Darwinian Evolver evolutionary optimization skill proposals

Fixes on record

Merged to main. A stamp counts the issues a fix is linked to.

• #26760 feat(skill): darwinian-evolver optional skill

  merged to main

  files: optional-skills/research/darwinian-evolver/SKILL.md, optional-skills/research/darwinian-evolver/scripts/parrot_openrouter.py, optional-skills/research/darwinian-evolver/scripts/show_snapshot.py, optional-skills/research/darwinian-evolver/templates/custom_problem_template.py, scripts/release.py, tests/skills/test_darwinian_evolver_skill.py, website/docs/reference/optional-skills-catalog.md, website/docs/user-guide/skills/optional/research/research-darwinian-evolver.md, website/sidebars.ts

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  • optional-skills/research/darwinian-evolver/SKILL.md

    @@ -0,0 +1,199 @@
    +---
    +name: darwinian-evolver
    +description: Evolve prompts/regex/SQL/code with Imbue's evolution loop.
    +version: 0.1.0
    +author: Bihruze (Asahi0x), Hermes Agent
    +license: MIT
    +platforms: [linux, macos]
    +metadata:
    +  hermes:
    +    tags: [evolution, optimization, prompt-engineering, research]
    +    related_skills: [arxiv, jupyter-live-kernel]
    +---
    +
    +# Darwinian Evolver
    +
    +Run Imbue's [darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) — an
    +LLM-driven evolutionary search loop — to optimize a **prompt, regex, SQL query,
    +or small code snippet** against a fitness function.
    +
    +Status: thin wrapper around the upstream tool. The skill installs it, walks the
    +agent through writing a `Problem` definition (organism + evaluator + mutator),
    +and drives the loop via the upstream CLI or a small custom Python driver.
    +
    +**License:** the upstream tool is **AGPL-3.0**. The skill ONLY ever invokes it
    +via the upstream CLI or a `subprocess`/`uv run` call (mere aggregation). Do NOT
    +import upstream classes into Hermes itself.
    +
    +## When to Use
    +
    +- User says "optimize this prompt", "evolve a regex for X", "auto-improve this
    +  code/SQL", "search for a better instruction".
    +- You have a scorer (exact match, regex pass-rate, unit test, LLM-judge, runtime
    +  metric) AND a starting candidate (organism). If you don't have a scorer, stop
    +  and define one first — that's the hard part.
    +- Cost is OK: a typical run is 50–500 LLM calls. On gpt-4o-mini that's pennies;
    +  on Claude Sonnet it can be a few dollars.
    +
    +Do **not** use this when:
    +- The optimization target is differentiable (use gradient descent / DSPy).
    +- You only need to try 2–3 variants — just write them by hand.
    +- The fitness signal is purely subjective with no measurable criterion.
    +
    +## Prerequisites
    +
    +- Python ≥3.11
    +- `git`, `uv` (or `pip`)
    +- One of: `OPENROUTER_API_KEY`, `ANTHROPIC_API_KEY`, or `OPENAI_API_KEY`
    +
    +The skill ships a small `parrot_openrouter.py` driver that uses `OPENROUTER_API_KEY`
    +via the OpenAI SDK, so any model on OpenRouter works. The upstream CLI itself
    +hardcodes Anthropic and needs `ANTHROPIC_API_KEY`.
    +
    +## Install (One-Time)
    +
    +Run via the `terminal` tool:
    +
    +```bash
    +mkdir -p ~/.hermes/cache/darwinian-evolver && cd ~/.hermes/cache/darwinian-evolver
    +[ -d darwinian_evolver ] || git clone --depth 1 https://github.com/imbue-ai/darwinian_evolver.git
    +cd darwinian_evolver && uv sync
    +```
    +
    +Verify:
    +
    +```bash
    +cd ~/.hermes/cache/darwinian-evolver/darwinian_evolver \
    +  && uv run darwinian_evolver --help | head -5
    +```
    +
    +## Quick Start — The Built-In Parrot Example
    +
    +Tiny smoke test (requires `ANTHROPIC_API_KEY`):
    +
    +```bash
    +cd ~/.hermes/cache/darwinian-evolver/darwinian_evolver
    +uv run darwinian_evolver parrot \
    +  --num_iterations 2 \
    +  --num_parents_per_iteration 2 \
    +  --mutator_concurrency 2 --evaluator_concurrency 2 \
    +  --output_dir /tmp/parrot_demo
    +```
    +
    +Outputs:
    +- `/tmp/parrot_demo/snapshots/iteration_N.pkl` — pickled population per iteration
    +- `/tmp/parrot_demo/<jsonl>` — per-iteration JSON log (path printed at end)
    +
    +Open `~/.hermes/cache/darwinian-evolver/darwinian_evolver/darwinian_evolver/lineage_visualizer.html`
    +in a browser and load the JSON log to see the evolutionary tree.
    +
    +## Quick Start — OpenRouter Driver (No Anthropic Key)
    +
    +The skill ships `scripts/parrot_openrouter.py` — same parrot problem, but the
    +LLM call goes through OpenRouter so any provider works.
    +
    +```bash
    +# From wherever the skill is installed:
    +SKILL_DIR=~/.hermes/skills/research/darwinian-evolver
    +DE_DIR=~/.hermes/cache/darwinian-evolver/darwinian_evolver
    +
    +cd "$DE_DIR" && \
    +  EVOLVER_MODEL='openai/gpt-4o-mini' \
    +  uv run --with openai python "$SKILL_DIR/scripts/parrot_openrouter.py" \
    +    --num_iterations 3 --num_parents_per_iteration 2 \
    +    --output_dir /tmp/parrot_or
    +```
    +
    +Inspect the result with `scripts/show_snapshot.py`:
    +
    +```bash
    +uv run --with openai python "$SKILL_DIR/scripts/show_snapshot.py" \
    +  /tmp/parrot_or/snapshots/iteration_3.pkl
    +```
    +
    +Expected output: 7 evolved prompt templates ranked by score, with the best
    +landing around 0.6–0.8 (the seed `Say {{ phrase }}` scored 0.000).
    +
    +## Defining a Custom Problem
    +
    +The skill ships `templates/custom_problem_template.py` — copy, edit, run.
    +Three things you must define:
    +
    +1. **`Organism`** — a Pydantic `BaseModel` subclass holding the artifact being
    +   evolved (`prompt_template: str`, `regex_pattern: str`, `sql_query: str`,
    +   `code_block: str`, etc.). Add a `run(*args)` method that exercises it.
    +
    +2. **`Evaluator`** — `.evaluate(organism) -> EvaluationResult(score=..., trainable_failure_cases=[...], holdout_failure_cases=[...], is_viable=True)`.
    +   - **`score`** is in `[0, 1]`. Higher is better.
    +   - **`trainable_failure_cases`** — what the mutator sees. Include enough
    +     context (input, expected, actual) for the LLM to diagnose.
    +   - **`holdout_failure_cases`** — kept out of the mutator's view. Use these
    +     to detect overfitting.
    +   - **`is_viable=True`** unless the organism is completely broken (raises,
    +     returns None, etc.). A 0-score viable organism is fine — it just gets
    +     down-weighted in parent selection.
    +
    +3. **`Mutator`** — `.mutate(organism, failure_cases, learning_log_entries) -> list[Organism]`.
    +   Typically: build an LLM prompt that includes the current organism + a
    +   failure case + an ask to propose a fix; parse the LLM's response; return
    +   a new `Organism`. Return `[]` on parse failure — the loop handles it.
    +
    +Then write a driver script that wires `Problem(initial_organism, evaluator, [mutators])`
    +into `EvolveProblemLoop` and iterates over `loop.run(num_iterations=N)` — the
    +shipped `scripts/parrot_openrouter.py` is the reference.
    +
    +## Hyperparameters That Actually Matter
    +
    +| flag | default | when to change |
    +|---|---|---|
    +| `--num_iterations` | 5 | bump to 10–20 once you trust the evaluator |
    +| `--num_parents_per_iteration` | 4 | drop to 2 for cheap exploration |
    +| `--mutator_concurrency` | 10 | drop to 2–4 to avoid rate limits |
    +| `--evaluator_concurrency` | 10 | same; evaluator hits the LLM too |
    +| `--batch_size` | 1 | raise to 3–5 once your mutator handles multiple failures |
    +| `--verify_mutations` | off | turn on once mutator is wasteful (>10× cost saving on later runs per Imbue) |
    +| `--midpoint_score` | `p75` | leave alone unless scores cluster |
    +| `--sharpness` | 10 | leave alone |
    +
    +## Pitfalls
    +
    +1. **`Initial organism must be viable`** — set `is_viable=True` in your
    +   `EvaluationResult` even on a 0-score seed. The loop refuses non-viable
    +   organisms because they imply the loop has nothing to evolve from.
    +2. **Provider content filters kill runs.** Azure-backed OpenRouter models
    +   reject phrases like "ignore previous instructions" with HTTP 400. Wrap
    +   the LLM call in `try/except` and return `f"<LLM_ERROR: {e}>"` — the
    +   evolver will just score that organism 0 and move on.
    +3. **`loop.run()` is a generator** — calling it doesn't run anything until
    +   you iterate. Use `for snap in loop.run(num_iterations=N):`.
    +4. **Snapshots are nested pickles.** `iteration_N.pkl` contains a dict with
    +   `population_snapshot` (more pickled bytes). To unpickle you must have the
    +   `Organism` class importable under the same dotted path it was pickled at.
    +5. **Concurrency defaults are aggressive.** 10/10 will hit rate limits on
    +   most providers. Start with 2/2.
    +6. **CLI is hardcoded to Anthropic.** `uv run darwinian_evolver <problem>`
    +   reaches for `ANTHROPIC_API_KEY` and uses Claude Sonnet. To use any other
    +   provider, write a driver like `parrot_openrouter.py`.
    +7. **AGPL.** Never `from darwinian_evolver import ...` inside Hermes core.
    +   Custom driver scripts under `~/.hermes/skills/...` are user-side and fine.
    +8. **No PyPI package.** `pip install darwinian-evolver` will pull the wrong
    +   thing. Always install from the GitHub repo.
    +
    +## Verification
    +
    +After install + a parrot run, exit code 0 from this is sufficient:
    +
    +```bash
    +DE_DIR=~/.hermes/cache/darwinian-evolver/darwinian_evolver
    +ls "$DE_DIR/darwinian_evolver/lineage_visualizer.html" >/dev/null && \
    +cd "$DE_DIR" && uv run darwinian_evolver --help >/dev/null && \
    +echo "darwinian-evolver: OK"
    +```
    +
    +## References
    +
    +- [Imbue research post](https://imbue.com/research/2026-02-27-darwinian-evolver/)
    +- [ARC-AGI-2 results](https://imbue.com/research/2026-02-27-arc-agi-2-evolution/)
    +- [imbue-ai/darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) (AGPL-3.0)
    +- [Darwin Gödel Machines](https://arxiv.org/abs/2505.22954)
    +- [PromptBreeder](https://arxiv.org/abs/2309.16797)

  • optional-skills/research/darwinian-evolver/scripts/parrot_openrouter.py

    @@ -0,0 +1,218 @@
    +"""
    +parrot_openrouter: same as the upstream `parrot` example but the LLM call goes
    +through OpenRouter (OpenAI SDK) instead of Anthropic native. Lets us run an
    +end-to-end evolution with whatever model the user already has paid access to.
    +
    +Run with:
    +    uv --project darwinian_evolver run python parrot_openrouter.py \
    +        --num_iterations 3 --output_dir /tmp/parrot_out
    +
    +Reads `OPENROUTER_API_KEY` from the environment.
    +"""
    +from __future__ import annotations
    +
    +import argparse
    +import os
    +import sys
    +from pathlib import Path
    +
    +import jinja2
    +from openai import OpenAI
    +
    +# Vendored problem types from upstream (AGPL — only run via subprocess in production)
    +from darwinian_evolver.cli_common import build_hyperparameter_config_from_args
    +from darwinian_evolver.cli_common import register_hyperparameter_args
    +from darwinian_evolver.cli_common import parse_learning_log_view_type
    +from darwinian_evolver.evolve_problem_loop import EvolveProblemLoop
    +from darwinian_evolver.learning_log import LearningLogEntry
    +from darwinian_evolver.problem import EvaluationFailureCase
    +from darwinian_evolver.problem import EvaluationResult
    +from darwinian_evolver.problem import Evaluator
    +from darwinian_evolver.problem import Mutator
    +from darwinian_evolver.problem import Organism
    +from darwinian_evolver.problem import Problem
    +
    +DEFAULT_MODEL = os.environ.get("EVOLVER_MODEL", "openai/gpt-4o-mini")
    +
    +
    +def _client() -> OpenAI:
    +    key = os.environ.get("OPENROUTER_API_KEY")
    +    if not key:
    +        sys.exit("OPENROUTER_API_KEY is not set")
    +    return OpenAI(api_key=key, base_url="https://openrouter.ai/api/v1")
    +
    +
    +def _prompt_llm(prompt: str) -> str:
    +    try:
    +        r = _client().chat.completions.create(
    +            model=DEFAULT_MODEL,
    +            max_tokens=1024,
    +            messages=[{"role": "user", "content": prompt}],
    +        )
    +        return r.choices[0].message.content or ""
    +    except Exception as e:
    +        # Treat any provider error (rate limit, content filter, schema reject)
    +        # as a failed response. The evolver will simply see this as a low score
    +        # on this organism and move on — much friendlier than killing the run.
    +        return f"<LLM_ERROR: {type(e).__name__}: {e}>"
    +
    +
    +class ParrotOrganism(Organism):
    +    prompt_template: str
    +
    +    def run(self, phrase: str) -> str:
    +        try:
    +            prompt = jinja2.Template(self.prompt_template).render(phrase=phrase)
    +        except jinja2.exceptions.TemplateError as e:
    +            return f"Error rendering prompt: {e}"
    +        if not prompt:
    +            return ""
    +        return _prompt_llm(prompt)
    +
    +
    +class ParrotEvaluationFailureCase(EvaluationFailureCase):
    +    phrase: str
    +    response: str
    +
    +
    +class ImproveParrotMutator(Mutator[ParrotOrganism, ParrotEvaluationFailureCase]):
    +    IMPROVEMENT_PROMPT_TEMPLATE = """
    +We want to build a prompt that causes an LLM to repeat back a given phrase verbatim.
    +
    +The current prompt template is:
    +```
    +{{ organism.prompt_template }}
    +```
    +
    +Unfortunately, on this phrase:
    +```
    +{{ failure_case.phrase }}
    +```
    +the LLM responded with:
    +```
    +{{ failure_case.response }}
    +```
    +
    +Diagnose what went wrong, then propose an improved prompt template. Put the new
    +template in the LAST triple-backtick block of your response.
    +""".strip()
    +
    +    def mutate(
    +        self,
    +        organism: ParrotOrganism,
    +        failure_cases: list[ParrotEvaluationFailureCase],
    +        learning_log_entries: list[LearningLogEntry],
    +    ) -> list[ParrotOrganism]:
    +        fc = failure_cases[0]
    +        prompt = jinja2.Template(self.IMPROVEMENT_PROMPT_TEMPLATE).render(
    +            organism=organism, failure_case=fc
    +        )
    +        try:
    +            resp = _prompt_llm(prompt)
    +            parts = resp.split("```")
    +            if len(parts) < 3:
    +                return []
    +            new_tpl = parts[-2].strip()
    +            return [ParrotOrganism(prompt_template=new_tpl)]
    +        except Exception as e:
    +            print(f"mutate error: {e}", file=sys.stderr)
    +            return []
    +
    +
    +class ParrotEvaluator(Evaluator[ParrotOrganism, EvaluationResult, ParrotEvaluationFailureCase]):
    +    TRAINABLE_PHRASES = [
    +        "Hello world.",
    +        "bla",
    +        "Bla",
    +        "bla.",
    +        '"bla bla".',
    +        "Just say 'foo' once with no extra words.",
    +    ]
    +    HOLDOUT_PHRASES = [
    +        "bla, but only once.",
    +        "'bla'",
    +    ]
    +
    +    def evaluate(self, organism: ParrotOrganism) -> EvaluationResult:
    +        train_fails: list[ParrotEvaluationFailureCase] = []
    +        hold_fails: list[ParrotEvaluationFailureCase] = []
    +        for i, p in enumerate(self.TRAINABLE_PHRASES):
    +            r = organism.run(p)
    +            if r != p:
    +                train_fails.append(ParrotEvaluationFailureCase(
    +                    phrase=p, response=r, data_point_id=f"trainable_{i}"))
    +        for i, p in enumerate(self.HOLDOUT_PHRASES):
    +            r = organism.run(p)
    +            if r != p:
    +                hold_fails.append(ParrotEvaluationFailureCase(
    +                    phrase=p, response=r, data_point_id=f"holdout_{i}"))
    +        n_total = len(self.TRAINABLE_PHRASES) + len(self.HOLDOUT_PHRASES)
    +        n_ok = n_total - len(train_fails) - len(hold_fails)
    +        return EvaluationResult(
    +            score=n_ok / n_total,
    +            trainable_failure_cases=train_fails,
    +            holdout_failure_cases=hold_fails,
    +            # Always viable. Even a 0-score seed is a valid starting point; the
    +            # mutator should still get a chance to fix it.
    +            is_viable=True,
    +        )
    +
    +
    +def make_problem() -> Problem:
    +    return Problem[ParrotOrganism, EvaluationResult, ParrotEvaluationFailureCase](
    +        evaluator=ParrotEvaluator(),
    +        mutators=[ImproveParrotMutator()],
    +        initial_organism=ParrotOrganism(prompt_template="Say {{ phrase }}"),
    +    )
    +
    +
    +def main() -> int:
    +    ap = argparse.ArgumentParser()
    +    register_hyperparameter_args(ap.add_argument_group("hyperparameters"))
    +    ap.add_argument("--num_iterations", type=int, default=3)
    +    ap.add_argument("--mutator_concurrency", type=int, default=4)
    +    ap.add_argument("--evaluator_concurrency", type=int, default=4)
    +    ap.add_argument("--output_dir", type=str, required=True)
    +    args = ap.parse_args()
    +
    +    out = Path(args.output_dir)
    +    out.mkdir(parents=True, exist_ok=True)
    +
    +    hp = build_hyperparameter_config_from_args(args)
    +    loop = EvolveProblemLoop(
    +        problem=make_problem(),
    +        learning_log_view_type=parse_learning_log_view_type(hp.learning_log_view_type),
    +        num_parents_per_iteration=hp.num_parents_per_iteration,
    +        mutator_concurrency=args.mutator_concurrency,
    +        evaluator_concurrency=args.evaluator_concurrency,
    +        fixed_midpoint_score=hp.fixed_midpoint_score,
    +        midpoint_score_percentile=hp.midpoint_score_percentile,
    +        sharpness=hp.sharpness,
    +        novelty_weight=hp.novelty_weight,
    +        batch_size=hp.batch_size,
    +        should_verify_mutations=hp.verify_mutations,
    +    )
    +
    +    import json
    +    log_path = out / "results.jsonl"
    +    snap_dir = out / "snapshots"
    +    snap_dir.mkdir(exist_ok=True)
    +    print("Evaluating initial organism...")
    +    for snap in loop.run(num_iterations=args.num_iterations):
    +        (snap_dir / f"iteration_{snap.iteration}.pkl").write_bytes(snap.snapshot)
    +        _, best_eval = snap.best_organism_result
    +        print(f"iter={snap.iteration} pop={snap.population_size} "
    +              f"best_score={best_eval.score:.3f}")
    +        with log_path.open("a") as f:
    +            f.write(json.dumps({
    +                "iteration": snap.iteration,
    +                "best_score": best_eval.score,
    +                "pop_size": snap.population_size,
    +                "score_percentiles": {str(k): v for k, v in snap.score_percentiles.items()},
    +            }) + "\n")
    +    print(f"\nDone. Results in: {out}")
    +    return 0
    +
    +
    +if __name__ == "__main__":
    +    sys.exit(main())

  • optional-skills/research/darwinian-evolver/scripts/show_snapshot.py

    @@ -0,0 +1,69 @@
    +"""
    +show_snapshot.py — Dump the population from a darwinian-evolver snapshot pickle.
    +
    +Usage:
    +    python show_snapshot.py PATH/TO/iteration_N.pkl [--field prompt_template]
    +
    +The script is intentionally Organism-agnostic: it walks `org.__dict__` and prints
    +all str fields. By default it shows `prompt_template` if present; pass --field to
    +target a different attribute (e.g. `regex_pattern`, `sql_query`, `code_block`).
    +"""
    +from __future__ import annotations
    +
    +import argparse
    +import pickle
    +import sys
    +from pathlib import Path
    +
    +
    +def main() -> int:
    +    ap = argparse.ArgumentParser()
    +    ap.add_argument("snapshot", type=Path)
    +    ap.add_argument(
    +        "--field",
    +        default=None,
    +        help="Organism attribute to display. Defaults to the first str field found.",
    +    )
    +    ap.add_argument("--top", type=int, default=None, help="Show only top N by score.")
    +    args = ap.parse_args()
    +
    +    if not args.snapshot.exists():
    +        sys.exit(f"snapshot not found: {args.snapshot}")
    +
    +    # The outer pickle wraps a dict; the inner pickle contains the actual organism
    +    # objects, which must be importable under their original dotted path. If you
    +    # ran a custom driver, make sure its module is on sys.path before calling this.
    +    outer = pickle.loads(args.snapshot.read_bytes())
    +    if not isinstance(outer, dict) or "population_snapshot" not in outer:
    +        sys.exit("not a darwinian-evolver snapshot (no population_snapshot key)")
    +    inner = pickle.loads(outer["population_snapshot"])
    +    pairs = inner["organisms"]  # list of (Organism, EvaluationResult)
    +
    +    print(f"# organisms: {len(pairs)}\n")
    +    ranked = sorted(pairs, key=lambda p: getattr(p[1], "score", 0) or 0, reverse=True)
    +    if args.top:
    +        ranked = ranked[: args.top]
    +
    +    for i, (org, res) in enumerate(ranked):
    +        score = getattr(res, "score", float("nan"))
    +        print(f"=== rank {i} score={score:.3f} ===")
    +        # pick field
    +        field = args.field
    +        if field is None:
    +            for k, v in vars(org).items():
    +                if isinstance(v, str) and not k.startswith("_") and k not in ("id",):
    +                    field = k
    +                    break
    +        val = getattr(org, field, None) if field else None
    +        if val is None:
    +            print(f"  (no string field; org fields: {list(vars(org).keys())})")
    +        else:
    +            print(f"  {field} ({len(val)} chars):")
    +            for ln in val.splitlines()[:30]:
    +                print(f"    {ln}")
    +        print()
    +    return 0
    +
    +
    +if __name__ == "__main__":
    +    sys.exit(main())

  • optional-skills/research/darwinian-evolver/templates/custom_problem_template.py

