LLM-focused workflow for working in this repo: compile Zig, run the orchestrated test runner, consume test-report.json/html artifacts, and discover/debug ConfigFlags.
cd ~/.claude/skills
git clone https://github.com/atopile/atopile.git atopile mkdir -p ~/.claude/skills/dev
curl -fsSL https://raw.githubusercontent.com/atopile/atopile/HEAD/.claude/skills/dev/SKILL.md \
-o ~/.claude/skills/dev/SKILL.md This skill is written for LLMs working inside this repo. It focuses on the fastest, most reliable inner loop:
ato dev test --llm, not raw test output)artifacts/test-report.json, artifacts/test-report.html, artifacts/test-report.llm.json)ConfigFlags correctly (and inventory them repo-wide)source .venv/bin/activate
ato dev compile
ato dev test --llm -k solver
ato dev test --llm --view HEAD --open
ato dev test --reuse --baseline HEAD~1
ato dev flags
src/atopile/cli/dev.py
ato dev compile (triggers Zig build via import faebryk.core.zig)ato dev test --llm (runs test/runner/main.py with args; supports baseline/CI report helpers)src/faebryk/core/zig/__init__.py (ZIG_NORECOMPILE, ZIG_RELEASEMODE)src/faebryk/libs/util.py (ConfigFlag, ConfigFlagInt, …)test/runner/main.py (artifacts/test-report.json, artifacts/test-report.html, artifacts/test-report.llm.json).github/workflows/pytest.yml (test-report.json, test-report.html)dev commands are the primary interface for GitHub Actions workflows.ato dev compile after modifying Zig code.ato dev compile: compile native extensions (graph/typegraph/sexp bindings).ato dev test --llm: runs the orchestrated test runner (defaults to -p test -p src); supports:
-k filter (-- -k ... also works via passthrough args)--baseline comparisons (commit hash or HEAD~N style)--view / --open to fetch and open the test-report.html artifact from GitHub Actions (requires gh CLI)--ci to apply the CI marker expression (not not_in_ci and not regression and not slow)--direct -k <testname> to run a single test via test/runtest.py (tight single-test loops)Local test runs write:
artifacts/test-report.json (single source of truth; outcomes/durations/memory/baseline compare status + stdout/stderr/logs/tracebacks; see tests[].output_full)artifacts/test-report.html (human dashboard; derived from JSON; controlled by FBRK_TEST_GENERATE_HTML=1)artifacts/test-report.llm.json (LLM-friendly; derived from JSON; ANSI stripped logs)CI uploads both artifacts (see .github/workflows/pytest.yml):
test-report.jsontest-report.htmlNotes for LLM debugging:
artifacts/test-report.json or artifacts/test-report.llm.json over raw output; they include structured failures, logs, baseline compare, and collection errors.Remote/baseline behavior:
ato dev test --llm --baseline <commit> uses the CI test-report.json artifact as the baseline (requires gh CLI).ato dev test --llm --view <commit> --open currently fetches/opens only the HTML artifact; for JSON, download the test-report.json artifact via gh run download.ato dev test --reuse --baseline <commit> rebuilds JSON/HTML/LLM against a baseline without rerunning tests.ato dev test --keep-open keeps the live report server running after tests finish.Useful test-runner environment variables (see test/runner/main.py):
FBRK_TEST_REPORT_INTERVAL (seconds; report refresh cadence)FBRK_TEST_LONG_THRESHOLD (seconds; “long test” threshold)FBRK_TEST_WORKERS (0 = cpu count, negative scales workers)FBRK_TEST_GENERATE_HTML (1/0)FBRK_TEST_PERIODIC_HTML (1/0)FBRK_TEST_OUTPUT_MAX_BYTES (truncate preview output used by HTML; tests[].output_full remains complete)FBRK_TEST_OUTPUT_TRUNCATE_MODE (head or tail)FBRK_TEST_BIND_HOST (orchestrator bind host; default 0.0.0.0)FBRK_TEST_REPORT_HOST (host used in printed report URL; default bind host)FBRK_TEST_PERF_THRESHOLD_PERCENT (default 0.30)FBRK_TEST_PERF_MIN_TIME_DIFF_S (default 1.0)FBRK_TEST_PERF_MIN_MEMORY_DIFF_MB (default 50.0)LLM quick usage:
artifacts/test-report.llm.json is always generated (ANSI stripped, full tests + logs).ato dev test --llm prints a concise summary + schema + jq hints (stdout only).llm.jq_recipes.ato dev test enables the summary automatically when running under claude-code/codex-cli/cursor.FBRK_TEST_LLM=1 or FBRK_TEST_LLM=0.ConfigFlag is the repo’s “toggle-by-env-var” mechanism. The environment variable name is the first argument to ConfigFlag(...).
Usage:
export SOME_FLAG=1
Inventory all ConfigFlags in-tree (preferred over trying to maintain a manual list):
ato dev flags
Prefer using ato dev flags when you want the full picture (types/defaults/descriptions + callsite counts) in one place.
High-leverage flags you’ll use often:
ZIG_NORECOMPILE, ZIG_RELEASEMODESLOG, SVERBOSE_TABLE, SPRINT_START, SMAX_ITERATIONS, SSHOW_SS_ISCOLOR_LOGS, LOG_TIME, LOG_FILEINFOsrc/faebryk/core/zig/src/ -> Run ato dev compile.ato dev profile <command> to generate a flamegraph or stats.ato dev test --llm.test/ that exercise the command surface.ConfigFlag instead of commenting out code.Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`; user asks for the Claude API, Anthropic SDK, or Managed Agents; user adds/modifies/tunes a C
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack.