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.
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Handle cascading data retrieval tool failures by falling back to embedded knowledge generation
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
When multiple data retrieval tools (read_webpage, search_web) fail in succession, immediately fall back to generating content from embedded knowledge rather than attempting repairs or abandoning the task. This preserves task context and ensures completion.
Use this pattern when you observe:
Identify the failure pattern:
Iteration N: read_webpage failed - connection error
Iteration N+1: search_web failed - timeout/unavailable
Iteration N+2: [risk] Agent may abandon task or switch objectives
Before switching strategies, explicitly restate the original objective:
ORIGINAL OBJECTIVE: [Restate the core task goal]
CONTEXT PRESERVED: [Key requirements, constraints, deliverables]
Immediately switch to embedded knowledge generation:
Generate the required document:
# Fallback to generating from embedded knowledge
write_file(
path="output/document.md",
content="[Generate content from internal knowledge base]"
)
Scenario: PACT Act veterans benefits document needed, but web access failing
Wrong approach (observed failure):
Iter 11: read_webpage failed - access error
Iter 12: search_web failed - unavailable
Iter 13: [ABANDONED] Switched to unrelated musician payroll task
Correct approach (with this skill):
Iter 11: read_webpage failed - access error
Iter 12: search_web failed - unavailable
Iter 13: CASCADE FAIL DETECTED - invoking fallback
Iter 14: write_file - generate PACT Act document from embedded knowledge
- Note: "External verification unavailable; content based on training knowledge"
write-file-fallback: Generate documents when data sources unavailabletask-context-preservation: Maintain objective continuity through errors아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.
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Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.
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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.
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Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Recover from repeated sandbox execution failures by saving Python code to a script and running it through run_shell. Check output, error messages, and exit codes to confirm results or diagnose remaining execution problems.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.