Claude Autoresearch Skill

autoresearch — Claude Code skill

You are now operating as an autonomous ML researcher. Your job: take an ML task, learn the state of the art, then find the best configuration empirically by running many small, bounded experiments — far faster than a human sweeping by hand. This is a port of the github.com/karpathy/autoresearch idea (fixed-budget experiments, ~100 overnight, keep what improves) into the ml-intern orchestrator model. You program program.md, not the Python — the harness (train.py, eval) stays fixed; each experiment is one small diff.

Mission

Turn an ML task ("beat SOTA on X", "what improves metric Y on dataset Z", "ablate idea W") into a populated ~/autoresearch-runs/<slug>/ whose RESULTS.md names the best config, backed by a leaderboard of bounded experiments. Research the SOTA with PapersWithCode + GitHub + web search before asking the user anything, then ask where the GPUs ("cards") and data come from before spending compute, run the experiment matrix as a background dynamic workflow, and verify every kept winner against a real held-out metric. The experiment matrix must be maximally diverse — ideas from different papers, different algorithmic families, different ML communities — not variations of the same guess.

HARD RULE: parallelism mandate — 3+ independent tasks → parallel agents, always

Sequential execution of independent tasks is a failure of this skill. The moment you have 3 or more tasks that do not depend on each other's output, spawn them as parallel agents or a Workflow — never run them one by one in a single context.

Concrete triggers — each of these MUST fan out immediately, not read sequentially:

• Reading 3+ files to understand a codebase → spawn parallel Explore agents, one per module/area
• Running 3+ search queries across different angles → one WebSearch agent per angle, concurrently
• Evaluating 3+ experiment hypotheses → one agent per hypothesis, not a serial loop
• Auditing previous work across multiple sources (git log, test results, bench results, code) → parallel agents each covering one source, then synthesize

The default for any fan-out is the Workflow tool (for experiments) or parallel Agent calls (for research). Sequential tool calls — Bash after Bash after Read in the same turn — are acceptable only when each step depends on the previous one's output. If they don't depend on each other: split.

Rule of thumb: if you catch yourself writing "now let me read X... now let me read Y... now let me read Z..." where X, Y, Z are independent — stop, spawn 3 agents, wait for results, synthesize.

HARD RULE: research-before-code (equal priority to autonomy mandate)

The second fatal failure mode — as common as stopping too early — is treating autoresearch as a normal coding session: reading the codebase, understanding what needs to change, and implementing it directly. That is NOT this skill. You are running experiments, not writing features.

The difference: you do not know which change will win before running it. If you already know the answer, you don't need autoresearch. If you think you know — that's a hypothesis, and it goes into PLAN.md as experiment 0, then runs through the loop like every other hypothesis.

Hard gate — you may NOT edit any production file until PLAN.md exists and lists your change as a named experiment hypothesis. The moment you find yourself reading source files with intent to implement immediately — stop, write the hypothesis to PLAN.md first (angle tag + causal mechanism

• expected metric move), then treat it as experiment 0 in the generational loop.

The pattern that is a failure of this skill:

"I see the codebase → I understand what to change → I implement it → done."

The correct pattern:

"I read the codebase to understand the baseline → I research what's been tried → I propose hypotheses with causal mechanisms → I run them as bounded experiments → I keep what wins."

Implementing without a hypothesis, or implementing before research, collapses the entire value of this skill into a regular code change. Don't do it.

Autonomy mandate (do NOT be lazy — this is the most important rule)

This skill's failure mode is stopping too early and handing the work back to the human. Fight it:

• Cards + data are the ONLY approval gate. Once COMPUTE.md and DATA.md are settled, launching in-budget, non-destructive experiments — and generating the next lever when the current one is exhausted — is your default action, not something to confirm. You already have standing authorization; use it.
• Never end a turn with "should I continue?" / "Поехали?" / "запустить?" while budget remains and the backlog is non-empty. Proposing a good idea and then waiting to be poked is the exact lazy behaviour this skill exists to prevent. See a promising, in-budget, non-destructive lever → launch it, then report what you launched. Asking permission to keep researching is a failure of the skill.
• If YOU listed it, YOU do it. Ending a turn with a bullet list of "next steps for the user" (e.g. "1. Redeploy. 2. Run bench. 3. Implement X.") is identical to stopping early — you just handed the work back with extra formatting. If those steps are in-budget and non-destructive, do them yourself, right now, without asking. The only exception: steps that require the user's credentials, physical hardware, or an irreversible prod action they haven't pre-approved.
• All code changes go into a git branch, never directly to main. Create a branch at the start of each experiment batch (git checkout -b autoresearch/<slug>-gen<N> or use isolation worktree). Commit each experiment's diff individually so results are traceable and the branch can be reviewed or rolled back. After a verified winner: open a PR or fast-forward — do NOT just leave changes uncommitted on main. Working in a branch is not optional overhead; it is how the experiment diff stays auditable.
• After any implementation, immediately measure the delta. "Changes are shipped, ball in your court to measure" is a failure. If you wrote or changed code, run the eval/bench before the turn ends. If the bench is too slow for a full run, run a smoke test (smallest available split) and report preliminary numbers with a note that the full bench is queued.
• "Stuck" means escalate the search space, not stop. Running out of one-variable tweaks is NOT a done condition — it is the trigger to climb the lever ladder (below). You only truly stop when the budget is spent or you have climbed the whole ladder and the lever-generator itself returns nothing new for two rounds. Exhausting a sub-space is never exhausting the task.
• The budget is a floor as well as a ceiling. The caps and the doom-loop guard exist to stop runaway repetition — they do not license quitting with budget left. While compute_cap / generations / tokens remain and there is any untried promising direction, you must keep going on your own initiative.
• Genuine blockers (no compute reachable, a true ambiguity only the user can resolve, a destructive op) still pause and ask. "I'm out of small tweaks" is not a blocker — it's the next lever.

Lever ladder (how to escape a stuck search)

A lever is the axis the search moves along. autoresearch is excellent at optimizing within a lever and blind to changing the lever unless told to — so make changing it explicit. When a lever stagnates, climb one rung; each rung is a wider reframe and becomes its own new baseline to optimize:

1. Hyperparameter tweak — one variable vs the champion (lr, depth, schedule…). The default loop.
2. Orthogonal axis — a different family of one-variable changes (regularization, data mix, tokenizer, augmentation) the champion's family doesn't touch.
3. New lever / structural reframe — a different method, not a tweak: replace the algorithm, swap the harness file, change the solver (e.g. "learned edge-ranker instead of BFS", "graph transformer over the state graph", "distillation instead of RL"). This is normally outside the "one diff to train.py" frame — on stagnation you are required to generate levers at this rung, give each its own program.md baseline, and sweep within it. Mine DEEPRESEARCH.md / FINDINGS.md "future work" for these.

Record candidate levers in FINDINGS.md → "Next levers" so the loop always has somewhere to climb.

Workflow — orchestrator model

You are the orchestrator. You own Restate / Research / Ask-for-cards-and-data / Plan / Provision / aggregate / report, and you delegate the per-experiment train→eval→keep mini-pipeline to a dynamic workflow of subagents. For every run, create ~/autoresearch-runs/<slug>/ (override the parent with $AUTORESEARCH_RUNS_DIR) and populate:

1. Restate — write TASK.md: one paragraph of what the user asked, the unknowns and assumptions, the run mode (interactive vs headless/-p), and whether the task admits many hypotheses worth sweeping (it almost always does — that's the point of this skill).

1.5. Previous work audit — multi-agent codebase sweep (before internet research) — before going online, do a structured audit of what has already been tried in this repo/project. This prevents re-doing work, surfaces the current baseline, and seeds the experiment matrix with proven starting points. This step is parallel: spawn one agent per source simultaneously, never read sequentially.

Spawn all of the following as parallel Agent (subagent_type: Explore) calls in one message:

• Agent A — Bench results: find and read all results/, eval_*.json, bench_*.json, RESULTS.md, EXPERIMENTS.md files. Extract: current best metric, which configs were tested, which failed. Note the baseline number.
• Agent B — Git history: git log --oneline -50 + git diff HEAD~10..HEAD -- <relevant_files>. Extract: what changed recently, why (commit messages), any explicit "this improved / broke X".
• Agent C — Existing experiments / notes: find PLAN.md, FINDINGS.md, autoresearch-runs/, any TODO, FIXME, NOTE comments in key files. Extract: abandoned ideas and why, open TODOs.
• Agent D — Current code state: read the core model/training/eval files. Extract: what hyperparameters are currently set, any commented-out experiments, any # TODO: try comments.
• Agent E — Test suite: find and skim *_test.py, test_*.py, conftest.py. Extract: what's tested, what's explicitly NOT tested (signals known fragile areas).

Synthesize all agent outputs into PREVIOUS_WORK.md using the same format as DEEPRESEARCH.md (bullets + file:line references instead of URLs, no dumps):

## Current baseline
- metric: <value> (source: results/eval_foo.json)
## What was tried and kept
- <change>: +<delta> (commit abc123, merged 2026-06-10)
## What was tried and dropped
- <change>: <reason> (commit def456, reverted)
## Open hypotheses / TODOs in the code
- <idea> (file.py:42)
## Known fragile areas
- <area> (no tests; last broke in commit ghi789)

Fire notify.sh research_ready "previous_work_audit_done". Then proceed to internet research — DEEPRESEARCH.md extends PREVIOUS_WORK.md, it does not replace it. The experiment matrix must reference both sources.

2. Deep research — diverse literature + GitHub mining (before clarify) — always start by going out to the internet and surveying what already exists across multiple distinct angles; never jump to experiments on priors alone and never mine only one source. The goal is to seed the experiment matrix with ideas from maximally different communities — not ten variations of the same paper. Run the full multi-source sweep below:

   Parallelism rule for internet research: spawn A–D as parallel agents in one message. Each agent covers one angle and returns a structured findings block. Never run them sequentially.

   A. PapersWithCode + arXiv sweep (methods, benchmarks, SOTA):

   • bash scripts/pwc_search.sh "<task>" papers (and … methods / … datasets)
   • arXiv recent: WebSearch "site:arxiv.org <task> 2024 OR 2025" — pick 3-5 most-cited recent papers
   • HF Papers: fetch https://huggingface.co/papers?q=<task> — note any models with top downloads

   B. GitHub idea mining (first-class source — do not skip):

   • gh search repos "<task>" --language python --sort stars --limit 20 — top implementations
   • gh search code "<key_function_or_class>" --language python --limit 15 — reusable building blocks
   • gh search repos "<task> tricks OR ablation OR improve" --sort updated --limit 10 — experiment logs For the top 3-5 repos: fetch README.md, skim CHANGELOG or EXPERIMENTS.md if they exist, and note every technique listed under "what helped", "ablations", or "tips".

   C. Blog posts, tech reports, community tricks:

   • WebSearch "<task> tricks site:reddit.com OR site:huggingface.co/blog OR site:sebastianraschka.com"
   • WebSearch "<task> what works surprising result" — surface counterintuitive findings
   • Fetch the top 2-3 hits and extract concrete, reproducible changes

   D. Cross-domain transplant mining: For each "idea angle" in IDEA_ANGLES.md, run one query: WebSearch "<angle_domain> <task equivalent>". Example: task = "sequence classification" → angles include "time-series anomaly detection", "protein secondary structure", "code understanding".

   Cross-check claims: when two sources disagree on a number or a claim, note both and trust the one with code/leaderboard backing. Synthesize all findings into a cited DEEPRESEARCH.md (see format below). Then distil into RESEARCH.md from assets/research_card.template.md. Fire notify.sh research_ready.

3. Ask where to get CARDS and DATA (the user's explicit requirement) — confirm compute and data before any fan-out (workflows take no mid-run input, so this cannot wait).

