# List Experiments > Operator-side inventory of experiment PRs for auditing a Senpai research track. It outputs merged winners, a compact results table, and full details for deep dives. - Skill: `wandb/list-experiments` (Agent Skill) - Install (CLI): `npx skillmds@latest add wandb/list-experiments` - Raw SKILL.md: https://api.skillmd.com/api/skills/wandb/list-experiments/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: wandb (https://skillmd.com/u/wandb) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/wandb/list-experiments --- # List Experiments This is a human/operator audit guide, not a live advisor or student skill. Run the script below to fetch all experiment PRs from the advisor branch and organize them into 3 files. The `BASE_BRANCH` should be set to the advisor branch (e.g. `noam`). Check the `$ADVISOR_BRANCH` env var or the PR base branch. ## Code ```python import subprocess, json, os, re from datetime import datetime BASE_BRANCH = os.environ.get("ADVISOR_BRANCH", "noam") def get_repo_info(): r = subprocess.run(["gh", "repo", "view", "--json", "owner,name"], capture_output=True, text=True) if r.returncode: raise RuntimeError(r.stderr) info = json.loads(r.stdout) return info["owner"]["login"], info["name"] def fetch_issue_comments(owner, name, number): """Fetch all PR conversation comments for a PR, across every REST page.""" comments = [] page = 1 while True: endpoint = f"repos/{owner}/{name}/issues/{number}/comments?per_page=100&page={page}" r = subprocess.run(["gh", "api", endpoint], capture_output=True, text=True) if r.returncode: raise RuntimeError(r.stderr) batch = json.loads(r.stdout) comments.extend((comment.get("body") or "") for comment in batch) if len(batch) < 100: return comments page += 1 def fetch(): """Fetch PRs with complete comments via GraphQL plus REST overflow pages.""" owner, name = get_repo_info() prs = [] cursor = None while True: after = f', after: "{cursor}"' if cursor else "" query = f'''{{ repository(owner: "{owner}", name: "{name}") {{ pullRequests(first: 100, baseRefName: "{BASE_BRANCH}", states: [OPEN, CLOSED, MERGED]{after}) {{ pageInfo {{ hasNextPage endCursor }} nodes {{ number title body state mergedAt url labels(first: 10) {{ nodes {{ name }} }} comments(first: 100) {{ pageInfo {{ hasNextPage }} nodes {{ body }} }} }} }} }} }}''' r = subprocess.run(["gh", "api", "graphql", "-f", f"query={query}"], capture_output=True, text=True) if r.returncode: raise RuntimeError(r.stderr) data = json.loads(r.stdout)["data"]["repository"]["pullRequests"] for node in data["nodes"]: first_comments = [c["body"] or "" for c in node["comments"]["nodes"]] comments = ( fetch_issue_comments(owner, name, node["number"]) if node["comments"]["pageInfo"]["hasNextPage"] else first_comments ) prs.append({ "number": node["number"], "title": node["title"], "body": node["body"] or "", "state": node["state"], "mergedAt": node["mergedAt"], "url": node["url"], "labels": [{"name": l["name"]} for l in node["labels"]["nodes"]], "comments": comments, }) if not data["pageInfo"]["hasNextPage"]: break cursor = data["pageInfo"]["endCursor"] return prs def extract_section(text, header): """Extract a markdown section (# or ##) by header name.""" match = re.search(rf'(^#{{1,2}} {header}.*?)(?=^#{{1,2}} |\Z)', text, re.MULTILINE | re.DOTALL) return match.group(1).strip() if match else None def find_results_text(pr): """Search body then comments (newest first) for the Results section.""" body = pr.get("body", "") or "" section = extract_section(body, "Results") if section and "_To be filled" not in section: return section for comment in reversed(pr.get("comments", [])): section = extract_section(comment, "Results") if section and "_To be filled" not in section: return section return None def extract_val_loss(text): if not text: return None patterns = [ r'val[/_]loss[:\s]*\*?