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。
Handle cascading data retrieval tool failures by falling back to embedded knowledge generation
安装前可以查看交给 AI 代理的指令内容。
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 errors还没有评价。欢迎分享你的使用感受。
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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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。