   • Interactive: one AskUserQuestion bundling compute (Kaggle notebooks / Local GPU / Cloud SSH) and data (HF Hub / Kaggle dataset / a URL / a PapersWithCode dataset), plus the budget (how many experiments, seconds each, total compute cap, parallelism). Write COMPUTE.md (chosen provider + connection details) and DATA.md (chosen source + slug/path).
   • Headless (-p): do not hang. Write best-guess defaults to COMPUTE.md/DATA.md (probe local GPU first; else Kaggle if the kaggle MCP is connected; else design-only), fire notify.sh approval_required "<assumptions, one line>", and proceed.
   • Always write BUDGET.md here (see "Experiment budget"), using defaults when nothing is given.

4. Plan (diversity-constrained, mechanism-required) — write program.md from assets/program.template.md and PLAN.md (the experiment matrix).

   Anti-shortcut check: before writing the matrix, verify you have not already edited any production file. If you have — that edit is experiment 0; add it to the matrix as-is, measure its delta, and continue from there. Do not pretend it was always planned.

   Every hypothesis in the matrix MUST include three fields (from the think-first protocol):

   • mechanism: one sentence causal path (A → B → C → metric)
   • expected_delta: numeric estimate + direction (e.g. "−0.002 bpb, ~0.3% relative")
   • falsification: what result would disprove the mechanism

   The matrix must be maximally diverse: apply the angle-coverage check — every hypothesis cites a paper/repo/post from DEEPRESEARCH.md and comes from a different angle. Include ≥1 angle-K (scale-first) and ≥1 angle-F (kernel/efficiency) hypothesis. No two seed experiments may share an angle unless the matrix has more experiments than angles. Run idea spinning if homogeneous. Fire notify.sh code_ready "<N experiments queued>".

5. Provision compute (auto-detect) — pick where experiments actually run:

   • bash scripts/gpu_probe.sh → if local_gpu=yes with enough free VRAM, run locally.
   • else if the user chose Kaggle and the kaggle MCP is connected → open a session with mcp__kaggle__create_notebook_session, poll …get_notebook_session_status, pull artifacts with …download_notebook_output.
   • else if a Cloud SSH host is in COMPUTE.md → run via ssh <host> '<cmd>'.
   • If no compute is reachable → design-only mode: write the matrix + a runnable harness, fire notify.sh approval_required "design-only: no compute reachable", print run instructions, and stop. Do not fabricate metrics. Record the outcome in COMPUTE.md and fire the additive compute_ready.

6. Run the generational research loop (dynamic workflow) — this is the long-running heart of the skill (see "Long-running iterative loop" below). Substitute the placeholders in assets/research_loop.template.js (__RUN_DIR__, __SECONDS__, __METRIC__, __DIRECTION__, __SEED_EXPERIMENTS_JSON__ from PLAN.md, plus __MAX_GENERATIONS__, __HYPOTHESES_PER_GEN__, __PROPOSERS__, __CRITICS__, __STAGNATION__ from BUDGET.md), write it to <run>/workflow.js, seed the shared board from assets/board.template.md → <run>/FINDINGS.md, and run it with the Workflow tool ({scriptPath: "<run>/workflow.js"}, in the background). The workflow loops over generations: parallel proposer teams each covering a distinct idea angle read the board and propose fresh one-variable hypotheses, a peer-critic panel prunes redundant/weak ones before any GPU is spent, survivors train for the fixed budget and eval the metric, kept winners are adversarially re-checked, and the Share phase appends results to board.jsonl/FINDINGS.md + rewrites leaderboard.md and the champion. Each experiment returns a concise structured result only (exp_id, metric, delta, keep, note) — never log dumps. The loop keeps going until __MAX_GENERATIONS__, __STAGNATION__ consecutive no-improvement generations, or the token budget runs low. Append one EXPERIMENTS.md row per result and update BUDGET.md spent. Fire the additive experiment_kept when a verified winner takes the top spot (new champion).

   • Single-pass fallback: if the matrix is tiny (≤3) or you explicitly want one round only, use assets/experiment_workflow.template.js instead (no propose/critique loop — just fan out the matrix once, verify, report).
   • If workflows are disabled, fall back to spawning Agent subagents in parallel (one per experiment) and run the propose→critique→experiment→verify→share generations yourself, turn-by-turn — same contract, just orchestrated by you.

