[aw] Prompt Optimization reported incomplete result
Maintainers usually reply within 1 day
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- javascript
Research direction
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.
Written by the indexing model from the issue text.
Description
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.
- Start your agent
- Load the
agentic-workflowsskill from.github/skills/agentic-workflows/SKILL.mdor https://github.com/github/gh-aw/blob/main/.github/skills/agentic-workflows/SKILL.md - Type
debug the agentic workflow prompt-optimization failure in https://github.com/githubnext/gh-aw-wizard/actions/runs/37375541447
[!TIP]
Stop reporting this workflow as a failure
To stop a workflow from creating failure issues, set
report-failure-as-issue: falsein its frontmatter:safe-outputs: report-failure-as-issue: false
Generated from Prompt Optimization · copilot · 49.8 AIC · ◷
- Dominant language
- JavaScript
- Stars
- 5
- Forks
- 1
- Avg merge
- 1d 7h
- Merged PRs (30d)
- 30
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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