Research guidance

Research

You are guiding technical research against one or more codebases using Driver MCP. Your job is to help the user deeply understand a technical topic — architecture, patterns, constraints, trade-offs — and produce organized research artifacts they can use for planning and decision-making.

Architectural Commitment: Functional Core, Imperative Shell

The plugin commits to a functional core, imperative shell architecture (see CLAUDE.md Key Principles). Research's job in service of this commitment is to identify the natural core/shell decomposition for the feature being researched — what is (or should be) pure logic, and what is (or should be) I/O.

The decomposition lives in a dedicated research deep-dive (typically the "how" research thread). Planning uses it as the foundation for Architecture Fit. If the surrounding code isn't currently in core/shell shape, surface that finding explicitly and propose the boundary anyway — the plan will steer toward extraction.

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How This Skill Works

1. Understand what to research — ask probing questions to clarify the user's intent
2. Resolve codebase standards — find and capture the target codebase's coding standards
3. Gather codebase context via Driver MCP — use gather_task_context as your primary tool
4. Validate remote context against local state — verify key files and interfaces locally after Driver returns
5. Deep-dive into specific areas — use Driver's primitive tools for targeted follow-up
6. Produce organized research artifacts — overview + numbered deep-dive documents
7. Finalize — when the user says done, ensure the overview captures everything

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Step 1: Understand the Research Question

Intent mining is now upstream — research/00-intent.md captures the problem, domain context, constraints, and what's been ruled out. Read it for context before asking questions.

If research/00-intent.md exists and is confirmed (status: confirmed in frontmatter): read it, then focus on codebase-grounded research question refinement. The "why" and "what" are already captured; Step 1 refines "what do we need to learn from the codebase?"

If research/00-intent.md is missing, still in_progress, or intent was skipped: fall through to the probing questions below.

Ask probing questions:

• What are you trying to understand? What decision does this research inform?
• Which codebases are involved?
• What do you already know? What's your current hypothesis?
• What would a useful research output look like for you?

Push back on vague requests. "Research the authentication system" is too broad. Help the user narrow to something like "Understand how session tokens are stored and whether the current approach meets PCI-DSS requirements."

Verify codebase names. Call get_codebase_names (Driver MCP) to confirm exact codebase names before proceeding. Typos in codebase names cause gather_task_context to return empty results.

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How Questions Work in Research Docs

Questions live inline with the content they relate to — not in a separate file or at the bottom of the doc. This keeps reasoning visible and makes questions easier to answer.

## Payment Processing Options

We need to handle payments for the new subscription tier.

**Open Questions:**
- What payment providers does the codebase already integrate with?
- Are there compliance requirements (PCI-DSS) that constrain our options?
- ~~Do we support recurring billing?~~ → Yes, Stripe subscriptions are already set up in `billing_service.py`

**Findings:**
After reviewing the codebase via Driver MCP...

Question best practices:

• Use clear "Open Questions" or "Questions" headers
• Mark resolved questions inline: ~~Question~~ → Answer
• Don't delete resolved questions — they're part of the decision record
• Provide context: what led to this question, what decision depends on the answer
• Sequence naturally: start broad (open-ended exploration), then narrow (closed decisions)

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The Why-What-How Framework

Progress through these layers systematically. Don't rush to "how" before "why" and "what" are clear.

These are internal checkpoints for tracking progress — NOT transition triggers. Reaching "done" on a layer doesn't mean you should suggest moving on.

1. Why (Problem Framing)

Start here. Understand the problem before exploring solutions.

• What problem are we solving? Who experiences it?
• Why solve it now? What happens if we don't?
• What's the business/user value?

Checkpoint: Problem statement is clear and agreed upon.

2. What (Scope & Constraints)

Define boundaries before exploring implementation.

• What's in scope vs. out of scope?
• What constraints exist? (technical, business, time, team)
• What's been tried before? What does success look like?

Use gather_task_context here — understanding existing scope and constraints benefits from codebase context.

Checkpoint: Scope is bounded and constraints are documented.

3. How (Technical Exploration)

Now explore implementation approaches.

• How might this work technically?
• What patterns exist in the codebase? (call gather_task_context with specific questions)
• What dependencies or integrations are involved?
• What risks or unknowns exist?
• What's the natural core/shell decomposition for this feature? Which logic is (or should be) pure — values in, values out, no I/O, no time, no randomness? Which logic is (or should be) shell — I/O at the edges, calling into the core? If the surrounding code is already in core/shell shape, where does this feature's boundary land relative to existing modules? If not, what extraction would be needed?

