ai-research-workflows

Plugin ai-research-workflows from marketplace rse-plugins in uw-ssec/rse-plugins. Commands read from the repo's manifest — 12 days ago.

Skills (10)

• creating-handoffs

  Use when work context must transfer to another session or person. Triggers: create a handoff, hand off this work, summarize context for the next session, prepare a handoff.

• ensuring-reproducibility

  Use when a result, experiment, or analysis must be reproducible by others or by a future session. Triggers: make this reproducible, capture provenance, pin the environment for this result, why can't I reproduce X.

• hardening-research-code

  Use when research or scientific code must be trustworthy — verifiably correct, regression-safe, and numerically stable. Triggers: harden this code, is this numerically correct, add regression tests vs known results, make the research code robust.

• implementing-plans

  Use when an approved implementation plan (a docs/rse/specs/plan-*.md file) exists and the next step is writing the code. Triggers: implement the plan, execute the plan, start building, run the plan.

• iterating-plans

  Use when an existing implementation plan needs changes before or during execution. Triggers: update the plan, change the plan, add a phase, revise scope, incorporate experiment results.

• planning-implementations

  Use when a feature, refactor, or multi-file change needs to be designed before coding. Triggers: plan X, design the implementation, how should we build X, create an implementation plan.

• researching

  Use when you need to understand how an existing codebase works and/or survey external prior work (papers, methods, tools) before planning or changing something. Traces function call paths, maps module dependencies, searches academic papers and documentation, compares library alternatives. Triggers:

• running-experiments

  Use when the best technical approach is genuinely uncertain and a head-to-head prototype comparison is needed before committing to a design. Triggers: should I use X or Y, compare approaches, benchmark, which is faster or simpler.

• using-research-workflows

  Use when starting or continuing ANY research-software task — understanding or modifying code, surveying prior art, planning, experimenting, implementing, validating, reproducing, or hardening — and before reading code to "just check", before editing, or before answering "how does X work". Triggers:

• validating-implementations

  Use when an implementation is (claimed) complete and must be checked against its plan before shipping. Triggers: validate the implementation, verify it matches the plan, is the implementation correct, check before PR.

Slash commands (9)

• /ai-research-workflows:experiment

  Experiment with different approaches before committing to implementation

• /ai-research-workflows:handoff

  Create a handoff document to transfer work context to another session

• /ai-research-workflows:harden

  Make research/scientific code robust with correctness, regression, and stability checks

• /ai-research-workflows:implement

  Implement changes based on an approved plan, tracking progress phase by phase

• /ai-research-workflows:iterate-plan

  Update an existing implementation plan based on feedback

• /ai-research-workflows:plan

  Create a detailed implementation plan through interactive research and iteration

• /ai-research-workflows:reproduce

  Capture environment, data, seeds, and config so a result can be reproduced

• /ai-research-workflows:research

  Research the codebase and/or external prior art to build context for a task

• /ai-research-workflows:validate

  Validate that an implementation was correctly executed against its plan

Subagents (1)

• research-workflow-orchestrator

  Guides users through structured AI-enabled research workflows for software development. Orchestrates the full suite of research workflow skills — from codebase research and prior-art review through planning, experimentation, implementation, validation, reproducibility, and handoff.

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