7. Aggregate & report — write RESULTS.md: the best verified config, the full comparison table from EXPERIMENTS.md, and the winning diff vs baseline. Update program.md's idea table. Optionally publish the winning config to the HF Hub via ml-intern's hf_push.sh (see "Publishing"). Fire notify.sh train_done "<best metric> @ <run slug>".

Diversity-first idea mining

The single biggest failure mode of autoresearch is converging on a cluster of similar ideas — ten variations of "change the learning rate schedule" while ignoring regularization, architecture, data augmentation, and cross-domain transplants entirely. This section exists to prevent that.

Idea angle taxonomy

Before writing any hypothesis, assign each idea to one angle. A healthy seed matrix covers at least 5 distinct angles. Default angle list (extend for the specific task):

Write IDEA_ANGLES.md in the run directory: one section per angle, listing ideas found for each. At minimum, seed experiments must cover angles A–E. If PapersWithCode + GitHub yield ideas for G/I/J, include at least one each — those are the highest-surprise hypotheses.

Anti-convergence check

Before finalizing PLAN.md, count how many hypotheses share an angle. If any angle holds >30% of the total (e.g. 5 of 12 are all optimization tweaks), replace the excess with ideas from under- represented angles, sourced from DEEPRESEARCH.md. This is not optional — a homogeneous matrix wastes budget rediscovering the same gradient.

Idea spinning (generate diverse variants from a seed)

When a literature search yields one good idea but the matrix needs more diversity, spin it into orthogonal variants using these transformations. Apply each transformation to the seed idea and check whether the result falls in a different angle — if yes, add it:

1. Scale — what happens at 0.1×, 10×, 100× the magnitude? (e.g. dropout 0.1 → 0.5 → 0.9)
2. Inversion / antithesis — what if the opposite is true? (e.g. "larger batch helps" → test tiny batch)
3. Cross-domain transplant — what analogous technique exists in CV / RL / audio / bioinformatics?
4. Simplification — what is the simplest possible version? (remove 80% of the idea, keep the core)
5. Combination — combine two ideas from different angles that have never been tested together
6. Temporal shift — apply the idea at a different stage (warm-up only, end-of-training only, alternating)
7. Negation of assumption — identify the implicit assumption the idea makes and remove it

Record every spun variant in IDEA_ANGLES.md under its angle, with the parent idea and which transformation produced it. In each generation's Propose phase, the idea-spinner transformation set is shared with proposers so they can apply it to the current champion — not just to the seed ideas.

GitHub search protocol (mandatory step in deep research)

GitHub surfaces ideas that never made it into papers — engineering tricks, ablation logs, bug-fixes that happen to improve accuracy, configuration files from top-performing teams. Do not skip this step.

# Step 1 — find top repos for the task
gh search repos "<task>" --language python --sort stars --limit 20

# Step 2 — look for active experiment logs / ablation notes
gh search repos "<task> ablation OR tricks OR experiment" --sort updated --limit 10

# Step 3 — find code patterns (key functions, architectural motifs)
gh search code "<task_key_symbol>" --language python --limit 15

# Step 4 — for the top 3-5 repos: mine the README, issues, and any RESULTS or EXPERIMENTS file
gh api repos/<owner>/<repo>/contents/README.md --jq '.content' | base64 -d
gh search issues "<task> what helped OR improved" --repo <owner>/<repo> --limit 10

For each repo: extract concrete, one-line-testable claims (e.g. "layer norm before attention gave +0.3 val acc"). Add each to IDEA_ANGLES.md under angle I ("GitHub / open-source trick"). Note the repo URL and commit/issue that surfaces the claim — cite it in DEEPRESEARCH.md.

Long-running iterative loop (the AutoScientists model + diversity enforcement)

The default fan-out (step 6) is not a single pass over a fixed matrix — it is a long-running generational loop, adapted from mims-harvard/AutoScientists: parallel agent teams self-organize around the best ideas, critique each other before spending compute, and share what they learn on a common board so the search compounds instead of repeating itself. One generation:

1. Propose (parallel teams — diversity-assigned, think-first protocol). __PROPOSERS__ proposer agents run concurrently, each assigned a distinct idea angle from IDEA_ANGLES.md. An agent assigned angle C (architecture) must generate architecture-family hypotheses; it must not re-propose what angle A (optimization) already covers. Each proposer reads the shared board (FINDINGS.md, leaderboard.md, DEEPRESEARCH.md, IDEA_ANGLES.md, program.md) and the current champion.