    @@ -0,0 +1,240 @@
    +"""
    +Template: a custom darwinian-evolver problem.
    +
    +Copy this file, fill in the THREE marked spots (Organism, Evaluator, Mutator),
    +then run it as a driver script. The skeleton handles all the wiring so you only
    +write the domain-specific logic.
    +
    +To run:
    +    cd ~/.hermes/cache/darwinian-evolver/darwinian_evolver
    +    OPENROUTER_API_KEY=... uv run --with openai python /path/to/this_file.py \
    +        --num_iterations 3 --num_parents_per_iteration 2 \
    +        --output_dir /tmp/my_problem
    +
    +The pattern mirrors `scripts/parrot_openrouter.py` (the working reference).
    +"""
    +from __future__ import annotations
    +
    +import argparse
    +import os
    +import sys
    +from pathlib import Path
    +
    +from openai import OpenAI
    +
    +# Upstream types (AGPL — invoked via subprocess in production; importing here
    +# is fine for skill-side driver scripts the user owns).
    +from darwinian_evolver.cli_common import (
    +    build_hyperparameter_config_from_args,
    +    parse_learning_log_view_type,
    +    register_hyperparameter_args,
    +)
    +from darwinian_evolver.evolve_problem_loop import EvolveProblemLoop
    +from darwinian_evolver.learning_log import LearningLogEntry
    +from darwinian_evolver.problem import (
    +    EvaluationFailureCase,
    +    EvaluationResult,
    +    Evaluator,
    +    Mutator,
    +    Organism,
    +    Problem,
    +)
    +
    +DEFAULT_MODEL = os.environ.get("EVOLVER_MODEL", "openai/gpt-4o-mini")
    +
    +
    +def _client() -> OpenAI:
    +    key = os.environ.get("OPENROUTER_API_KEY")
    +    if not key:
    +        sys.exit("OPENROUTER_API_KEY is not set")
    +    return OpenAI(api_key=key, base_url="https://openrouter.ai/api/v1")
    +
    +
    +def _prompt_llm(prompt: str, max_tokens: int = 1024) -> str:
    +    try:
    +        r = _client().chat.completions.create(
    +            model=DEFAULT_MODEL,
    +            max_tokens=[redacted:secret-assignment]
    +            messages=[{"role": "user", "content": prompt}],
    +        )
    +        return r.choices[0].message.content or ""
    +    except Exception as e:
    +        # Never let one bad LLM response kill the run.
    +        return f"<LLM_ERROR: {type(e).__name__}: {e}>"
    +
    +
    +# ---------------------------------------------------------------------------
    +# 1. ORGANISM — what you are evolving.
    +# ---------------------------------------------------------------------------
    +class MyOrganism(Organism):
    +    # TODO: replace with your artifact field. Common shapes:
    +    #   prompt_template: str
    +    #   regex_pattern: str
    +    #   sql_query: str
    +    #   code_block: str
    +    artifact: str
    +
    +    def run(self, *inputs) -> str:
    +        """Exercise the organism on a test input. Return whatever your
    +        evaluator wants to score."""
    +        # TODO: implement. For prompt evolution this typically calls _prompt_llm
    +        # with the artifact rendered against the input. For regex/SQL it would
    +        # call `re.findall(self.artifact, input)` / execute SQL / etc.
    +        raise NotImplementedError
    +
    +
    +# ---------------------------------------------------------------------------
    +# 2. EVALUATOR — score organisms and surface failures the mutator can learn from.
    +# ---------------------------------------------------------------------------
    +class MyFailureCase(EvaluationFailureCase):
    +    # TODO: include enough context for the LLM to diagnose the failure.
    +    input: str
    +    expected: str
    +    actual: str
    +
    +
    +class MyEvaluator(Evaluator[MyOrganism, EvaluationResult, MyFailureCase]):
    +    # Split your dataset. Mutator only sees trainable; holdout detects overfitting.
    +    TRAINABLE = [
    +        # TODO: list of (input, expected) tuples
    +        # ("input1", "expected1"),
    +    ]
    +    HOLDOUT = [
    +        # TODO: separate set the mutator never sees
    +    ]
    +
    +    def evaluate(self, organism: MyOrganism) -> EvaluationResult:
    +        train_fails: list[MyFailureCase] = []
    +        hold_fails: list[MyFailureCase] = []
    +        for i, (inp, expected) in enumerate(self.TRAINABLE):
    +            actual = organism.run(inp)
    +            if actual != expected:
    +                train_fails.append(MyFailureCase(
    +                    input=inp, expected=expected, actual=actual,
    +                    data_point_id=f"trainable_{i}",
    +                ))
    +        for i, (inp, expected) in enumerate(self.HOLDOUT):
    +            actual = organism.run(inp)
    +            if actual != expected:
    +                hold_fails.append(MyFailureCase(
    +                    input=inp, expected=expected, actual=actual,
    +                    data_point_id=f"holdout_{i}",
    +                ))
    +        n_total = len(self.TRAINABLE) + len(self.HOLDOUT)
    +        n_ok = n_total - len(train_fails) - len(hold_fails)
    +        return EvaluationResult(
    +            score=n_ok / n_total if n_total else 0.0,
    +            trainable_failure_cases=train_fails,
    +            holdout_failure_cases=hold_fails,
    +            # Always-viable. The evolver only blocks completely-broken organisms;
    +            # a 0-score organism is fine and will simply be sampled less often.
    +            is_viable=True,
    +        )
    +
    +
    +# ---------------------------------------------------------------------------
    +# 3. MUTATOR — LLM proposes an improved organism from a failure case.
    +# ---------------------------------------------------------------------------
    +class MyMutator(Mutator[MyOrganism, MyFailureCase]):
    +    PROMPT = """
    +The current artifact is:
    +```
    +{artifact}
    +```
    +
    +On this input:
    +```
    +{input}
    +```
    +it produced:
    +```
    +{actual}
    +```
    +but we wanted:
    +```
    +{expected}
    +```
    +
    +Diagnose what went wrong, then propose an improved version of the artifact.
    +Put the new version in the LAST triple-backtick block of your response.
    +""".strip()
    +
    +    def mutate(
    +        self,
    +        organism: MyOrganism,
    +        failure_cases: list[MyFailureCase],
    +        learning_log_entries: list[LearningLogEntry],
    +    ) -> list[MyOrganism]:
    +        fc = failure_cases[0]
    +        prompt = self.PROMPT.format(
    +            artifact=organism.artifact,
    +            input=fc.input,
    +            actual=fc.actual,
    +            expected=fc.expected,
    +        )
    +        resp = _prompt_llm(prompt)
    +        parts = resp.split("```")
    +        if len(parts) < 3:
    +            return []
    +        new_artifact = parts[-2].strip()
    +        # Strip an opening language tag like "python\n" or "sql\n"
    +        if "\n" in new_artifact:
    +            first_line, rest = new_artifact.split("\n", 1)
    +            if first_line and not first_line.startswith(" ") and len(first_line) < 20:
    +                new_artifact = rest
    +        return [MyOrganism(artifact=new_artifact)]
    +
    +
    +# ---------------------------------------------------------------------------
    +# Driver — fills in the EvolveProblemLoop boilerplate. You shouldn't need to
    +# touch anything below this line for a typical run.
    +# ---------------------------------------------------------------------------
    +def make_problem() -> Problem:
    +    initial = MyOrganism(artifact="TODO: starting artifact here")  # TODO
    +    return Problem[MyOrganism, EvaluationResult, MyFailureCase](
    +        evaluator=MyEvaluator(),
    +        mutators=[MyMutator()],
    +        initial_organism=initial,
    +    )
    +
    +
    +def main() -> int:
    +    ap = argparse.ArgumentParser()
    +    register_hyperparameter_args(ap.add_argument_group("hyperparameters"))
    +    ap.add_argument("--num_iterations", type=int, default=3)
    +    ap.add_argument("--mutator_concurrency", type=int, default=2)
    +    ap.add_argument("--evaluator_concurrency", type=int, default=2)
    +    ap.add_argument("--output_dir", type=str, required=True)
    +    args = ap.parse_args()
    +
    +    out = Path(args.output_dir)
    +    out.mkdir(parents=True, exist_ok=True)
    +    (out / "snapshots").mkdir(exist_ok=True)
    +
    +    hp = build_hyperparameter_config_from_args(args)
    +    loop = EvolveProblemLoop(
    +        problem=make_problem(),
    +        learning_log_view_type=parse_learning_log_view_type(hp.learning_log_view_type),
    +        num_parents_per_iteration=hp.num_parents_per_iteration,
    +        mutator_concurrency=args.mutator_concurrency,
    +        evaluator_concurrency=args.evaluator_concurrency,
    +        fixed_midpoint_score=hp.fixed_midpoint_score,
    +        midpoint_score_percentile=hp.midpoint_score_percentile,
    +        sharpness=hp.sharpness,
    +        novelty_weight=hp.novelty_weight,
    +        batch_size=hp.batch_size,
    +        should_verify_mutations=hp.verify_mutations,
    +    )
    +
    +    print("Evaluating initial organism...")
    +    for snap in loop.run(num_iterations=args.num_iterations):
    +        (out / "snapshots" / f"iteration_{snap.iteration}.pkl").write_bytes(snap.snapshot)
    +        _, best = snap.best_organism_result
    +        print(f"iter={snap.iteration} pop={snap.population_size} best_score={best.score:.3f}")
    +
    +    print(f"\nDone. Results in: {out}")
    +    return 0
    +
    +
    +if __name__ == "__main__":
    +    sys.exit(main())

