved plan. It can use safe parallel work or suitable Apple-silicon backends when the project exposes them.
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Record deferred engineering decisions and temporary workarounds in a debt registry, with context such as effort, ongoing cost, affected area, and payoff trigger. Entries receive short labels so they can be reviewed or dropped later.
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Generate or compare AI agent code for LangChain, EINO, AutoGen, AgentScope, and CrewAI using framework-specific templates, API mappings, conventions, and project structures. Reference files include verification dates to help identify potentially outdated guidance.
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Investigate a reproducible bug, test failure, or unexpected page behavior, then make a narrowly scoped fix. The workflow calls for a regression test before changing code, verifies the fix, and reports remaining risks and evidence while avoiding unrelated refactoring or feature work.
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Build an AI agent project through six collaborative stages, from requirements and architecture to tools, prompts, code generation, and verification. Start framework-independent, then generate a runnable project for frameworks such as LangChain, EINO, AutoGen, AgentScope, or CrewAI.
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Audit a project or learning workflow for scope drift, conflicting plans, inconsistent names, and code quality concerns. The skill reads project files and code, then reports evidence, severity, and suggested next steps without changing files.
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