version: "1.0.2" name: search-fail-pivot description: Heuristic for detecting search tool failures and pivoting to domain knowledge for document generation

Search Tool Failure Detection and Pivot Strategy

Purpose

When using search_web or similar external data retrieval tools, repeated failures waste iterations. This skill provides a diagnostic heuristic to recognize when to abandon search attempts and proceed with existing domain knowledge.

Core Heuristic

If `search_web` fails 2+ consecutive times with 'unknown error' or similar non-recoverable errors, pivot to domain knowledge + `run_shell` for document generation.

Do not attempt more than 2 retries on the same or similar queries before pivoting.

Step-by-Step Instructions

Step 1: Track Search Failures

Monitor search_web outcomes during your task:

• Count consecutive failures (empty results, 'unknown error', timeout, access denied)
• Note the error type - distinguish between:
• Recoverable: Rate limiting, temporary timeout (retry 1-2 times)
• Non-recoverable: 'unknown error', persistent empty results, access denied

Step 2: Apply the 2-Failure Rule

IF search_web failures >= 2 (same or similar queries)
THEN:
  1. Stop attempting search_web
  2. Document what information you attempted to retrieve
  3. Proceed with existing domain knowledge

Step 3: Pivot to Domain Knowledge

When pivoting:

1. Acknowledge the limitation: Note that external verification was attempted but unavailable
2. Use established knowledge: Draw on training data for well-known facts, laws, cases, standards
3. Generate with run_shell: Create documents using Python/bash scripts rather than waiting for external data

Example pivot workflow:

# Instead of continuing search_web attempts:
# 1. Compile known information from domain knowledge
known_facts = {
    "law": "COPPA requirements for children's data",
    "precedent": "FTC v. Google/YouTube settlement patterns",
    "jurisdiction": "California privacy law framework"
}
# 2. Generate document directly
# Use run_shell with Python to create PDF/DOC

Step 4: Document Generation Pattern

When generating documents after pivoting:

# Use run_shell to execute document generation
# Example: Create legal memo with Python + reportlab
python << 'EOF'
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
 
# Generate document with domain knowledge
c = canvas.Canvas("memo.pdf", pagesize=letter)
c.drawString(100, 750, "Legal Analysis based on established precedents")
# ... continue with known information
c.save()
EOF

When This Applies

This heuristic is particularly useful for:

When NOT to Pivot

Do NOT apply this heuristic when:

• You need current/time-sensitive data (stock prices, weather, news < 24hrs)
• The task explicitly requires external verification
• You have not yet attempted the search (first attempt should complete)
• Errors are clearly recoverable (rate limit with retry-after header)

Example Decision Flow

Attempt search_web → Success? → Yes → Use results
                         ↓
                        No
                         ↓
            Is this failure #1? → Yes → Retry once with modified query
                         ↓
                        No (failure #2+)
                         ↓
            Pivot to domain knowledge + run_shell document generation

Benefits

• Saves iterations: Avoids wasting 3-4 attempts on unavailable data
• Maintains progress: Task continues instead of stalling on search
• Leverages training: Uses the substantial knowledge already available
• Clear handoff: Explicitly documents the pivot decision for transparency

Anti-Patterns to Avoid

❌ Continuing search_web attempts beyond 2 failures ❌ Not documenting why you pivoted from search ❌ Applying this to tasks requiring real-time data ❌ Abandoning search on first failure without retry

✅ Apply 2-failure rule consistently ✅ Document the pivot decision in your output ✅ Use run_shell efficiently after pivoting ✅ Distinguish recoverable vs non-recoverable errors