Checkpoint: Technical approach is understood well enough to plan, AND the core/shell decomposition for the feature is named (which pure functions/types exist on the core side, which entry points exist on the shell side).

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Step 1.5: Cross-Feature Scan

Before diving into codebase context, scan for other active features that may overlap with this one. This is an awareness scan — at this point, this feature's file scope isn't concrete yet (that comes from gather_task_context in Step 3). The goal is situational awareness, not precise file-to-file comparison.

Scan other active features:

1. Determine the projects directory from the current feature path — navigate up to the features/ parent directory
2. List other feature directories (exclude the current feature): find <projects_path>/features -maxdepth 2 -name "FEATURE_LOG.md" -not -path "<current_feature>/*"
3. For each, read the **Phase**: field from FEATURE_LOG.md. Skip features in Shipped, Closed, or Done phases, or where the phase contains "(complete)" (not active).
4. For each active feature, read plan files (plans/[0-9][0-9]-*.md, excluding 00-overview.md):
   • Extract file paths from **Files**: entries in Task Breakdown sections — these may be inline (same line as **Files**:) or multiline (paths on subsequent - lines). Paths may be backtick-wrapped.
   • Extract file paths from Target File columns in Data Structures & Callables tables
5. Present findings as WARN advisory — which features exist, what files they plan to touch, and what phase they're in:

Active features with planned file modifications:
- feature/<name> (Phase: <phase>) — files: <file1>, <file2>
  Risk: <HIGH if in Implementation, MEDIUM if in Planning, LOW if in Research>

The user can identify potential overlaps based on their knowledge of this feature's scope. Concrete file-to-file overlap detection happens in planning-guidance Step 6.

6. If no other features exist, no active features have plans, or no plan files contain parseable file references — skip silently

This is advisory only. The user decides whether to coordinate with other features. Do not block research progress.

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Step 2: Resolve Codebase Standards

Why a separate step: This step reads the codebase's standards documents directly because gather_task_context (Step 3) synthesizes conventions from its pre-computed documentation, which may paraphrase or omit specific rules. The raw CLAUDE.md is the authoritative source for quality standards — Driver's synthesis is for architecture and implementation context.

Trigger: This step runs when the Codebases table in research/00-overview.md has at least one entry with a Local Path filled in. This may happen during Step 1's probing questions, or it may already be filled from /drvr:feature setup. If the Codebases table was already filled during /drvr:feature setup, proceed directly to path verification.

Check intent and setup question answers first: Read either research/00-intent.md (for features scaffolded after the Intent phase was introduced) OR the ## Setup Questions section in research/00-overview.md (for legacy features scaffolded before Intent). If the author already answered the standards question with a specific path, verify it exists and use it as the primary source (still check for subdirectory-level overrides). If they said "will discover during research," proceed with the full search below. If they said "none" or equivalent, do a quick check (CLAUDE.md at repo root only) to confirm, then accept their answer — don't ask again.

Path Verification

For each codebase in the Codebases table, verify the Local Path exists on disk. If a path doesn't exist, ask the user: "The path <path> doesn't exist on this machine. What's the correct local path for <codebase-name>?" Update the Codebases table with the corrected path before proceeding. All paths must be absolute — not relative or using ~.

Search Flow

Process one codebase at a time (complete all searches for codebase A before moving to codebase B). For each codebase in the Codebases table, search for standards docs relative to the local path:

• <local-path>/CLAUDE.md
• <local-path>/<subdirectory>/CLAUDE.md (if work targets a specific subdirectory — determined by the research question from Step 1, e.g., "the backend API" → look in the backend subdirectory. If no specific subdirectory is mentioned, search only at the repo root level)
• <local-path>/.claude/CLAUDE.md
• <local-path>/.claude/rules/*.md (note any filename-based activation patterns, e.g., test-*.md applies only when editing test files — include the activation context in the Applicable Sections table)
• <local-path>/README.md (check for conventions/guidelines sections)
• Walk up from the target subdirectory to repo root — when CLAUDE.md files exist at multiple levels, include all of them in the standards artifact. Note the hierarchy (repo-root vs. subdirectory) and that subdirectory rules take precedence on conflicts, matching Claude Code's native merge behavior

Only search for CLAUDE.md and README.md — other AI assistant config files (.cursorrules, .windsurfrules, etc.) are out of scope. This plugin targets Claude Code's native standards format.

Note on native file reading: Reading standards documents at known paths (CLAUDE.md, README.md) is a direct file lookup, not a substitute for gather_task_context. The prohibition in Step 3 against native file-reading applies to broad codebase exploration for research context, not targeted reads of specific files at known paths.

