add-analysis

name	add-analysis
description	Scaffold and wire a new ResearchForge analysis end-to-end (executor branch + catalog entry + test + double-review + local commit), following the project constitution in CLAUDE.md. Use when adding a new method/analysis id to the engine, e.g. "/add-analysis rdd" or "add a regression-discontinuity analysis".

Add a ResearchForge analysis

Codifies the standard "加一个分析" flow from CLAUDE.md. One analysis = ① an executor branch, ② a catalog entry, ③ a test. Follow the steps in order; don't skip the review or full-suite gate.

0. Decide the id + family

Pick a snake_case id (e.g. rdd). Note its family (statistics/causal/sem/meta/ml/spatial/efficiency/configurational/...) and whether it does real statistical inference (→ needs review) or is deterministic (reuses already-reviewed helpers → verify by test).

1. Executor branch — researchforge/executor/run.py

Add elif entry.id == "<id>": in the dispatch chain. Copy the conventions from a sibling branch; do NOT reinvent:

• cfg = config or {} is already in scope. Read user overrides via cfg.get("<key>"); fall back to an auto default that still runs standalone. Document new keys in docs/loop-decisions.md.
• Outcome/predictors: regression-family convention = first continuous = outcome; override via cfg["outcome"]/cfg["predictors"].
• R backend (if used): go through researchforge/executor/rbridge.py — rbridge.r_available() + rbridge.r_package_available("<pkg>"), else degrade honestly (point to a pure-Python alternative). Guard column names before R formula interpolation: re.fullmatch(r"[A-Za-z.][A-Za-z0-9._]*", c). Pass arbitrary names as d[["name"]] vectors when possible (injection-safe). Write the temp CSV into the output dir d; delete it in finally. Never fetch/run R code from the network at runtime.
• Artifacts: CSV + PNG (matplotlib Agg; English figure labels — default fonts have no CJK). Fill the estimates dict. Write a Chinese summary with ⚠ bias/assumption disclosure. Wrap plotting in try/except so a missing lib doesn't abort.
• profiler gotchas: integer all-distinct columns profile as id (not count/continuous); named-detection should accept id. Duration columns may be the time_col — don't exclude it for survival-type methods. Binary 0/1 columns are often treatment_candidates — don't blanket-exclude them if the method needs them as factors.
• Degrade honestly (clear Chinese message) when preconditions/packages/columns are missing.

2. Catalog entry — catalog/entries/<family>.yaml

Add an entry: id, method, domain, family, goal, description, preconditions, produces, executor_ref, biases. Gate recommendation with the right preconditions (add a Precondition field + a recommender/match.py matcher if no existing field fits — mirror requires_geo/requires_effect_sizes) so it doesn't pollute every dataset's menu. Put real assumptions in biases.

3. Test — tests/test_<id>.py

• A demo dataset under data/demo_<id>.csv (or build it in the test). Use decimal/repeated values so continuous cols aren't profiled as id.
• Assert it runs + key estimates are sane. For R/optional-dep methods, @pytest.mark.skipif(not <available>). Add a no-dependency honest-degrade test (wrong/missing data → clear failure message).

4. Verify → review → commit

1. PYTHONUTF8=1 py -3 -m pytest tests/test_<id>.py -q >/tmp/t.log 2>&1; echo EXIT=$? — check the exit code, don't | tail (it masks pytest's code). Smoke via CLI: py -3 -m researchforge.cli run data/demo_<id>.csv <id>.
2. If the method does real inference: dispatch the inference-reviewer subagent (reviewer ≠ builder) on the branch. Apply must-fix items; adopt disclosure polish; you may override a reviewer finding WITH EVIDENCE (an empirical probe) — surface that to the user. Deterministic methods skip review with a one-line justification.
3. Run the full suite (PYTHONUTF8=1 py -3 -m pytest -q) incl. test_catalog_consistency (every catalog id must have a branch). It is R-heavy (~3–5 min) — run it in the background.
4. Local commit only. Multi-line Chinese messages: Write to a temp file then git commit -F <file> (the Bash tool is POSIX sh — do NOT use PowerShell @'...'@). Never push until the user says "今天 ok"; remind them via git log origin/main..HEAD if there are unpushed commits.

Key files

executor/run.py (dispatch + branches + helpers) · executor/rbridge.py (R bridge) · catalog/entries/*.yaml · catalog/schema.py (Precondition/AnalysisEntry) · recommender/match.py (preconditions) · recommender/scoring.py (method score card) · profiler/ · web/. Read CLAUDE.md first.