context7-research

Context7 Auto-Research Skill

Purpose

Provide official, version-specific library documentation as the primary research source across all Flywheel workflows. Context7 MCP tools are available natively to Claude Code and its Task() sub-agents — no external scripts or dependencies required.

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Trigger Conditions

This skill activates automatically when any of the following are detected:

1. Named technologies in the task description or design brief (e.g., "React", "Express", "Redis", "Prisma")
2. Version references (e.g., "v19", "upgrade to 5.x", "migrate from 3 to 4")
3. Framework-specific terms (e.g., "hooks", "middleware", "ORM", "server components")
4. Dependency manifest entries — technologies found in package.json, requirements.txt, go.mod, Cargo.toml, etc. during codebase scan phases

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Technology Detection

Before invoking Context7, identify target technologies:

1. Parse the task description — extract library/framework names and version references
2. Scan dependency manifests — read package.json (or equivalent) for relevant packages
3. Deduplicate — merge task-description technologies with manifest entries
4. Cap at 3 lookups per phase — prioritise by relevance to the task; skip generic/obvious ones (e.g., don't look up "Node.js" itself unless the task is about Node-specific APIs)

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Context7 Workflow

For each detected technology (max 3 per phase):

Step 1: Resolve Library ID

mcp__context7__resolve-library-id(
  libraryName: "<technology name>",
  query: "<task-specific query>"
)

• Select the result with the best match by name, reputation, and snippet count
• If no results found, skip this technology (non-blocking)

Step 2: Query Documentation

mcp__context7__query-docs(
  libraryId: "<resolved ID from step 1>",
  query: "<command-type-specific query — see templates below>"
)

Step 3: Format Findings

Structure the output as:

## Context7 Documentation Findings

### <Library Name> (via Context7)
- **Library ID**: `<resolved ID>`
- **Relevant APIs/Patterns**: <key findings from query>
- **Version Notes**: <any version-specific information>
- **Gotchas**: <documented pitfalls or breaking changes>

Step 4: Inject

• Pattern A (Task path): Include findings in the sub-agent prompt as context
• Pattern B (dispatch path): Prepend findings to the Gemini prompt as OFFICIAL DOCUMENTATION CONTEXT

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Integration Patterns

Pattern A — Task Path (fw-researcher sub-agent)

When a command invokes fw-researcher via Task(), the researcher calls Context7 directly as its first action before WebSearch/WebFetch:

1. Detect technologies from task + codebase scan
2. For each technology (max 3):
   a. resolve-library-id → get Context7 ID
   b. query-docs → get relevant documentation
3. Use Context7 findings to inform what gaps remain
4. Fill gaps via WebSearch/WebFetch (primary sources)
5. Include Context7 section in output

Context7 findings inform what to search for next — if official docs already cover the topic comprehensively, WebSearch can focus on community experiences and edge cases instead of duplicating basic API documentation.

Pattern B — Dispatch Path (Gemini via dispatch.sh)

When a command calls dispatch.sh gemini directly, Claude must enrich the prompt before dispatch (dispatch.sh agents cannot call MCP tools):

1. Detect technologies from task + codebase scan
2. For each technology (max 3):
   a. resolve-library-id → get Context7 ID
   b. query-docs → get relevant documentation
3. Prepend findings to the Gemini prompt:

   OFFICIAL DOCUMENTATION CONTEXT (via Context7):
   <formatted findings>

   ---
   <original Gemini prompt>

This gives Gemini access to official documentation it would otherwise need to search for (and might get wrong or miss).

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Query Templates by Command

Each command type uses a query template tailored to what it needs from documentation:

Command	Query Template
/fw:implement	"Best practices and API reference for implementing with "
/fw:design	"Architecture patterns and best practices for <DESIGN_BRIEF> with "
/fw:migrate	"Migration guide for . Breaking changes, deprecated APIs, and upgrade steps from <SOURCE_VERSION> to <TARGET_VERSION>"
/fw:debug	"Known issues, gotchas, and common errors with related to <ROOT_CAUSE>"
/fw:document	"How works in . API reference, usage patterns, and configuration"
/fw:harden	"Security best practices, known vulnerabilities, and secure configuration for "

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Error Handling

Every failure is non-blocking. This skill is purely additive — workflows must never halt on enrichment failure.

Failure	Behaviour
resolve-library-id returns no results	Skip this technology, continue to next
query-docs returns empty or irrelevant results	Skip this technology, continue to next
Context7 MCP server unavailable	Skip all Context7 enrichment, log a note, continue workflow
Network timeout	Skip, continue — do not retry
All 3 lookups fail	Proceed without Context7 context; workflow continues as before

When Context7 is skipped entirely, add a brief note to the session output:

ℹ️ Context7 enrichment skipped — <reason>. Proceeding with WebSearch/Gemini only.

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Token Budget

• Max 3 lookups per phase — most tasks involve 1-2 core technologies
• Prefer specific queries over broad ones — "React useEffect cleanup patterns" over "React documentation"
• Trim verbose results — extract only the relevant section from Context7 responses, not the full dump
• If Context7 returns >2000 tokens for a single query, summarise to the most relevant patterns and APIs

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Precedence Rules

1. Context7 results take precedence over generic web search results when both cover the same topic
2. WebSearch fills gaps that Context7 doesn't cover (community patterns, recent issues, real-world experiences)
3. Gemini dispatch receives Context7 context as authoritative input, not as suggestions to verify
4. When Context7 and WebSearch conflict, flag the discrepancy and prefer the Context7 (official) version