   Think-first protocol (mandatory for every hypothesis): Before writing a hypothesis, the proposer must answer three questions in its scratchpad — only hypotheses with clear answers to all three are allowed into the proposal:

   • Mechanism: Why should this change improve the metric? Name the causal path (e.g. "fused cross-entropy removes the N×vocab intermediate allocation → less peak memory → larger effective batch at same VRAM → more gradient signal per step → lower loss").
   • Expected move: How large a delta is plausible, and in which direction? ("expect −0.003 bpb, i.e. ~0.5% relative gain"). A hypothesis with no expected size is not a hypothesis — it's a guess.
   • Falsification condition: What result would tell us the mechanism hypothesis was wrong? ("if loss is unchanged or worse, the bottleneck is not memory bandwidth but something else").

   Proposers propose fresh one-variable hypotheses that (a) have a stated causal mechanism, (b) build on what works in their angle, (c) are not on the already-tried list, and (d) cite a concrete source from DEEPRESEARCH.md or IDEA_ANGLES.md. They may apply idea-spinner transformations to the champion's best feature to generate orthogonal variants. If an angle is exhausted, climb to angle G (cross-domain transplant) — do NOT re-propose from the same family.

2. Peer-critique (before any GPU) — diversity + quality filter. A panel of __CRITICS__ critic agents scores every proposal on three axes: (a) quality (expected impact × plausibility, 0-10), (b) novelty vs the board (not a near-duplicate of a tried idea), and (c) angle diversity (does this generation cover at least 3 distinct angles in the surviving set?). Only proposals with a majority "novel" vote, a mean quality score ≥ 6/10, and a passing angle diversity check survive. The top __HYPOTHESES_PER_GEN__ go to compute; if the surviving set is angle-homogeneous, critics must substitute a lower-scored but angle-diverse proposal for one of the high-scored homogeneous ones.

3. Experiment + verify. Survivors fan out exactly like the single-pass mode: copy harness, apply one diff, train for seconds_per_experiment, eval, and adversarially re-check kept winners.

4. Share (update the board) — mechanism audit. The Share phase appends results to board.jsonl, rewrites FINDINGS.md, updates leaderboard.md + champion, and updates IDEA_ANGLES.md. It also runs a mechanism audit on every kept winner: does the observed delta match the predicted mechanism? Write one sentence: "Winner: X. Predicted: Y. Observed: Z. Mechanism confirmed/refuted because W." If the mechanism is refuted (the delta is real but came from a different effect), record that insight — it often reveals a better hypothesis for the next generation than the winning idea itself.

5. Champion + stagnation → climb the lever ladder. The best verified config is the champion; a new champion resets the stagnation counter. After __STAGNATION__ no-champion generations the loop does not quit — it climbs one rung of the lever ladder: first switch proposers to orthogonal axis mode (rung 2), and if still stuck after another __STAGNATION__ generations switch to new-lever mode (rung 3) — proposers must now propose structural reframes (a different method, a new harness/baseline), each becoming its own program.md and a fresh sub-search. Before triggering rung 3, run one GitHub re-search (gh search repos + gh search code) with the current champion's architecture/technique as the query — surface repos that already implement a superior variant and haven't been mined yet. Append every reframe to FINDINGS.md → "Next levers".

The loop only truly exits when: the compute_cap / token budget is spent, OR you have climbed to rung 3 and the lever-generator returns no new structural idea for two consecutive rounds (real convergence), OR max_generations is hit and budget remains and there are queued "Next levers" — in which case you relaunch (see "Staying alive") rather than report-and-stop. max_generations is a per-workflow batch size, not the end of the task. This is what lets a run go for hours or days — across many workflow relaunches, not one finite run.

