search

name: search
description: Find relevant items under uncertainty across repositories, databases, web sources, or any searchable corpus. Use when exploring unknown territory, finding related information, or discovering relevant resources.
argument-hint: "[query] [sources: repo|web|docs] [constraints]"
disable-model-invocation: false
user-invocable: true
allowed-tools: Read, Grep, Web
context: fork
agent: explore

Live Context

Available search context:

• Current directory: !pwd
• File types available: !find . -type f -not -path './.git/*' 2>/dev/null | sed 's/.*\.//' | sort | uniq -c | sort -rn | head -10
• Total searchable files: !find . -type f -not -path './.git/*' 2>/dev/null | wc -l | tr -d ' '
• Common directories: !find . -type d -maxdepth 2 -not -path './.git/*' 2>/dev/null | head -10
• README files: !find . -name 'README*' -not -path './.git/*' 2>/dev/null | head -5

Intent

Execute search to find relevant information when you don't know exactly where to look. Unlike retrieve (which fetches known items), search explores under uncertainty and ranks results by relevance.

Success criteria:
- Relevant results are found and ranked
- Search strategy is appropriate for the domain
- Coverage is sufficient for the use case
- Results include relevance scores and evidence
- False positives are minimized

World Modeling Context:
Search feeds into the State Layer by discovering entities and observations to include in world-state. It supports the Identity & Continuity Layer by finding candidate matches for identity-resolution.

Inputs

Procedure

1) Parse query: Understand what the user is looking for
- Extract key terms and concepts
- Identify entity types being sought
- Determine search intent (existence, discovery, comparison)
- Expand with synonyms or related terms if appropriate

2) Select search strategy: Based on sources and query type
- Code/repo search: Use Grep patterns, file globs
- Web search: Use web search tools with keywords
- Documentation: Search docs directories, README files
- Semantic: Expand query with related concepts

3) Construct search queries: Formulate effective queries
- For Grep: Build regex patterns that balance precision/recall
- For web: Use keywords, exclude noise terms
- Apply filters (file type, date range, domain)

4) Execute searches: Run queries across sources
- Use multiple query variations if first returns poor results
- Apply pagination for comprehensive coverage
- Track which queries were tried

5) Collect and deduplicate results: Aggregate findings
- Merge results from multiple sources
- Remove duplicates (same content, different paths)
- Note source and search method for each result

6) Rank results: Score by relevance and other criteria
- Term match: How well result matches query
- Authority: Source credibility, recency
- Context: Relevance to broader task
- Uniqueness: Information not found elsewhere

7) Extract key information: For top results
- Relevant snippets that match query
- Metadata (file, date, author)
- Context around the match

8) Assess coverage: Evaluate search completeness
- What portion of relevant sources were searched?
- Any known blind spots?
- Suggest additional searches if coverage is poor

Output Contract

Return a structured object:

search_results:
  query: string  # Original query
  query_expansions: array[string]  # Variations tried
  sources_searched: array[string]  # Where we looked
  results:
    - id: string  # Result identifier
      type: file | web | doc | record
      source: string  # Where found
      path: string | null  # File path or URL
      title: string | null
      snippet: string  # Relevant excerpt
      relevance_score: number  # 0.0-1.0
      metadata:
        date: string | null
        author: string | null
        size: number | null
      evidence_anchor: string  # Reference for grounding
  total_found: integer  # Total results before filtering
  returned: integer  # Results in this response
coverage:
  sources_available: array[string]
  sources_searched: array[string]
  coverage_ratio: number  # 0.0-1.0
  blind_spots: array[string]  # Known gaps
search_strategy:
  method: string  # How search was conducted
  queries_executed: array[string]  # Actual queries run
  filters_applied: object
confidence: number  # 0.0-1.0
evidence_anchors: array[string]
assumptions: array[string]
next_searches: array[string]  # Suggested follow-up searches

Field Definitions

Examples

Example 1: Code Search for Authentication Logic

Input:

query: "JWT token validation"
sources: ["repo"]
constraints:
  file_types: [".ts", ".js"]
  exclude_paths: ["node_modules", "test"]

Output:

