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[aw] Prompt Optimization reported incomplete result

Aperta
  • Qualcosa non va?
#268 1 commento 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
cmake, github-actions, javascript, ollama
Ambito
ai, ci-cd, devops

Direzione di ricerca

Inizia da .github/workflows/prompt-optimization.md e scripts/prompt-optimizer.mjs, poi esamina l’agentic-workflows skill citato nell’issue. Riproduci la configurazione del modello e i problemi di serving dell’esecuzione, verificando il binario Ollama e il percorso bloccato per il download del modello. Il lavoro è completato quando entrambi i modelli di valutazione sono disponibili e --evaluate/--score produce misurazioni reali senza scores inventati.

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/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 · ◷

Lingua principale
JavaScript
Stelle
6
Fork
1
Merge medio
1g 5h
PR unite (30g)
30

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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