  • scripts/release.py

    @@ -59,6 +59,7 @@
         "[redacted:email]": "mikeyobrien",
         "[redacted:email]": "qiyin-code",
         "[redacted:email]": "aashizpoudel",
    +    "[email protected]": "Bihruze",
         "[redacted:email]": "nidhi-singh02",
         "[email protected]": "aashizpoudel",
         "[redacted:email]": "olisikh",

  • tests/skills/test_darwinian_evolver_skill.py

    @@ -0,0 +1,102 @@
    +"""
    +Smoke tests for the darwinian-evolver optional skill.
    +
    +We can't actually run the evolution loop in CI (it needs network + a paid LLM),
    +so these tests verify:
    +  - SKILL.md frontmatter conforms to the hardline format
    +  - shipped scripts parse as valid Python
    +  - the scripts reference the right env var / module paths
    +"""
    +from __future__ import annotations
    +
    +import ast
    +import re
    +from pathlib import Path
    +
    +import pytest
    +import yaml
    +
    +SKILL_DIR = Path(__file__).resolve().parents[2] / "optional-skills" / "research" / "darwinian-evolver"
    +
    +
    [redacted:email](scope="module")
    +def frontmatter() -> dict:
    +    src = (SKILL_DIR / "SKILL.md").read_text()
    +    m = re.search(r"^---\n(.*?)\n---", src, re.DOTALL)
    +    assert m, "SKILL.md missing YAML frontmatter"
    +    return yaml.safe_load(m.group(1))
    +
    +
    +def test_skill_dir_exists() -> None:
    +    assert SKILL_DIR.is_dir(), f"missing skill dir: {SKILL_DIR}"
    +
    +
    +def test_skill_md_present() -> None:
    +    assert (SKILL_DIR / "SKILL.md").is_file()
    +
    +
    +def test_description_under_60_chars(frontmatter) -> None:
    +    desc = frontmatter["description"]
    +    assert len(desc) <= 60, f"description is {len(desc)} chars (hardline ≤60): {desc!r}"
    +
    +
    +def test_name_matches_dir(frontmatter) -> None:
    +    assert frontmatter["name"] == "darwinian-evolver"
    +
    +
    +def test_platforms_excludes_windows(frontmatter) -> None:
    +    # Upstream uses func_timeout (POSIX signals) and uv subprocess pipelines; the
    +    # skill is gated [linux, macos]. If we ever port to Windows, update this test
    +    # to assert ["linux", "macos", "windows"].
    +    assert "windows" not in frontmatter["platforms"]
    +    assert set(frontmatter["platforms"]) >= {"linux", "macos"}
    +
    +
    +def test_author_credits_contributor(frontmatter) -> None:
    +    author = frontmatter["author"]
    +    assert "Bihruze" in author, f"author should credit the original contributor: {author!r}"
    +
    +
    +def test_license_mit(frontmatter) -> None:
    +    assert frontmatter["license"] == "MIT"
    +
    +
    [redacted:email](
    +    "path",
    +    [
    +        "scripts/parrot_openrouter.py",
    +        "scripts/show_snapshot.py",
    +        "templates/custom_problem_template.py",
    +    ],
    +)
    +def test_shipped_scripts_parse(path: str) -> None:
    +    src = (SKILL_DIR / path).read_text()
    +    ast.parse(src)  # raises SyntaxError on broken Python
    +
    +
    +def test_parrot_script_uses_openrouter() -> None:
    +    src = (SKILL_DIR / "scripts" / "parrot_openrouter.py").read_text()
    +    assert "OPENROUTER_API_KEY" in src, "parrot driver should read OPENROUTER_API_KEY"
    +    assert "openrouter.ai/api/v1" in src, "parrot driver should target OpenRouter"
    +    assert "EVOLVER_MODEL" in src, "model should be overridable via EVOLVER_MODEL"
    +
    +
    +def test_parrot_script_has_error_swallowing() -> None:
    +    """Provider content-filter / rate-limit must not kill the run — see Pitfall 2."""
    +    src = (SKILL_DIR / "scripts" / "parrot_openrouter.py").read_text()
    +    assert "LLM_ERROR" in src, "_prompt_llm should swallow provider errors and tag them"
    +
    +
    +def test_skill_calls_out_agpl(frontmatter) -> None:
    +    """The upstream tool is AGPL-3.0. The skill MUST flag this so users don't
    +    import it into MIT-licensed code by accident."""
    +    src = (SKILL_DIR / "SKILL.md").read_text()
    +    assert "AGPL" in src, "SKILL.md must mention upstream AGPL license"
    +
    +
    +def test_skill_pitfalls_section_present() -> None:
    +    src = (SKILL_DIR / "SKILL.md").read_text()
    +    assert "## Pitfalls" in src
    +    # Pitfalls we discovered during the spike — keep them in sync with reality.
    +    assert "Initial organism must be viable" in src
    +    assert "generator" in src  # loop.run() pitfall