search_results:
  query: "JWT token validation"
  query_expansions:
    - "jwt.*valid"
    - "token.*verify"
    - "jsonwebtoken"
  sources_searched: ["repo:src/"]
  results:
    - id: "r1"
      type: file
      source: "repo"
      path: "src/middleware/auth.ts"
      title: null
      snippet: |
        export function validateToken(token: string): JWTPayload {
          return jwt.verify(token, process.env.JWT_SECRET) as JWTPayload;
        }
      relevance_score: 0.95
      metadata:
        date: "2025-01-20"
        author: null
        size: 2450
      evidence_anchor: "src/middleware/auth.ts:45-48"
    - id: "r2"
      type: file
      source: "repo"
      path: "src/services/user-service.ts"
      title: null
      snippet: |
        const decoded = validateToken(authHeader.split(' ')[1]);
      relevance_score: 0.75
      metadata:
        date: "2025-01-15"
        size: 5200
      evidence_anchor: "src/services/user-service.ts:23"
    - id: "r3"
      type: file
      source: "repo"
      path: "src/utils/jwt-helpers.ts"
      title: null
      snippet: |
        export const JWT_ALGORITHM = 'HS256';
        export const TOKEN_EXPIRY = '24h';
      relevance_score: 0.60
      metadata:
        date: "2024-12-01"
        size: 800
      evidence_anchor: "src/utils/jwt-helpers.ts:1-5"
  total_found: 8
  returned: 3
coverage:
  sources_available: ["repo", "docs"]
  sources_searched: ["repo:src/"]
  coverage_ratio: 0.85
  blind_spots:
    - "Config files not searched"
    - "Documentation not searched"
search_strategy:
  method: "grep_pattern_search"
  queries_executed:
    - "grep -r 'jwt.*valid' --include='*.ts' src/"
    - "grep -r 'token.*verify' --include='*.ts' src/"
  filters_applied:
    file_types: [".ts", ".js"]
    excluded: ["node_modules", "test"]
confidence: 0.9
evidence_anchors:
  - "src/middleware/auth.ts:45-48"
  - "src/services/user-service.ts:23"
  - "src/utils/jwt-helpers.ts:1-5"
assumptions:
  - "All JWT logic is in TypeScript files"
  - "No JWT validation in excluded test files matters"
next_searches:
  - "Search config/ for JWT_SECRET references"
  - "Search docs/ for authentication documentation"

Evidence pattern: Used Grep with multiple patterns to find JWT-related code, ranked by directness of validation logic.

Example 2: Web Search for API Documentation

Input:

query: "OpenAI API rate limits 2025"
sources: ["web"]
ranking_criteria: recency

Output:

search_results:
  query: "OpenAI API rate limits 2025"
  query_expansions:
    - "OpenAI rate limit tiers"
    - "GPT-4 API quota"
  sources_searched: ["web"]
  results:
    - id: "w1"
      type: web
      source: "web"
      path: "https://platform.openai.com/docs/guides/rate-limits"
      title: "Rate limits - OpenAI API"
      snippet: "Rate limits are measured in tokens per minute (TPM) and requests per minute (RPM). Tier 1 users: 500 RPM, 100,000 TPM..."
      relevance_score: 0.95
      metadata:
        date: "2025-01-15"
        author: "OpenAI"
      evidence_anchor: "url:https://platform.openai.com/docs/guides/rate-limits"
    - id: "w2"
      type: web
      source: "web"
      path: "https://community.openai.com/t/rate-limit-changes-2025"
      title: "Rate Limit Changes in 2025"
      snippet: "As of January 2025, we've updated rate limit tiers..."
      relevance_score: 0.85
      metadata:
        date: "2025-01-10"
      evidence_anchor: "url:https://community.openai.com/t/rate-limit-changes-2025"
  total_found: 15
  returned: 2
coverage:
  sources_available: ["web", "docs"]
  sources_searched: ["web"]
  coverage_ratio: 0.7
  blind_spots:
    - "Internal documentation not accessible"
search_strategy:
  method: "web_search"
  queries_executed:
    - "OpenAI API rate limits 2025"
    - "OpenAI rate limit tiers"
  filters_applied:
    recency: "2025"
confidence: 0.85
evidence_anchors:
  - "url:https://platform.openai.com/docs/guides/rate-limits"
assumptions:
  - "Official documentation is current"
next_searches:
  - "Search OpenAI changelog for rate limit updates"

Verification

• [ ] All returned results have valid evidence anchors
• [ ] Relevance scores are between 0.0 and 1.0
• [ ] Coverage assessment identifies blind spots
• [ ] Search strategy is documented
• [ ] Suggested next searches address coverage gaps

Verification tools: Link validators, file existence checks

Safety Constraints

• mutation: false
• requires_checkpoint: false
• requires_approval: false
• risk: low

Capability-specific rules:
- Do not search paths marked as sensitive
- Rate-limit web searches appropriately
- Flag when search coverage is < 50%
- Distinguish authoritative from user-generated sources

Composition Patterns

Commonly follows:
- None typically - search is often a starting point
- May follow inspect when inspection reveals knowledge gaps

Commonly precedes:
- retrieve - After finding, retrieve full content
- world-state - Search results inform state construction
- identity-resolution - Find candidate entities to resolve
- grounding - Search for evidence to ground claims

Anti-patterns:
- Avoid searching when exact path is known (use retrieve)
- Never search without considering coverage limitations

Workflow references:
- See composition_patterns.md#world-model-build for search in model construction
- See composition_patterns.md#debug-code-change for search during debugging