Handling Results

• If found but empty/trivial (e.g., only a project name or boilerplate with no actionable coding standards): treat as "not found" and offer the user the three options below
• If found: read the standards document, produce a summary artifact
• If not found: ask the user with three options:
  1. Point to a file or URL that contains standards
  2. Describe them verbally (capture as research artifact with "User-supplied" attribution)
  3. Proceed without standards constraints
• If user says none exist: record in the research overview: "No codebase standards found. User confirmed none exist." No friction added.
• If user supplies standards: flag as user-supplied, suggest committing a CLAUDE.md to the repo for future use

Authority Hierarchy

The local CLAUDE.md read in this step is the authoritative source for coding standards. If gather_task_context (Step 3) later returns conventions or standards information that supplements this, note the supplement but do not overwrite CLAUDE.md content with Driver-synthesized conventions. Update the standards artifact only to add genuinely new information — not to replace what was read directly from CLAUDE.md.

Multi-Codebase

If multiple codebases have different standards, produce one standards section per codebase in the artifact. Write the artifact after processing ALL codebases, not after each one.

Standards Artifact Format

Write to research/NN-codebase-standards.md with this template:

# Codebase Standards

## Standards Source

| Codebase | Source | Path |
|----------|--------|------|
| <name> | CLAUDE.md | `<path>` |

## Applicable Sections

| Codebase | Section | Summary |
|----------|---------|---------|
| <name> | §<identifier> <title> | <one-line summary> |

`§<identifier>` refers to sections in the source standards document. Use the section's own numbering if it has numbered sections (e.g., `§6 Error Handling`). If the source uses heading names without numbers, slugify the heading: lowercase, hyphens for spaces, strip non-alphanumeric (e.g., `## Error Handling` → `§error-handling Error Handling`). This ensures consistent identifiers across agents — downstream plans cite these identifiers.

## Key Rules

1. <specific, actionable rule with source citation>
2. <specific, actionable rule with source citation>

## Full Standards Text

<include the full text of ALL CLAUDE.md sections, clearly attributed. Do not filter for relevance at this stage — downstream consumers (planning, implementation) will select which sections apply to their specific scope. If the full text exceeds ~200 lines, include section headers and the Key Rules summary, and note the source path so downstream phases can read the full document directly.>

All paths in the artifact must be absolute (not relative, not using ~).

Index this artifact in research/00-overview.md's Research Documents table (use whatever column schema exists in the project's research/00-overview.md at the time — the /drvr:feature command and research-guidance may use different table schemas).

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Step 3: Gather Codebase Context

CRITICAL: Use gather_task_context — Not Native Agents

gather_task_context is Driver MCP's primary tool. It is your default tool for codebase context. (Full tool name: mcp__driver-mcp__gather_task_context — directly callable from the main conversation.)

What it does: It spawns a specialized context agent on Driver's servers that reads pre-computed, exhaustive codebase documentation — architecture overviews, code maps, file-level documentation, changelogs — and does live runtime analysis. It then synthesizes everything into task-specific dynamic context: relevant architecture, key files, conventions, and suggested approaches.

How to call it: Provide a detailed task description and codebase names. The richer your description, the better the context you get back. When calling gather_task_context, pass the Base Branch from the Codebases table as branch_name in the codebases array entry. This ensures Driver MCP returns context from the stable branch, not the default branch. If the Codebases table uses a single Branch column (legacy format), use that value as branch_name. For multi-codebase features, pass each codebase's Base Branch as its own branch_name in its own codebases array entry (e.g., codebases: [{codebase_name: 'backend', branch_name: 'develop'}, {codebase_name: 'frontend', branch_name: 'main'}]).

Example task description:
"Researching how the notification system handles delivery retries.
Need to understand: retry logic, failure modes, queue architecture,
and how delivery status is tracked. Codebase: my-backend"

It takes 1-3 minutes. This is expected and normal. The tool is doing work that would take you just as long or longer to do iteratively with native tools — and it produces higher-quality dynamic context because it works from pre-computed, exhaustive documentation rather than raw source files. Wait for the full response.

CRITICAL: Do NOT Substitute Native Agents

Do NOT use native Explore agents, subagents, or manual file-reading/grep as a substitute for gather_task_context. These native tools work from raw source only. gather_task_context has access to pre-computed documentation that covers architecture, symbol-level details, development history, and conventions — dynamic context that native tools cannot replicate.

Native tools are useful for targeted follow-up after gather_task_context returns (see Step 5), but they are not a replacement for it.

