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[aw] Prompt Optimization reported incomplete result

Aperta
  • Qualcosa non va?
#306 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
35/100
Tipo di issue
Bug
Chiarezza
Da chiarire
Stato di attività
Attiva
Stack tecnologico
github-actions, javascript, ollama
Ambito
ai, ci-cd, devops

Direzione di ricerca

Start with .github/workflows/prompt-optimization.md and the failed run details, then read .github/skills/agentic-workflows/SKILL.md or the linked debugging guide. Inspect how the workflow provides Ollama, its model cache, GGUF proxies, and prompt-optimizer.mjs; done means the evaluation and scoring steps can run successfully in the workflow.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

agentic-workflows
Workflow Failure

Workflow: Prompt Optimization
Branch: main
Run: https://github.com/githubnext/gh-aw-wizard/actions/runs/36779212881

[!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 claimed prerequisite infra (restored Ollama cache, downloaded GGUF proxies, running Ollama server at 127.0.0.1:11434) did not actually exist; I could not run the hill-climbing optimization loop.

    No open PR titled "Prompt optimization:" existed, so the guard step was clear to proceed.

Attempting the described setup: ollama was not on PATH (only present at /opt/hostedtoolcache/ollama/0.33.2/x64/ollama, not installed to PATH). No ~/.ollama cache existed (no models, no blobs). No .gguf files were present anywhere on disk. Port 11434 had nothing listening (connection refused).

I started ollama serve manually to probe further:

  • It reported "total blobs: 0" — the model cache was empty, not restored.
  • GPU/inference discovery failed: "llama-server binary not found" (checked all standard paths) — the ollama installation is missing its inference backend (llama-server), so even with models present, standard chat completions via /v1/... would not work.
  • Attempting ollama pull hf.co/unsloth/SmolLM2-360M-Instruct-GGUF:Q4_K_M failed: "Get (hf.co/redacted) Forbidden" — outbound access to Hugging Face is blocked/forbidden in this sandbox, so I could not download the GGUF proxies myself as a workaround.

Given eval/scoring requires --eval-url (127.0.0.1/redacted) with both the Qwen2.5-1.5B and SmolLM2-360M GGUF proxies actually loadable and inferable, none of the prompt-optimizer.mjs --evaluate/--scoresteps could run. I made no repository content changes (only harness-owned metadata reformatting appeared in.github/skills/optimize-scenario-prompt/SKILL.md`, which I did not author and left untouched), and I did not create a pull request or review comment.

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/36779212881
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 · 32.9 AIC · ◷

Lingua principale
JavaScript
Stelle
6
Fork
1
Merge medio
1g 4h
PR unite (30g)
32

Preparare l'ambiente

Questo progetto non fornisce container di sviluppo, Dockerfile né guida per i contributori, quindi l'ambiente è a tuo carico: parti dal suo README e consulta la nostra guida al primo contributo per i passaggi generali.

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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