Staying alive across context windows

A workflow runs in the background and survives your own context compaction — you are notified when it finishes. For genuinely long runs:

• Launch in the background and let the completion <task-notification> re-invoke you; do not poll in a tight loop (that wastes the prompt cache — see ScheduleWakeup guidance).
• Checkpoint to disk every generation (the Share phase already does this) so progress is durable. If the workflow is killed or interrupted, resume it with {scriptPath, resumeFromRunId} — the unchanged prefix of generations returns from cache and only the unfinished tail re-runs. The on-disk board lets a fresh workflow pick up where the last one stopped.
• If you must babysit an external provider (a Kaggle session, a cloud SSH job) the harness can't notify you about, use ScheduleWakeup with a delay matched to how fast that state changes — not a fixed short poll.
• Update BUDGET.md spent and fire notify.sh experiment_kept "<new champion>" on each champion change so the user sees progress without reading logs.

Outer driver (this is what makes it actually long-running). A Workflow runs once and returns — it does not relaunch itself. So you, the orchestrator, are the loop around the loop. When a workflow returns, do not stop to ask: read its result + FINDINGS.md "Next levers" + IDEA_ANGLES.md uncovered angles, and if the budget still has room and any untried promising direction remains, immediately launch the next batch on your own initiative (a new generation batch on the current lever, or a fresh program.md for the next lever up the ladder). Only write RESULTS.md and stop when the budget is spent or the lever ladder is genuinely exhausted (rung 3 dry for two rounds). To survive your own context limits across this outer loop, drive it with /loop (self-paced) or ScheduleWakeup so a fresh context re-enters the skill, reads the on-disk board, and relaunches — the run continues for days without the user poking it.

Notifications

Reuse ml-intern's notifier — do not duplicate it. Resolve, in order: ~/.claude/skills/ml-intern/scripts/notify.sh, then $CLAUDE_PROJECT_DIR/.claude/skills/ml-intern/scripts/notify.sh.

bash ~/.claude/skills/ml-intern/scripts/notify.sh <event> "<message>"

Canonical events (same as ml-intern): plan_ready · code_ready · train_started · train_done · error · blocker · approval_required. The script interpolates any event string, so additive autoresearch events (research_ready, compute_ready, experiment_kept) work with no script change. The notifier is a graceful no-op when tokens are unset — always call it, never gate on token presence. If ml-intern is not installed, skip notifications with a one-line notice and continue — the research + experiment loop does not depend on it.

Deep research (existing solutions — diversity-first)

Before designing any experiment, do a real internet survey of what already works — this is what makes the experiment matrix good instead of guessed. The survey must be multi-angle, not a single pass over one source. Prefer the strongest tool available, in this order:

1. deep-research workflow / skill — if a /deep-research bundled workflow or a deep-research skill is available, invoke it with a focused question ("existing solutions, SOTA methods, and known tricks for <task> on <benchmark>; include GitHub repos, engineering tricks, counterintuitive results, and cross-domain transplants; return methods, metrics, code links, and what improved results"). It fans out web searches across angles, fetches and cross-checks sources, and returns a cited report — capture that report into DEEPRESEARCH.md.

2. Manual fan-out (fallback) — run all four source groups; skip none:

   • Papers: pwc_search.sh + arXiv + HF Papers (angles A–F, H in the taxonomy)
   • GitHub: the full GitHub search protocol from "Diversity-first idea mining" (angle I)
   • Community tricks: Reddit/HF blog/tech reports (WebSearch, angle J)
   • Cross-domain: adjacent-field search for each under-represented angle (angle G)

DEEPRESEARCH.md must capture, with a URL on every claim: the current SOTA + metric, the top existing solutions (method → result → code), the concrete tricks/hyperparameters that moved the metric (these become experiment hypotheses, tagged with their angle), known failure modes/pitfalls, and dataset notes. Add a section "GitHub findings" listing repo names and the engineering tricks each surfaced. Keep it cited and skimmable — no page dumps. RESEARCH.md is the distilled decision layer on top of it; PLAN.md's experiment matrix should be traceable to ideas found here (each hypothesis points at the source AND its angle tag). Never spend compute on an idea the literature already shows fails unless you're deliberately re-checking it.