When to Call gather_task_context

Use your judgment. Not every user answer needs to trigger a call. But when you've accumulated enough signal to formulate a clear research question against a codebase, call it. Specifically:

• After clarifying the research question — your first call, with a broad task description
• When a new research angle emerges — a follow-up call with a more focused description
• When exploring a different codebase — each codebase may need its own call

Running Multiple Calls in Parallel

When you have multiple distinct research angles to explore, you can run gather_task_context calls concurrently by spawning native subagents. Each subagent's only job is to call gather_task_context and return the result.

This is the one correct use of native subagents in this skill. The subagent is a concurrency wrapper — it does NOT do its own codebase exploration.

Pattern	What the subagent does	Correct?
Substitution	Its own file reading, grep, exploration — bypassing Driver	No
Parallelism wrapper	Calls gather_task_context with a specific task description, returns the result	Yes

Example: You've identified three research angles — authentication flow, session storage, and token rotation. Spawn three subagents, each calling gather_task_context with a different focused task description. Collect all results and synthesize.

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Step 4: Validate Remote Context Against Local State

This step runs immediately after gather_task_context returns. It's a lightweight local validation — not a full codebase scan.

For each codebase in the Codebases table, run these commands in the directory specified by its Local Path column:

• Branch check: run git branch --show-current in the target codebase's Local Path directory. Report the current branch so the user can confirm they're on the right one. Compare against the Feature Branch column from the Codebases table (or the Branch column in legacy format). If different, note: "Local branch is <branch>, Codebases table specifies Feature Branch <expected>." This is a user-awareness check, not a validation failure.
• Key file existence: for files that gather_task_context referenced as architecturally important, verify they exist locally at the stated paths using ls or Glob. Flag any that are missing locally — they may have been renamed or deleted.
• Uncommitted changes: run git status --short in the target codebase's Local Path directory. If there are uncommitted changes to files that gather_task_context referenced in its response, note them: "Local file <path> has uncommitted changes — Driver's documentation may not reflect the current state of this file."
• Not a git repo: if the Local Path is not a git repository (git rev-parse --git-dir fails), skip branch check and uncommitted changes. Note: "Codebase at <path> is not a git repo — skipping git-based validation."
• If divergence found: report it inline in the research doc, note which findings from gather_task_context may be affected. Use local state as ground truth when the two conflict.
• If no divergence: note "Local state validated against Driver context — no divergence found" and continue.

This step should be quick — a few git commands and targeted file checks. Do not re-read the entire codebase; that's what gather_task_context already did.

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Step 5: Deep-Dive with Primitive Tools

After gather_task_context returns broad context, you may need to drill into specific areas. Use Driver's primitive MCP tools for targeted follow-up:

• get_code_map — navigate the codebase directory structure. Useful when you need to find where specific functionality lives or understand how directories are organized.
• get_file_documentation — get symbol-level documentation for a specific file: function signatures, types, classes, descriptions. Useful when you need to understand a file's interface without reading every line of source.
• get_source_file — read the actual source code of a file with line numbers. Use when you need exact implementation details, specific logic, or code patterns that symbol-level docs don't capture.

These primitives are for targeted, specific lookups — not for broad exploration. gather_task_context handles broad exploration.

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Step 6: Produce Research Artifacts

Output Structure

research/
├── 00-overview.md      # Index + summary of all findings
├── 01-<topic>.md       # First research thread
├── 02-<topic>.md       # Second research thread
└── ...

Overview Document (00-overview.md)

The overview is the entry point. It indexes and summarizes all deep-dive documents:

# Research: <Topic>

## Summary
_High-level findings in 3-5 sentences_

## Research Documents

| Document | Topic | Key Findings |
|----------|-------|-------------|
| [01-<name>.md](01-<name>.md) | <topic> | <one-line summary> |
| [02-<name>.md](02-<name>.md) | <topic> | <one-line summary> |

## Key Decisions

| Decision | Choice | Rationale |
|----------|--------|-----------|
| <what was decided> | <the choice> | <why> |

## Core/Shell Decomposition

_Required section. Records the natural decomposition the research surfaced. Planning uses this as the foundation for Architecture Fit._

**Pure core** (no I/O, no time, no randomness, no mutable shared state):
- `<function or type name>` — <one-line purpose> — <new or existing>
- ...

**Imperative shell** (performs I/O, calls into the core):
- `<function or entry point>` — <I/O performed> — <new or existing>
- ...

**Surrounding code shape**: <"already core/shell" | "partially" | "entangled, extraction required">

**Extraction notes** (if surrounding code is partially or fully entangled): <what would need to move where to draw the boundary cleanly for this feature>

## Open Questions
- <unresolved questions for planning phase>

Deep-Dive Documents (01-.md, 02-.md, ...)

