researching

Researching

Build the context a task needs by investigating the current codebase and/or external prior art in one pass, then synthesizing a single research document that feeds planning. Run the internal dimension, the external dimension, or both.

Interaction mode

Leans Collaborative by default; see ai-research-workflows:using-research-workflows for the full protocol.

Checklist

Work through these in order and track them as tasks. Do not skip the review gates (steps 5, 7, and 8):

1. Explore context — the request, existing docs/rse/specs/ (and legacy .agents/) docs, recent commits
2. Scope — internal / external / both; what counts as relevant; depth
3. Propose the research plan — sub-questions + sources; get a quick OK
4. Investigate — run the codebase and/or prior-art pass
5. Present findings — in sections; confirm understanding before writing anything out
6. Write the research document — docs/rse/specs/research-<slug>.md from the template
7. Self-review — read the written doc with fresh eyes; fix issues inline
8. User reviews the document — have them go through the written doc before you move on
9. Hand off — point to the next skill

Process

Scope → plan → investigate → present → write → self-review → user review → hand off.

1. Explore context

Read the request. Scan docs/rse/specs/ (then legacy .agents/) for existing research and plan documents (research-*.md, and legacy prior-art-*.md). Note recent commits if relevant.

2. Scope the question

Establish: the precise question; whether it needs internal codebase research, external prior-art research, or both; what counts as relevant; and the desired depth. In Collaborative mode ask one focused question at a time; in Direct mode infer scope from the request and state it before proceeding.

3. Propose the research plan

Before investigating, present the decomposition: the codebase sub-questions you will trace and/or the prior-art sources and dimensions you will search. Get a quick OK (Collaborative) or state it and proceed (Direct).

4. Investigate

Run the dimension(s) in scope:

• Codebase pass — see references/codebase-research.md
• Prior-art pass — see references/prior-art-research.md

Both passes may surface gaps and light, clearly-labeled observations; defer deep critique and design decisions to planning or validation.

5. Present findings

Present synthesized findings in sections (codebase first, then prior art), confirming understanding as you go. Use file:line anchors for code and citations for external sources.

6. Write the research document

Derive a slug (lowercase, hyphenated). Read the template: assets/research-template.md. Fill the sections that apply and write to docs/rse/specs/research-<slug>.md. Omit sections for any dimension not investigated.

If the internal pass ran, record the codebase state you researched against — the commit SHA (git rev-parse --short HEAD) and the date — so file:line findings can be re-checked as the code evolves.

7. Self-review

Before showing the document, read it through with fresh eyes and fix issues inline:

• Placeholders — no "TBD"/"TODO", empty sections, or vague findings.
• Anchors & citations — every codebase finding cites a file:line; every prior-art claim cites a source.
• Consistency & coverage — sections don't contradict, the synthesis matches the findings, and every scoped sub-question is answered (or its gap is named).

Confirm the doc meets the Quality checklist below.

8. User reviews the document

Surface the written document and let the user go through it before you move on:

Research doc written to docs/rse/specs/research-<slug>.md. Please review it and tell me if anything is missing or off before we move on to planning.

In Collaborative mode, wait for their response; if they request changes, revise and re-run the self-review. In Direct mode, present the doc and proceed, noting it is ready for their review.

9. Hand off

Point to the next skill — usually ai-research-workflows:planning-implementations (or ai-research-workflows:running-experiments when an approach is uncertain).

Common Mistakes

• Researching both dimensions when one suffices — scope first; don't search external prior art for a question that is purely about current code, or vice-versa.
• Uncited prior-art claims — every external factual claim needs a primary-source citation; label secondary sources as such.
• Vague codebase findings — every code finding cites a specific file:line; "it's somewhere in auth" is not a finding.
• Skipping the disconfirming search — for prior art, actively search for limitations, critiques, and failures, not just supporting evidence.
• Turning research into a rewrite plan — surface gaps and light observations, but defer detailed design and critique to planning/validation.

Quality checklist

• Scope (internal / external / both) was established and stated
• Codebase findings carry file:line references (if the internal pass ran)
• Every prior-art claim is cited and ≥1 disconfirming search ran (if the external pass ran)
• Synthesis connects findings and names gaps
• Codebase state recorded (commit SHA + date) if the internal pass ran
• Template used; saved to docs/rse/specs/research-<slug>.md
• Document is self-contained
• Findings presented before writing; written doc self-reviewed and offered for user review before hand-off
• Next skill suggested

Cross-references

• Feeds ai-research-workflows:planning-implementations.
• For comparing uncertain approaches with real code, use ai-research-workflows:running-experiments.
• Surveyed automatically by ai-research-workflows:using-research-workflows.