[aw] Fix Bug from Labeled Issue exceeded max AI credits

### Workflow Failure

**Workflow:** [Fix Bug from Labeled Issue](https://github.com/github/spec-kit/blob/main/.github/workflows/bug-fix.md)
**Branch:** main
**Run:** https://github.com/github/spec-kit/actions/runs/34245344272
**Pull Request:** [#2884](https://github.com/github/spec-kit/pull/2884)

> [!WARNING]
> **AI Credits Budget Exceeded**
>
> The workflow hit the configured `max-ai-credits` guardrail. Used `1K` of `1K` max (over by `8.4`).

Increase the limit

Update `max-ai-credits` in your workflow frontmatter:

```yaml
max-ai-credits: 2000
```

Tips for reducing AI credit usage

- Review the [cost optimization guidance](https://github.github.com/gh-aw/reference/cost-management/).
- Reduce unnecessary model or tool calls in the prompt.
- Trim large inputs or excess context that does not change the outcome.
- Split large tasks across smaller runs when possible.

> [!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 requested pytest command could not start: the execution environment returned “Permission denied and could not request permission from user.”

Command attempted: ./.venv/bin/python -m pytest -q tests/integration/test_bundler_install_flow.py tests/integration/test_bundler_init_install.py tests/integration/test_bundler_local_install.py tests/integration/test_bundler_offline.py

No tests ran, so pass/fail counts are unavailable. No repository files were modified.

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.

> [!WARNING]
> **Engine Failure**: The `copilot` engine terminated unexpectedly.

**Last agent output:**
```
│ src/specify_cli/workflows/_commands.py tests/bundler_helpers.py
│ tests/integration/test_bundler_install_flow.py
│ tests/unit/test_bundler_primitives.py && git --no-pager status --short …
└ 11 lines...
● search_issues (MCP: github) · is:pr is:open author:mnriem · owner: "mnriem", repo: "spec-kit", qu…
└ [{"content":[{"text":"{\"total_count\":0,\"incomplete_results\":false,\"...
Failed to get response from the AI model; retried 5 times (total retry wait time: 94.71 seconds) (Request-ID 7539ecf4-d237-4f2f-8248-9ae909d6335e) Last error: CAPIError: 429 Maximum AI credits exceeded (1008.399375 / 1000).
Changes +276 -43
Duration 16m 25s
Tokens ↑ 7.7m (7.4m cached) • ↓ 43.7k (25.1k reasoning)
```

### 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/github/spec-kit/actions/runs/34245344272
````

Manually invoke the agent

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

- Start your agent
- Load the `agentic-workflows` skill from `.github/skills/agentic-workflows/SKILL.md` or
- Type `debug the agentic workflow bug-fix failure in https://github.com/github/spec-kit/actions/runs/34245344272`

> [!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:
> ```yaml
> safe-outputs:
> report-failure-as-issue: false
> ```
>
>

> Generated from [Fix Bug from Labeled Issue](https://github.com/github/spec-kit/actions/runs/34245344272) · 1K AIC · [◷](https://github.com/search?q=repo%3Agithub%2Fspec-kit+is%3Aissue+%22gh-aw-workflow-id%3A+bug-fix%22&type=issues)
> - [x] expires on Sep 15, 2026, 3:53 PM UTC

Start with .github/workflows/bug-fix.md and the failed run, then inspect src/specify_cli/workflows/_commands.py and tests/bundler_helpers.py. Run the listed integration tests in tests/integration and review tests/unit/test_bundler_primitives.py; done means the workflow no longer fails on its reported credit or execution errors and the tests can run.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.