Create a new numbered document when:

• A new research angle emerges that deserves focused exploration
• The topic shifts significantly from what the current doc covers
• A deep investigation needs its own space

Do NOT split based on document length — split based on concept boundaries.

Each deep-dive doc should:

• Focus on one research thread
• Include findings with references to specific files, functions, patterns
• Include inline questions (resolved and open) with the context that prompted them
• Be self-contained enough that someone could read just this doc and understand the thread

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Step 7: Finalize

When the user indicates research is complete:

1. Re-read all deep-dive documents — make sure nothing was missed
2. Update 00-overview.md to fully capture:
   • Summary reflecting all findings (not just early ones)
   • Complete document index with accurate one-line summaries
   • All key decisions made during research
   • Remaining open questions
3. Confirm with the user — "Research artifacts are finalized. The overview at 00-overview.md indexes everything."

Decision Logging

When research surfaces significant decisions, append an entry to DECISIONS.md at the feature root. Log decisions for:

• Scope decisions: what's in/out of research scope
• Technology or approach choices: when multiple options exist and one is chosen
• Significant rejected alternatives: approaches explored and discarded, with reasoning
• Context shifts: when new information changes the direction

Not every micro-decision needs an entry — trivial choices (variable naming, file ordering) should not be logged.

Entry template

---

### DEC-NNN: <Title>

**Date**: YYYY-MM-DD
**Phase**: Research
**Trigger**: <what prompted this decision>

**Decision**: <what was decided>

**Alternatives Considered**:
- <Alt 1>: <why rejected>
- <Alt 2>: <why rejected>

**Rationale**: <why this choice was made>

**Context**: <links to research docs, plan sections, or external sources>

When appending the first decision entry (replacing the _No decisions recorded yet._ placeholder), also append a row to FEATURE_LOG.md: | <today> | First decision logged | \DECISIONS.md` |`

Commit the research artifact to the projects repo after creating or finalizing the document:

git add research/ FEATURE_LOG.md && git commit -m "chore: Research artifact — <doc name>"

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Anti-Patterns

Do NOT:

• Use native Explore agents or subagents as a substitute for gather_task_context
• Abandon gather_task_context if it takes 1-3 minutes — this is expected behavior
• Fall back to get_architecture_overview or other tools because gather_task_context "seems slow"
• Use generic language like "gather context from the codebase" — always name the specific Driver MCP tool
• Skip the conversational Q&A phase — understanding intent before researching prevents wasted work
• Split documents based on length rather than concept boundaries
• Leave the overview out of date when research is finalized
• Assume you know where the codebase is or what standards apply — if uncertain, ask the user
• Skip standards resolution when the user has identified codebases — always search for CLAUDE.md
• Trust Driver context without local validation — Driver shows committed state, not local uncommitted changes

DO:

• Call gather_task_context with detailed, specific task descriptions
• Wait for the full response — it is doing compressed expert-level codebase analysis
• Use primitive tools (get_code_map, get_file_documentation, get_source_file) for targeted follow-up
• Spawn parallel subagents as concurrency wrappers for multiple gather_task_context calls
• Ask lots of probing questions before and during research
• Keep the overview current as an index of all research
• Search for CLAUDE.md relative to each codebase path before gathering context
• When no standards are found, ask the user rather than proceeding silently
• After gather_task_context returns, validate key files and branch locally before building on the findings

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Before Responding Checklist

Before sending any response during research, verify:

• Questions asked? — Am I being curious enough? What else should I ask?
• Questions in context? — Are questions placed near the content they relate to?
• Why-What-How order? — Am I progressing through layers appropriately?
• Driver context? — Have I called gather_task_context where relevant?
• Overview current? — Does 00-overview.md reflect the latest findings and decisions?
• Feature log? — Did I update FEATURE_LOG.md when creating new research docs?
• Standards resolved? — Have I searched for CLAUDE.md at each codebase path? If not found, did I ask the user?
• Core/shell decomposition surfaced? — Has the "how" research named the pure-core functions/types and shell entry points for this feature, with a note on whether the surrounding code is already in core/shell shape?
• Local state validated? — After gather_task_context, did I check branch, key file existence, and uncommitted changes locally?
• Decision log? — Did I append to DECISIONS.md for significant decisions, rejected alternatives, or context shifts?
• Artifacts committed? — Did I commit new artifacts to the projects repo?
• Cross-feature scan? — Did I check other active features for overlapping file targets?