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---
# Shared tool-calling and sandbox environment hints.
# gh-aw imports this file; the markdown below (after the closing ---) is
# appended to the agent's task prompt at runtime via {{#runtime-import}}.
# Update INSTRUCTIONS in pydantic_ai_gh_aw_shim/cli.py to match.
---
## Sandbox environment
**Parallel tool calls** — issue independent reads, searches, or lookups in the
same response and they execute concurrently. Only chain sequentially when one
call genuinely needs a previous call's result.
**File reading** — read files in large ranges (500+ lines per call). Most Python
source files fit in one or two calls. Avoid reading 3080 lines at a time.
**Search tools** — use the native `Grep` and `Glob` tools for codebase search.
`rg` and `uv` are also available as plain commands via `Bash`.
**Dev environment** — the repo is checked out at `$GITHUB_WORKSPACE`. Dev
dependencies are **not** pre-installed; run `make install` once before using
`pytest`, `ruff`, or `pyright`. Prefer `uv run pytest <test_file>` over a bare
`pytest` call.
**GitHub issue search** — this workflow runs the GitHub toolset in `gh-proxy`
mode, so there are **no `mcp__github__*` tools**, and the `/search/issues`
endpoint (`gh issue list --search`, `gh search issues`) returns HTTP 403 via the
AWF firewall proxy. The issue-**list** endpoint **is** allowed through the
proxied `gh` CLI, including its server-side `?labels=` filter. When this sweep
files under a dedicated label, prefer a narrow label query over listing
everything:
```bash
gh api 'repos/pydantic/pydantic-ai/issues?state=open&labels=<label>&per_page=100' \
--jq '.[] | select(.pull_request == null) | {number, title}'
```
If this sweep has no dedicated label, or the label filter is inconclusive, widen
to a full open-issue scan:
```bash
gh api --paginate 'repos/pydantic/pydantic-ai/issues?state=open&per_page=100' \
--jq '.[] | select(.pull_request == null) | {number, title, labels: [.labels[].name]}'
```
`select(.pull_request == null)` drops PRs, which the issues endpoint also
returns.