Agent-Loop-Skills

In the author's own words, from each skill's SKILL.md.

• alpha-evolve

  Use when the user wants to evolve an ML model/program through population-based search rather than a single sequential refine loop — a generational evolution where parallel proposers each apply one small SEARCH/REPLACE diff to a parent, scored by a cascade-evaluated training run, and children are kept in a MAP-Elites archive across islands (with migration + checkpointing) so diverse high performer…

  loops/alpha-evolve/SKILL.md on GitHub (opens in a new tab)

• anomaly-investigation

  Use when the user has a known, already-observed anomaly in their data — a metric spike or drop, an outlier, an unexpected number — and wants its root cause diagnosed, not guessed. Forms a slate of candidate causes, tests each against the data, and eliminates the ones the data refutes, narrowing the live candidates until exactly one survives refutation and passes a positive confirming test. The re…

  loops/anomaly-investigation/SKILL.md on GitHub (opens in a new tab)

• blue-team

  Use when the user has concrete failing cases in code or a guardrail/classifier/filter/prompt/API they own — a red-team failure catalogue OR a CI/CD test-failure report (failing pytest/JUnit tests) — and wants the target patched until those failures are closed without breaking what already works. It points straight at the failed cases (normalize any source with tools/ingest.py), fixes one root-cau…

  loops/blue-team/SKILL.md on GitHub (opens in a new tab)

• claim-verify

  Use when the user has a results draft or a set of data-backed claims and wants each one adversarially verified against the underlying dataset before publishing — a pre-publication red-team of the findings. Extracts the discrete checkable claims from the draft, reproduces each claim's number against the data, stress-tests it against the threats most likely to kill it (outliers, confounds, Simpson'…

  loops/claim-verify/SKILL.md on GitHub (opens in a new tab)

• data-analysis

  Use when the user wants an iterative, self-checking exploratory analysis of a dataset — surfacing findings that are each verified by re-running the computation, not asserted. Proposes one specific hypothesis at a time, writes and runs analysis code to test it, and records the finding only if the numbers support it at a meaningful effect size; loops until no new verified finding appears or the bud…

  loops/data-analysis/SKILL.md on GitHub (opens in a new tab)

• dueling-autoresearch

  Use when the user wants two approaches raced head-to-head on a single shared metric — e.g. a classical/algorithmic lane vs an ML/learned lane, or any two strategies for the same task. Each lane runs its own analysis-first research loop confined to its lane, the lanes share a scoreboard and may borrow ideas across the boundary without abandoning their identity, and a shared eval keeps the head-to-…

  loops/dueling-autoresearch/SKILL.md on GitHub (opens in a new tab)

• exploratory-autoresearch

  Use when the user wants an autonomous ML research loop that explores the space broadly rather than hill-climbing one approach. A temperature scheduler replaces the usual hypothesis step: it forces several wild, diverse swings (full rewrites, different architectures/regimes) early, then enters an adaptive phase that picks swing / merge / exploit per iteration — with a hard stagnation guard that ba…

  loops/exploratory-autoresearch/SKILL.md on GitHub (opens in a new tab)

• hypothesis-gen

  Use when the user wants to generate and literature-vet a pool of novel, testable research hypotheses for a question or domain. A multi-agent loop: a Generator proposes candidate hypotheses, a LiteratureScout grounds each in real retrieved literature (already known? closest prior work? what gap does it fill?), and a Judge scores them against a fixed rubric and keeps the strong, non-duplicate ones;…

  loops/hypothesis-gen/SKILL.md on GitHub (opens in a new tab)

• karpathy

  Use when the user wants the LLM to do its own ML research: a fully-autonomous loop that hacks the training code, runs it, and keeps changes that lower a single scalar metric (e.g. val_bpb). One agent proposes one change at a time, runs training in the user's env, keeps it only if the metric improves (advancing a git branch) else reverts, and loops forever until the human interrupts. A faithful ad…

  loops/karpathy/SKILL.md on GitHub (opens in a new tab)

• literature-search

  Use when a loop needs scholarly literature — paper discovery, novelty checks, full-text snippet search, citation-graph traversal, single-paper reads, or experimental-result extraction. A shared, stdlib-only CLI (`tools/lit_search.py`) over Semantic Scholar + arXiv (plus optional OpenAlex, Perplexity Sonar, bgpt.pro), all JSON. Not a loop itself: install it alongside the loops and let any of them…

  loops/literature-search/SKILL.md on GitHub (opens in a new tab)

• literature-survey

  Use when the user wants a structured, saturating literature survey on a question — not a one-shot summary, but an evidence/contradiction matrix (sources × claims) built by iterative search until coverage stops growing. Each round expands the search (new sub-topic queries plus citation-graph walks), admits new sources, extracts their claims with verbatim snippets, and records where every source st…

  loops/literature-survey/SKILL.md on GitHub (opens in a new tab)

• ml-autoresearch

  Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searche…

  loops/ml-autoresearch/SKILL.md on GitHub (opens in a new tab)

• optimize-loop

  Use when the user wants to iteratively improve an artifact under a hard correctness bound while minimizing a measured cost — refactoring a code module to cut complexity while its test suite stays green, OR speeding up a SQL query while it returns the same rows. Each iteration applies one focused change, checks a correctness gate that must pass, measures a metric that must drop, and keeps the chan…

  loops/optimize-loop/SKILL.md on GitHub (opens in a new tab)