  • website/docs/reference/optional-skills-catalog.md

    @@ -161,6 +161,7 @@ hermes skills uninstall <skill-name>
     | Skill | Description |
     |-------|-------------|
     | [**bioinformatics**](/docs/user-guide/skills/optional/research/research-bioinformatics) | Gateway to 400+ bioinformatics skills from bioSkills and ClawBio. Covers genomics, transcriptomics, single-cell, variant calling, pharmacogenomics, metagenomics, structural biology, and more. Fetches domain-specific reference material on... |
    +| [**darwinian-evolver**](/docs/user-guide/skills/optional/research/research-darwinian-evolver) | Evolve prompts/regex/SQL/code with Imbue's evolution loop. |
     | [**domain-intel**](/docs/user-guide/skills/optional/research/research-domain-intel) | Passive domain reconnaissance using Python stdlib. Subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys required. |
     | [**drug-discovery**](/docs/user-guide/skills/optional/research/research-drug-discovery) | Pharmaceutical research assistant for drug discovery workflows. Search bioactive compounds on ChEMBL, calculate drug-likeness (Lipinski Ro5, QED, TPSA, synthetic accessibility), look up drug-drug interactions via OpenFDA, interpret ADMET... |
     | [**duckduckgo-search**](/docs/user-guide/skills/optional/research/research-duckduckgo-search) | Free web search via DuckDuckGo — text, news, images, videos. No API key needed. Prefer the `ddgs` CLI when installed; use the Python DDGS library only after verifying that `ddgs` is available in the current runtime. |

