Incremental audio production with duration mismatch handling, adaptive stem extension, and pre-mix alignment verification
日本語の概要は準備中です。原文の説明を表示しています。
Heuristic for detecting search tool failures and pivoting to domain knowledge for document generation
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Incremental audio production with duration mismatch handling, adaptive stem extension, and pre-mix alignment verification
日本語の概要は準備中です。原文の説明を表示しています。
Incremental audio production with duration alignment handling, per-stem verification, and adaptive extension strategies
日本語の概要は準備中です。原文の説明を表示しています。
Create serverless API proxy endpoints that hide API keys and provide a unified backend for the dashboard frontend. Designed for Vercel deployment.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end audio production workflow with stems, effects, archiving, and verification
日本語の概要は準備中です。原文の説明を表示しています。
Handle cascading data retrieval tool failures by falling back to embedded knowledge generation
日本語の概要は準備中です。原文の説明を表示しています。
Fallback pattern for executing Python code when execute_code_sandbox fails
日本語の概要は準備中です。原文の説明を表示しています。