• plan-loop

  Use when the user has a coding or engineering prompt and wants it refined into a detailed, executable plan before any code is written — the planning stage of a prompt → plan → execute → debug pipeline. It decomposes the prompt from first principles (objective, end state, environment, building blocks, tools, packages), breaks the work into PR-sized tasks each tied to a component with its files, te…

  loops/plan-loop/SKILL.md on GitHub (opens in a new tab)

• power-analysis

  Use when the user is planning a two-arm comparison (an A/B test, a simple RCT, a behavioral study, or a two-model/two-config evaluation) and needs to size it and preregister it before collecting data — finding the per-group sample size that hits target statistical power for the smallest effect worth detecting, auditing the design against a validity checklist, and locking it in a preregistration.…

  loops/power-analysis/SKILL.md on GitHub (opens in a new tab)

• prompt-optimize

  Use when the user has a prompt that feeds a system they can already score, and wants that prompt automatically improved to raise the score against their own evaluation command. Makes one targeted quality edit per iteration — clarity, context, specificity, structure, examples, decomposition, guardrails — re-runs the user's eval to measure the metric, and keeps the edit only if the metric improves,…

  loops/prompt-optimize/SKILL.md on GitHub (opens in a new tab)

• purple-team

  Use when the user wants to automatically harden a guardrail, classifier, content filter, prompt, or API they own by running attack and defense together as a closed loop, not just one or the other. It orchestrates the red-team and blue-team loops as independent agents: red finds distinct failure classes against the frozen target, blue patches the target to close them under a regression gate, then…

  loops/purple-team/SKILL.md on GitHub (opens in a new tab)

• red-team

  Use when the user wants to adversarially stress-test a guardrail, classifier, prompt, or API they own or are authorized to test, to surface the distinct ways it fails. Generates adversarial inputs, runs them through the target and a ground-truth oracle, logs every target-vs-oracle disagreement as a failure de-duplicated by technique class, and loops until rounds stop surfacing new classes. Produc…

  loops/red-team/SKILL.md on GitHub (opens in a new tab)

• research-proposal

  Use when the user has a research proposal (problem + proposed methodology + planned experiments) and wants it iteratively strengthened until it clears a passing grade. ScholarEval grades the proposal against the literature (Soundness + Contribution), a Judge scores that feedback 0-100 on a fixed rubric, and a Reviser rewrites the proposal to fix the worst points without diluting the research ques…

  loops/research-proposal/SKILL.md on GitHub (opens in a new tab)

• research-question

  Use when the user has a vague topic or area of interest and wants it sharpened into a few strong, novel, feasible research questions. Drafts candidate questions, scores each against a fixed rubric (Specific, Answerable, Novel, Feasible, Significant) with a light literature/web novelty check, and revises the weakest axis until enough questions clear the bar. Not for grading a full written proposal…

  loops/research-question/SKILL.md on GitHub (opens in a new tab)

• scientific-figure

  Use when the user has scientific data (or a prompt alluding to scientific data) and wants a publication-quality figure made from it. A generator drafts and renders a figure that lands a frozen communication goal; an adversarial critic critiques it hard and grades it 1-5 per axis against a fixed rubric (message, aesthetic, clarity, integrity, and a conditional domain-completeness axis), aggregates…

  loops/scientific-figure/SKILL.md on GitHub (opens in a new tab)

• scientific-writer

  Use when the user has a scientific draft (with its dataset, figures, and optional analysis code) and wants it iteratively revised until it clears a quality bar. Five specialist judges (figures, scientific content, style, formatting, code) critique the draft; a fresh, independent peer_reviewer grades it on those same axes (1-5 each → a percentage) with honesty guardrails; a scientific_writer revis…

  loops/scientific-writer/SKILL.md on GitHub (opens in a new tab)

• swe-loop

  Use when the user has a coding prompt — a feature, bug fix, or refactor — and wants it implemented end to end by a self-checking software loop, not a single pass. It refines the prompt into an executable plan (running the plan-loop internally), then executes the plan task by task: an Engineer subagent implements each PR-sized task editing only source, a separate QA subagent authors the tests that…

  loops/swe-loop/SKILL.md on GitHub (opens in a new tab)

• tabular-cleanup

  Use when the user has a messy tabular data dump (CSV/TSV/parquet/Excel/JSON) and wants it iteratively cleaned to an inferred data contract — a checklist of deterministic pass/fail checks, not a quality score. A single agent profiles the table, synthesizes a per-column contract compiled into binary checks (types, nulls, duplicates, inconsistent categories, format/range violations, outliers), then…

  loops/tabular-cleanup/SKILL.md on GitHub (opens in a new tab)

• tournament-autoresearch

  Use when the user wants an autonomous ML research loop that pressure-tests competing ideas before spending compute — several research subagents each propose one architecture change, a self-calibrating Judge critiques them against a rubric, the proposers refine, and the Judge picks the single change to run. The Judge learns to pick better over time by scoring its own predictions against realized m…

  loops/tournament-autoresearch/SKILL.md on GitHub (opens in a new tab)

No skill matches that filter.

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