gaasher

gaasher/Agent-Loop-Skills

Plugin Claude Code

Plugin marketplace listing 1 plugin: agent-loops.

gaasher/Agent-Loop-Skills

Plugin Claude Code

Drop-in agentic loop skills — autoresearch, literature & scientific writing, data analysis, code/SQL/prompt optimization, red-teaming, and power analysis. Each loop binds to your own task at invocation time.

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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. valbpb). 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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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/litsearch.py) over Semantic Scholar + arXiv (plus optional OpenAlex, Perplexity Sonar, bgpt.pro), all…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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 on/off dial adds scientific-literature…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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)…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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…

gaasher/Agent-Loop-Skills

Skill Claude CodeCodex

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 peerreviewer grades it on those same…