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sandbox-feedback-loop

Build a runnable reproduce→fix→verify loop for a bug or feature in a fresh sandbox, and record it as a feedback-loop doc. Use when investigating a reported bug, validating a root cause, or proving a fix works — before and after the change.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bagofwords1/bagofwords스타 463(GitHub 463 + cccskills 0)
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SKILL.md(원문)

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Sandbox Feedback Loop

A feedback loop is a runnable document: anyone (human or agent) can re-execute it in a fresh sandbox and observe the same failure, then the same pass after the fix. Examples of the format live in docs/feedback-loops/ (e.g. save-button.md, fabric-obo-second-admin-tables.md).

Process

  1. Reproduce first. Never fix a bug you haven't watched fail. Write the smallest deterministic reproduction — a pytest test, a Playwright spec, or a static harness — that fails on current code for the reported reason.
  2. Isolate the root cause and cite it as file.py:line references.
  3. Fix, re-run the same loop, and show the observed output flipping.
  4. Write the doc to docs/feedback-loops/<topic>.md (do NOT add new sandbox-feedback-loop-*.md files at the repo root).

Environment setup (fresh sandbox)

The app targets Python 3.13 (3.12+ works; needs 3.12 f-string syntax; the sandbox default python may be 3.11).

cd backend
pip install uv
uv sync --frozen --extra dev
export BOW_DATABASE_URL="sqlite:///db/app.db"   # required by bow-config.dev.yaml
mkdir -p db
  • Tests run on SQLite by default; the autouse run_migrations fixture builds the schema per test (tests/conftest.py). Use --db=postgres for the testcontainers leg, --db=external when Docker is unavailable.
  • Playwright browsers are pre-provisioned at /opt/pw-browsers in cloud sandboxes — export PLAYWRIGHT_BROWSERS_PATH=/opt/pw-browsers, never playwright install.
  • Full running stack when the loop needs the real UI: tools/agent/boot_stack.sh then cd backend && uv run python ../tools/agent/seed_org.py.

Doc template

# Feedback Loop — <symptom, quoted from the report>

One-paragraph statement of the reported behavior and the claim being validated.

## Root cause (validated)
What actually breaks, citing file:line. Distinguish validated facts from hypotheses.

## Loop A — deterministic reproduction (no external services)
Exact commands + the observed FAIL output. Stub external boundaries (LLMs,
OAuth providers, ODBC) — the loop must run in a clean sandbox.

## Loop B — live confirmation (optional, real credentials)
Only when the premise itself needs a real third party. Secrets via env vars
only — never commit them, never echo them into logs or docs.

## The fix
What changed and where. Re-run Loop A output showing the flip to PASS.

## What this proves / regression notes
What the loop demonstrates; any pre-existing unrelated failures you hit
(verify they reproduce with your change stashed before calling them unrelated).

Rules

  • Loop A must be self-contained — seeded data, stubbed boundaries, no live credentials. Loop B is the exception, not the default.
  • Secrets: env vars only. Never in the doc, the repo, or command output.
  • The reproduction test should survive as a regression test — follow backend/tests/AGENTS.md: assert the general invariant, not the one magic scenario that happened to be reported.
  • If the change affects UI/UX, the loop must include before/after evidence — invoke the ui-evidence skill.

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