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[aw] Prompt Optimization reported incomplete result

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  • ¿Algo no está bien?
#317 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
Bastante claro
Estado de actividad
Activo
Stack tecnológico
javascript
Área
ai, ci-cd, tooling

Línea de trabajo

Start with .github/workflows/prompt-optimization.md and the failed run logs, then inspect scripts/prompt-optimizer.mjs and the workflow environment setup for Ollama, model caching, and llama-server availability. Reproduce the failure where possible and verify that both required models can be loaded and evaluated, or that the workflow reports infrastructure failures accurately without claiming completion.

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/37375541447

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

Reasons:

  • Ollama/model infrastructure described as pre-set-up by the workflow was not actually available in this session, so the hill-climbing optimization loop could not run.

    Checked for an existing open 'Prompt optimization:' PR first (gh pr list) — none found, so proceeded to optimization.

Found no running Ollama server on 127.0.0.1:11434 (connection refused) despite the task description stating it had been started. The ollama binary existed at /opt/hostedtoolcache/ollama/0.33.2/x64/ollama, so I started ollama serve manually; it came up and responded to /api/tags with an empty model list (no cached GGUF models present in ~/.ollama/models — blobs/manifests dirs were empty).

Attempted ollama pull hf.co/unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M to populate the cache myself; it failed with Forbidden — outbound HTTPS to hf.co/huggingface.co is blocked at the squid egress proxy (403 ERR_ACCESS_DENIED), confirmed via direct curl to (hf.co/redacted) and (huggingface.co/redacted)

Additionally, the Ollama server log showed llama-server binary not found during GPU/CPU discovery, meaning even a successfully pulled model could not actually be run for inference in this environment without a separate llama-server build step.

Net effect: neither of the two required proxy models (Qwen2.5-1.5B-Instruct-GGUF and SmolLM2-360M-Instruct-GGUF) could be loaded, so scripts/prompt-optimizer.mjs --evaluate/--score against `(127.0.0.1/redacted) cannot produce measured scores. 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/37375541447
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 · 49.8 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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