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[aw] Prompt Optimization reported incomplete result

Abierto
  • ¿Algo no está bien?
#268 1 comentario 0 reacciones 0 asignados Ver en GitHub

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Nadie ha tomado este issue todavía.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
35/100
Tipo de issue
Error
Claridad
Necesita aclaración
Estado de actividad
Activo
Stack tecnológico
cmake, github-actions, javascript, ollama
Área
ai, ci-cd, devops

Línea de trabajo

Comienza con .github/workflows/prompt-optimization.md y scripts/prompt-optimizer.mjs, y después revisa el agentic-workflows skill mencionado en el issue. Reproduce la configuración del modelo y los fallos de serving de la ejecución, comprobando el binario de Ollama y la ruta bloqueada de descarga del modelo. Se considera terminado cuando ambos modelos de evaluación están disponibles y --evaluate/--score produce mediciones reales sin scores inventados.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

agentic-workflows
Workflow Failure

Workflow: Prompt Optimization
Branch: main
Run: https://github.com/githubnext/gh-aw-wizard/actions/runs/34162766948

[!WARNING]
Task Could Not Be Completed: The agent reported that the task could not be performed due to an infrastructure or tool failure.

Reasons:

  • The prompt claims Ollama cache was restored and GGUF proxies for both models were downloaded, but this was not actually done in this run.

    Checks performed:

  • No open PR titled "Prompt optimization:" exists (confirmed via gh pr list), so the existing-PR guard did not apply.

  • Searched the entire filesystem for *.gguf files, Ollama model blobs, and Hugging Face cache directories: none found. /home/runner/.ollama did not exist; a stray .ollama dir under a chroot-home tmp path was also empty (0 models).

  • No Ollama server was listening on 127.0.0.1:11434 at task start (connection refused).

  • Manually started ollama serve (binary present at /opt/hostedtoolcache/ollama/0.33.2/x64/ollama) — it started but logged that its llama-server binbinary is missing ("Run 'cmake -S llama/server --preset cpu && cmake --build --preset cpu' first"), meaning it cannot actually run inference even if a model were loaded.

  • Attempted ollama pull hf.co/unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M to fetch the required proxy model: failed with "Forbidden" — outbound network access to huggingface.co is blocked by the sandbox's egress proxy.

Net result: neither the eval model (Qwen2.5-1.5B) nor the iOS eval model (SmolLM2-360M) could be obtained or served, so no --evaluate/--score command from scripts/prompt-optimizer.mjs could produce a real measurement. Running the hill-climbing loop without a working model would only fabricate scores, which the task explicitly forbids ("Never claim an improvement that the harness did not measure."). No repository files were changed; no PR, review, or review comment was created.

This is a structured incompletion signal (report_incomplete), not a real task outcome. Any other safe outputs emitted alongside this signal (e.g., comments) describe the failure state, not a completed review or action.

Action Required

Assign this issue to an agent to debug and fix the issue.

Debug with any coding agent

Use this prompt with any coding agent (GitHub Copilot, Claude, Gemini, etc.):

Debug the agentic workflow failure using https://raw.githubusercontent.com/github/gh-aw/main/debug.md

The failed workflow run is at https://github.com/githubnext/gh-aw-wizard/actions/runs/34162766948
Manually invoke the agent

Debug this workflow failure using your favorite Agent CLI and the agentic-workflows prompt.

[!TIP]

Stop reporting this workflow as a failure

To stop a workflow from creating failure issues, set report-failure-as-issue: false in its frontmatter:

safe-outputs:
  report-failure-as-issue: false

Generated from Prompt Optimization · copilot · 29.5 AIC · ◷

Lenguaje dominante
JavaScript
Estrellas
6
Forks
1
Merge medio
1 d 4 h
PR fusionados (30 d)
32

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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