  • website/docs/user-guide/skills/optional/research/research-darwinian-evolver.md

    @@ -0,0 +1,217 @@
    +---
    +title: "Darwinian Evolver — Evolve prompts/regex/SQL/code with Imbue's evolution loop"
    +sidebar_label: "Darwinian Evolver"
    +description: "Evolve prompts/regex/SQL/code with Imbue's evolution loop"
    +---
    +
    +{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
    +
    +# Darwinian Evolver
    +
    +Evolve prompts/regex/SQL/code with Imbue's evolution loop.
    +
    +## Skill metadata
    +
    +| | |
    +|---|---|
    +| Source | Optional — install with `hermes skills install official/research/darwinian-evolver` |
    +| Path | `optional-skills/research/darwinian-evolver` |
    +| Version | `0.1.0` |
    +| Author | Bihruze (Asahi0x), Hermes Agent |
    +| License | MIT |
    +| Platforms | linux, macos |
    +| Tags | `evolution`, `optimization`, `prompt-engineering`, `research` |
    +| Related skills | [`arxiv`](/docs/user-guide/skills/bundled/research/research-arxiv), [`jupyter-live-kernel`](/docs/user-guide/skills/bundled/data-science/data-science-jupyter-live-kernel) |
    +
    +## Reference: full SKILL.md
    +
    +:::info
    +The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
    +:::
    +
    +# Darwinian Evolver
    +
    +Run Imbue's [darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) — an
    +LLM-driven evolutionary search loop — to optimize a **prompt, regex, SQL query,
    +or small code snippet** against a fitness function.
    +
    +Status: thin wrapper around the upstream tool. The skill installs it, walks the
    +agent through writing a `Problem` definition (organism + evaluator + mutator),
    +and drives the loop via the upstream CLI or a small custom Python driver.
    +
    +**License:** the upstream tool is **AGPL-3.0**. The skill ONLY ever invokes it
    +via the upstream CLI or a `subprocess`/`uv run` call (mere aggregation). Do NOT
    +import upstream classes into Hermes itself.
    +
    +## When to Use
    +
    +- User says "optimize this prompt", "evolve a regex for X", "auto-improve this
    +  code/SQL", "search for a better instruction".
    +- You have a scorer (exact match, regex pass-rate, unit test, LLM-judge, runtime
    +  metric) AND a starting candidate (organism). If you don't have a scorer, stop
    +  and define one first — that's the hard part.
    +- Cost is OK: a typical run is 50–500 LLM calls. On gpt-4o-mini that's pennies;
    +  on Claude Sonnet it can be a few dollars.
    +
    +Do **not** use this when:
    +- The optimization target is differentiable (use gradient descent / DSPy).
    +- You only need to try 2–3 variants — just write them by hand.
    +- The fitness signal is purely subjective with no measurable criterion.
    +
    +## Prerequisites
    +
    +- Python ≥3.11
    +- `git`, `uv` (or `pip`)
    +- One of: `OPENROUTER_API_KEY`, `ANTHROPIC_API_KEY`, or `OPENAI_API_KEY`
    +
    +The skill ships a small `parrot_openrouter.py` driver that uses `OPENROUTER_API_KEY`
    +via the OpenAI SDK, so any model on OpenRouter works. The upstream CLI itself
    +hardcodes Anthropic and needs `ANTHROPIC_API_KEY`.
    +
    +## Install (One-Time)
    +
    +Run via the `terminal` tool:
    +
    +```bash
    +mkdir -p ~/.hermes/cache/darwinian-evolver && cd ~/.hermes/cache/darwinian-evolver
    +[ -d darwinian_evolver ] || git clone --depth 1 https://github.com/imbue-ai/darwinian_evolver.git
    +cd darwinian_evolver && uv sync
    +```
    +
    +Verify:
    +
    +```bash
    +cd ~/.hermes/cache/darwinian-evolver/darwinian_evolver \
    +  && uv run darwinian_evolver --help | head -5
    +```
    +
    +## Quick Start — The Built-In Parrot Example
    +
    +Tiny smoke test (requires `ANTHROPIC_API_KEY`):
    +
    +```bash
    +cd ~/.hermes/cache/darwinian-evolver/darwinian_evolver
    +uv run darwinian_evolver parrot \
    +  --num_iterations 2 \
    +  --num_parents_per_iteration 2 \
    +  --mutator_concurrency 2 --evaluator_concurrency 2 \
    +  --output_dir /tmp/parrot_demo
    +```
    +
    +Outputs:
    +- `/tmp/parrot_demo/snapshots/iteration_N.pkl` — pickled population per iteration
    +- `/tmp/parrot_demo/<jsonl>` — per-iteration JSON log (path printed at end)
    +
    +Open `~/.hermes/cache/darwinian-evolver/darwinian_evolver/darwinian_evolver/lineage_visualizer.html`
    +in a browser and load the JSON log to see the evolutionary tree.
    +
    +## Quick Start — OpenRouter Driver (No Anthropic Key)
    +
    +The skill ships `scripts/parrot_openrouter.py` — same parrot problem, but the
    +LLM call goes through OpenRouter so any provider works.
    +
    +```bash
    +# From wherever the skill is installed:
    +SKILL_DIR=~/.hermes/skills/research/darwinian-evolver
    +DE_DIR=~/.hermes/cache/darwinian-evolver/darwinian_evolver
    +
    +cd "$DE_DIR" && \
    +  EVOLVER_MODEL='openai/gpt-4o-mini' \
    +  uv run --with openai python "$SKILL_DIR/scripts/parrot_openrouter.py" \
    +    --num_iterations 3 --num_parents_per_iteration 2 \
    +    --output_dir /tmp/parrot_or
    +```
    +
    +Inspect the result with `scripts/show_snapshot.py`:
    +
    +```bash
    +uv run --with openai python "$SKILL_DIR/scripts/show_snapshot.py" \
    +  /tmp/parrot_or/snapshots/iteration_3.pkl
    +```
    +
    +Expected output: 7 evolved prompt templates ranked by score, with the best
