{"uid":"cap_yXCLJZjaJDNaxII1K7q0T","slug":"blinka-rescue-pro-62c96c93","name":"Blinka Rescue Pro","description":"Bounded stateless machine services plus public procurement discovery. No private Mika memory or arbitrary tool execution.","url":"https://blinka-machine-mouths.netlify.app/rescue-pro?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"goal":{"type":"string","description":"The bounded task the failed run was trying to accomplish."},"run_log":{"type":"string","description":"Transcript, tool results, errors, or state summary from the failed/stalled run."},"artifact":{"type":"string","description":"Optional code, config, diff, or text artifact related to the failure. 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Can you diagnose what went wrong, review the code, and tell me the single smallest next action the next agent should take, given it has access to edit_file and run_tests?","exampleUseCases":[{"title":"Crashed deployment pipeline recovery","prompt":"My agent was trying to deploy a containerized service but it crashed with a TypeError midway through. I have the run log and the deploy helper Python script. Can you find the root cause, check the code for bugs, and tell me the exact next step the recovery agent should take using only edit_file and run_tests?"},{"title":"Stalled data-processing agent handoff","prompt":"My data pipeline agent stalled while trying to transform and load a CSV file — it completed the transform step but never wrote the output. Here's the run log and the loader script as a code artifact. What's blocking it and what's the one smallest action the next agent should execute to continue, with constraints that it's read-only on the source directory?"},{"title":"Looping agent retry-trap detection","prompt":"My agent keeps retrying the same failed API call in a loop and never makes progress — the goal was to fetch and summarize external data. Here's the transcript of its last 10 tool calls. Can you identify if it's in a retry trap, what the actual blocker is, and what the next agent should do differently, given it can use request_user_input and make_http_request?"}],"resultDescription":"Returns a JSON object containing: an overall recovery status (continue/blocked/done), a list of priority findings and evidence-backed blockers, a step-by-step repair sequence, the single smallest recommended next action (with tool name and reason), a structured artifact review (issues, suggestions, confidence, executed=false), a continuation handoff contract specifying allowed tools and authority boundaries, and a confidence score for the recovery assessment.","failureModes":["Missing or empty run_log may result in low-confidence recovery with no actionable findings","Artifact too large or malformed may cause artifact review to be skipped or truncated","Incorrect artifact_kind enum causes wrong review path (code vs text analysis)","Tool names in available_tools that don't match exact names may produce unauthorized tool recommendations","Ambiguous or very short goal description reduces precision of blocker identification","Non-Python code artifacts receive weaker static analysis than Python artifacts"],"whenToPreferThis":"Choose this endpoint when an AI agent run has failed, stalled, or looped and you need a structured, evidence-grounded recovery plan rather than a simple retry. 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