Incremental audio production with duration mismatch handling, adaptive stem extension, and pre-mix alignment verification
中文简介正在准备中。当前显示原文说明。
Decide when repeated search failures justify continuing document creation with existing knowledge. Track errors, distinguish retryable problems, and record verification limits, while excluding tasks that require current information or explicit external verification.
中文简介正在准备中。当前显示英语简介。
Heuristic for detecting search tool failures and pivoting to domain knowledge for document generation
安装前可以查看交给 AI 代理的指令内容。
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.
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.
Monitor search_web outcomes during your task:
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
When pivoting:
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
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
This heuristic is particularly useful for:
| Task Type | Why It Applies |
|---|---|
| Legal research | Case law and statutes are well-documented in training |
| Technical documentation | Standards and best practices are established knowledge |
| Historical analysis | Past events and data are in training corpus |
| Regulatory compliance | Major regulations (GDPR, COPPA, HIPAA) are well-known |
Do NOT apply this heuristic when:
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
❌ 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
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Incremental audio production with duration mismatch handling, adaptive stem extension, and pre-mix alignment verification
中文简介正在准备中。当前显示原文说明。
Incremental audio production with duration alignment handling, per-stem verification, and adaptive extension strategies
中文简介正在准备中。当前显示原文说明。
Build serverless endpoints that relay external API requests for a dashboard while keeping API keys on the server. Includes Vercel examples for input checks, caching, cross-origin access, and consistent JSON responses.
中文简介正在准备中。当前显示英语简介。
End-to-end audio production workflow with stems, effects, archiving, and verification
中文简介正在准备中。当前显示原文说明。
Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.
中文简介正在准备中。当前显示英语简介。
Fallback pattern for executing Python code when execute_code_sandbox fails
中文简介正在准备中。当前显示原文说明。