\*?(\d+\.\d+)', r'\*\*val/loss\*\*[:\s|]*\*?\*?(\d+\.\d+)', r'val/loss[:\s|]+(\d+\.\d+)', ] for p in patterns: m = re.search(p, text, re.IGNORECASE) if m: return float(m.group(1)) return None def has_results(pr): return find_results_text(pr) is not None def labels_str(pr): return ", ".join(l["name"] for l in pr.get("labels", [])) def hypothesis_oneline(body): """Extract first sentence of hypothesis.""" sec = extract_section(body or "", "Hypothesis") if not sec: return "" text = re.sub(r'^#{1,2} Hypothesis\n', '', sec).strip() m = re.match(r'(.+?[.!])\s', text) return m.group(1) if m else text[:120] # --- Fetch --- prs = fetch() merged = sorted([p for p in prs if p.get("mergedAt")], key=lambda p: p["mergedAt"]) with_results = [p for p in prs if has_results(p) and not p.get("mergedAt")] no_results = [p for p in prs if not has_results(p)] ts = datetime.now().strftime("%Y-%m-%d_%H-%M") outdir = "experiment_log" os.makedirs(outdir, exist_ok=True) # --- File 1: Merged winners (chronological) --- merged_path = f"{outdir}/merged_{ts}.md" with open(merged_path, "w") as f: f.write(f"# Merged Experiments ({len(merged)} winners)\n\n") f.write("These PRs beat baseline and were merged, in chronological order.\n\n") for pr in merged: body = pr.get("body", "") or "" hyp = extract_section(body, "Hypothesis") or "" results = find_results_text(pr) or "" val = extract_val_loss(results) val_str = f" | val_loss: {val}" if val else "" f.write(f"## #{pr['number']}: {pr['title']}{val_str}\n\n") if hyp: f.write(f"{hyp}\n\n") if results: f.write(f"{results}\n\n") f.write("---\n\n") # --- File 2: Compact results table --- table_path = f"{outdir}/results_table_{ts}.md" with open(table_path, "w") as f: f.write(f"# Experiment Results Table\n\n") f.write(f"Total: {len(prs)} PRs | Merged: {len(merged)} | ") f.write(f"Ran (not merged): {len(with_results)} | Never ran: {len(no_results)}\n\n") f.write("## Merged\n\n") f.write("| PR | Title | val_loss |\n|---|---|---|\n") for pr in merged: results = find_results_text(pr) val = extract_val_loss(results) val_str = f"{val:.4f}" if val else "—" f.write(f"| #{pr['number']} | {pr['title']} | {val_str} |\n") f.write(f"\n## Ran but not merged ({len(with_results)})\n\n") f.write("| PR | Title | val_loss | Hypothesis (short) |\n|---|---|---|---|\n") for pr in sorted(with_results, key=lambda p: p["number"], reverse=True): body = pr.get("body", "") or "" results = find_results_text(pr) val = extract_val_loss(results) val_str = f"{val:.4f}" if val else "—" hyp = hypothesis_oneline(body) f.write(f"| #{pr['number']} | {pr['title']} | {val_str} | {hyp[:80]} |\n") f.write(f"\n## Never ran ({len(no_results)})\n\n") f.write("| PR | Title | Hypothesis (short) |\n|---|---|---|\n") for pr in sorted(no_results, key=lambda p: p["number"], reverse=True): hyp = hypothesis_oneline(pr.get("body", "")) f.write(f"| #{pr['number']} | {pr['title']} | {hyp[:80]} |\n") # --- File 3: Full details (only PRs that ran) --- full_path = f"{outdir}/full_details_{ts}.md" with open(full_path, "w") as f: f.write(f"# Full Experiment Details ({len(merged) + len(with_results)} experiments)\n\n") for pr in merged + sorted(with_results, key=lambda p: p["number"], reverse=True): body = pr.get("body", "").strip() or "_No description._" state = "MERGED" if pr.get("mergedAt") else "CLOSED" f.write(f"# #{pr['number']}: {pr['title']} [{state}]\n\n{body}\n\n") results = find_results_text(pr) if results and results not in body: f.write(f"{results}\n\n") f.write("---\n\n") print(f"Saved {len(prs)} PRs ({len(merged)} merged, {len(with_results)} ran, {len(no_results)} never ran):") print(f" {merged_path} — merged winners with hypothesis + results") print(f" {table_path} — compact table of all experiments") print(f" {full_path} — full PR bodies (only experiments that ran)") ```