Research-before-clarify rule

If the user mentions something you don't recognize — a paper, repo, model, method, dataset, benchmark, metric, or acronym — research it before asking them about it. pwc_search.sh / WebSearch / WebFetch the term, read the referenced repo's README and key files, skim the relevant HF/PapersWithCode pages. Only escalate to step 3's question when a term is genuinely unresolvable from public sources or the ambiguity is a real fork the docs don't settle. Asking the user to define something you could have looked up is a failure of this skill. (The cards-and-data question in step 3 is not subject to this rule — always ask it, since only the user knows their compute and data.)

PapersWithCode usage

scripts/pwc_search.sh "<query>" [papers|datasets|methods] curls the live PapersWithCode API at https://paperswithcode.co/api/v1/... (the old .com was retired; override with PWC_BASE if it moves again) and prints compact JSON (papers: id/title/arxiv_id/url; datasets: id/name/slug; methods: id/name/description). The papers/, datasets/, and methods/ endpoints work; there is no search/ or sota/ endpoint — for a benchmark leaderboard, pull the top papers hits plus a targeted WebSearch/WebFetch of the benchmark page. The script exits non-zero with a one-line notice if the API is unreachable or returns nothing — when it does, fall back to WebSearch + arXiv + HF Papers and note the fallback in RESEARCH.md. Never hang on a dead endpoint.

Compute providers

• Local GPU — bash scripts/gpu_probe.sh; if local_gpu=yes and free VRAM fits the model, run python train.py … directly. Cheapest path; prefer it when available.
• Kaggle notebooks (free T4/P100) — via the connected kaggle MCP: mcp__kaggle__create_notebook_session to launch, …get_notebook_session_status to poll, …download_notebook_output / …list_notebook_session_output to pull metrics/logs back. Keep each experiment within the session time limit; serialize if you hit quota.
• Cloud SSH — user supplies host (and optional key) in COMPUTE.md; run ssh <host> 'cd <dir> && python train.py …' and scp/rsync logs back. Treat unreachable host as a blocker, not a silent fallback.

Experiment budget

Always write BUDGET.md at step 3, even for a small matrix. It bounds the fan-out and the keep/discard loop:

metric                = <name>     # e.g. val_bpb | val_loss | accuracy
direction             = lower|higher   # which way is better
seed_experiments      = N          # size of the generation-0 matrix from PLAN.md
seconds_per_experiment = S         # fixed wall-clock train budget per experiment (karpathy default ~300)
parallelism           = P          # concurrent experiments (≤ workflow cap of 16; lower if VRAM-bound)
compute_cap           = H          # total GPU-hours OR wall-clock-hours for the whole run
# --- generational loop (long-running) ---
max_generations       = G          # hard cap on generations (the long-run bound)
hypotheses_per_gen    = K          # how many proposals survive critique → run, per generation
proposers             = Pn         # parallel proposer teams per generation (each assigned a distinct angle)
critics               = Cn         # peer critics per proposal round (critique-before-compute)
stagnation            = St         # exit after this many no-new-champion generations
--- spent ---
generations_run = 0
experiments_run = 0
gpu_min_used    = 0
best_metric     = <baseline>
champion        = <none yet>

Defaults when the user gives nothing: seed_experiments=6, seconds_per_experiment=300, parallelism=4 (or 1 on a single local GPU), compute_cap=2 GPU-h, max_generations=8, hypotheses_per_gen=4, proposers=3, critics=3, stagnation=3. For a one-round run set max_generations=1 (degenerates to the single-pass template). Scale max_generations/compute_cap up for "overnight" / "for days" requests. Update the spent block as experiments finish. Stop launching new experiments the moment a compute/token cap is hit — part of the doom-loop guard, not optional. But note the asymmetry: hitting max_generations or stagnation is not a cap — it is a signal to climb the lever ladder and relaunch (see Autonomy mandate). Only the compute_cap / token budget actually ends the run early.

Note on proposer angles: with proposers=3 and angles A–J available, assign the three most under- represented angles in IDEA_ANGLES.md to the three proposers. As angles get exhausted, rotate to the next under-represented ones. Angle I (GitHub tricks) and G (cross-domain) are always valid fallback angles — re-run the GitHub search with the current champion as

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