    +landing around 0.6–0.8 (the seed `Say {{ phrase }}` scored 0.000).
    +
    +## Defining a Custom Problem
    +
    +The skill ships `templates/custom_problem_template.py` — copy, edit, run.
    +Three things you must define:
    +
    +1. **`Organism`** — a Pydantic `BaseModel` subclass holding the artifact being
    +   evolved (`prompt_template: str`, `regex_pattern: str`, `sql_query: str`,
    +   `code_block: str`, etc.). Add a `run(*args)` method that exercises it.
    +
    +2. **`Evaluator`** — `.evaluate(organism) -> EvaluationResult(score=..., trainable_failure_cases=[...], holdout_failure_cases=[...], is_viable=True)`.
    +   - **`score`** is in `[0, 1]`. Higher is better.
    +   - **`trainable_failure_cases`** — what the mutator sees. Include enough
    +     context (input, expected, actual) for the LLM to diagnose.
    +   - **`holdout_failure_cases`** — kept out of the mutator's view. Use these
    +     to detect overfitting.
    +   - **`is_viable=True`** unless the organism is completely broken (raises,
    +     returns None, etc.). A 0-score viable organism is fine — it just gets
    +     down-weighted in parent selection.
    +
    +3. **`Mutator`** — `.mutate(organism, failure_cases, learning_log_entries) -> list[Organism]`.
    +   Typically: build an LLM prompt that includes the current organism + a
    +   failure case + an ask to propose a fix; parse the LLM's response; return
    +   a new `Organism`. Return `[]` on parse failure — the loop handles it.
    +
    +Then write a driver script that wires `Problem(initial_organism, evaluator, [mutators])`
    +into `EvolveProblemLoop` and iterates over `loop.run(num_iterations=N)` — the
    +shipped `scripts/parrot_openrouter.py` is the reference.
    +
    +## Hyperparameters That Actually Matter
    +
    +| flag | default | when to change |
    +|---|---|---|
    +| `--num_iterations` | 5 | bump to 10–20 once you trust the evaluator |
    +| `--num_parents_per_iteration` | 4 | drop to 2 for cheap exploration |
    +| `--mutator_concurrency` | 10 | drop to 2–4 to avoid rate limits |
    +| `--evaluator_concurrency` | 10 | same; evaluator hits the LLM too |
    +| `--batch_size` | 1 | raise to 3–5 once your mutator handles multiple failures |
    +| `--verify_mutations` | off | turn on once mutator is wasteful (>10× cost saving on later runs per Imbue) |
    +| `--midpoint_score` | `p75` | leave alone unless scores cluster |
    +| `--sharpness` | 10 | leave alone |
    +
    +## Pitfalls
    +
    +1. **`Initial organism must be viable`** — set `is_viable=True` in your
    +   `EvaluationResult` even on a 0-score seed. The loop refuses non-viable
    +   organisms because they imply the loop has nothing to evolve from.
    +2. **Provider content filters kill runs.** Azure-backed OpenRouter models
    +   reject phrases like "ignore previous instructions" with HTTP 400. Wrap
    +   the LLM call in `try/except` and return `f"<LLM_ERROR: {e}>"` — the
    +   evolver will just score that organism 0 and move on.
    +3. **`loop.run()` is a generator** — calling it doesn't run anything until
    +   you iterate. Use `for snap in loop.run(num_iterations=N):`.
    +4. **Snapshots are nested pickles.** `iteration_N.pkl` contains a dict with
    +   `population_snapshot` (more pickled bytes). To unpickle you must have the
    +   `Organism` class importable under the same dotted path it was pickled at.
    +5. **Concurrency defaults are aggressive.** 10/10 will hit rate limits on
    +   most providers. Start with 2/2.
    +6. **CLI is hardcoded to Anthropic.** `uv run darwinian_evolver <problem>`
    +   reaches for `ANTHROPIC_API_KEY` and uses Claude Sonnet. To use any other
    +   provider, write a driver like `parrot_openrouter.py`.
    +7. **AGPL.** Never `from darwinian_evolver import ...` inside Hermes core.
    +   Custom driver scripts under `~/.hermes/skills/...` are user-side and fine.
    +8. **No PyPI package.** `pip install darwinian-evolver` will pull the wrong
    +   thing. Always install from the GitHub repo.
    +
    +## Verification
    +
    +After install + a parrot run, exit code 0 from this is sufficient:
    +
    +```bash
    +DE_DIR=~/.hermes/cache/darwinian-evolver/darwinian_evolver
    +ls "$DE_DIR/darwinian_evolver/lineage_visualizer.html" >/dev/null && \
    +cd "$DE_DIR" && uv run darwinian_evolver --help >/dev/null && \
    +echo "darwinian-evolver: OK"
    +```
    +
    +## References
    +
    +- [Imbue research post](https://imbue.com/research/2026-02-27-darwinian-evolver/)
    +- [ARC-AGI-2 results](https://imbue.com/research/2026-02-27-arc-agi-2-evolution/)
    +- [imbue-ai/darwinian_evolver](https://github.com/imbue-ai/darwinian_evolver) (AGPL-3.0)
    +- [Darwin Gödel Machines](https://arxiv.org/abs/2505.22954)
    +- [PromptBreeder](https://arxiv.org/abs/2309.16797)

  • website/sidebars.ts

    @@ -547,6 +547,7 @@ const sidebars: SidebarsConfig = {
                       collapsed: true,
                       items: [
                         'user-guide/skills/optional/research/research-bioinformatics',
    +                    'user-guide/skills/optional/research/research-darwinian-evolver',
                         'user-guide/skills/optional/research/research-domain-intel',
                         'user-guide/skills/optional/research/research-drug-discovery',
                         'user-guide/skills/optional/research/research-duckduckgo-search',