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58 lines
3.4 KiB
Markdown
58 lines
3.4 KiB
Markdown
# codeflash-agent — how this repo works
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## Packages (UV workspace)
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- `packages/codeflash-core/` — shared foundation: models, AI client, telemetry, git helpers
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- `packages/codeflash-python/` — Python language CLI (`codeflash` command), extends core
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- `packages/codeflash-mcp/` — MCP server (stub)
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- `packages/codeflash-lsp/` — LSP server (stub)
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## Services
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- `services/github-app/` — GitHub App integration service
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## Plugin
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- `plugin/` — Claude Code plugin (self-contained, multi-language). See [plugin/README.md](plugin/README.md) for architecture and session flow.
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## Evals
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Two types of evals, both run through `run-eval.sh`:
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**v1 (templates)** — Small synthetic projects in `evals/templates/`. Each bundles source code, tests, and a `pyproject.toml`. The runner copies the template to a temp dir, installs deps with `uv`, and runs Claude. Good for testing specific agent behaviors (ranking accuracy, memory profiling methodology, cross-domain detection). 9 templates across ranking, memory, crossdomain, and layered types.
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**v2 (repos)** — Real repos in `evals/repos/`. Each has a `manifest.json` pointing to a GitHub repo + commit where a known bug exists. The runner shallow-clones the repo (cached locally after first run), drops Claude in, and the agent handles everything — setup, profiling, diagnosis, fix. More realistic but slower and more expensive (~$2/run). The manifest includes a `fix_commit` for reference and a rubric for scoring.
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Each eval produces results in `evals/results/<name>-<timestamp>/`. Score with `score.py`, which uses a mix of deterministic checks (did the agent use a profiler? did tests pass?) and LLM grading against the manifest's rubric.
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**Regression testing** — Go to Actions > "Eval Regression" > Run workflow. Runs a subset of evals, scores them, compares to baselines in `evals/baseline-scores.json`. Fails if any score drops below threshold. Use before merging agent behavior changes.
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```
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./evals/run-eval.sh --list # see all evals (v1 + v2)
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./evals/run-eval.sh ranking --skill-only # run a v1 eval
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./evals/run-eval.sh codeflash-internal-psycopg-serialization --skill-only # run a v2 eval
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./evals/score-eval.sh evals/results/<dir> # score it
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./evals/check-regression.sh # full regression check
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```
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## CI (runs on every PR)
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The `validate` workflow runs Claude with the `plugin-dev` plugin to check:
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- Plugin structure (frontmatter, manifest, cross-references)
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- Agent consistency (all domain agents must have the same experiment loop steps)
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- Eval manifest validity
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- Skill quality
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Warnings are blocking — any issue fails the job. Claude posts a summary comment on the PR.
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## Key conventions
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- Domain agents are self-contained — all methodology is inline, no required file reads before starting
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- Every agent uses the same experiment loop structure (choose target > implement > benchmark > keep/discard > commit only on KEEP)
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- Changes to one domain agent should be mirrored to others where applicable (CI enforces this)
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- The plugin uses `.codeflash/` in the user's project for session state (results.tsv, HANDOFF.md)
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- Language-agnostic methodology lives in `plugin/references/shared/`; language-specific implementations live under `plugin/languages/<lang>/references/`
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## Contributing
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1. Branch off main
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2. Make changes, push — CI validates automatically
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3. If you changed agent behavior, trigger an eval regression run before merging
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