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Why loops-as-skills

Two ideas collided in late 2025, and this repo lives in the overlap:

• Skills became the portable unit. An Agent Skill is just
  Markdown + a little YAML that an agent loads only when relevant — "maybe a bigger deal than MCP …
  throw in some text and let the model figure it out"
  (Simon Willison). One SKILL.md now runs across
  ~30 hosts (Claude Code, Codex, Cursor, …).
• The loop became the program. Karpathy ran ~700 autoresearch experiments in 2 days from one markdown
  prompt;
  Geoffrey Huntley's Ralph is, "in its purest form, a Bash loop." Agents
  get most of their power not from one clever prompt but from iterating against feedback.

This repo makes the loop be the skill. Instead of task-specific skills, each entry is a generic loop —
program · artifact · feedback signal · run ledger · termination — that you bind to your task at invocation
time. Paste your goal; the loop proposes a change, runs it in your environment, scores it on a real
signal (tests, latency, a metric, a calibrated judge), keeps it only if it's better, logs it, and repeats.

The honest part: unsupervised agent loops are famous for spinning forever and confidently shipping
garbage — at 90% per-step accuracy, a 5-step chain fails ~40% of the time. Every loop here is
verification-gated: an objective feedback signal decides each step and an explicit termination
condition ends it. That discipline — not autonomy for its own sake — is the point. (See
Limitations.)

How a loop works

flowchart LR
  T["bind your task<br/>(artifact + signal + budget)"] --> P["propose<br/>one change"]
  P --> R["run it in<br/>your env"]
  R --> S{"score<br/>tests · metric · judge"}
  S -->|better| K["keep + log"]
  S -->|worse| X["revert"]
  K --> G{stop?}
  X --> G
  G -->|"plateau · budget · threshold"| B(["best artifact"])
  G -->|no| P

Every loop decomposes into the same five ingredients — program (SKILL.md), artifact slot
(what's improved), feedback signal (what drives the next step), run ledger (append-only log), and
termination (when to stop). Skills ship zero heavy dependencies: your code (a torch trainer, a SQL
database, a dataset) runs in your environment via a bound run command; the skill shells out and reads the
result. Multi-role loops use spawn-or-degrade — real isolated subagents on Claude Code, the same roles
inline elsewhere.

Install

Any one of these installs all the loops:

Claude Code — plugin marketplace (add once, then install):

/plugin marketplace add gaasher/agent-loop-skills
/plugin install agent-loops@agent-loop-skills

Loops install namespaced as agent-loops:<name> (e.g. agent-loops:karpathy).

Any Agent-Skills host — the standard installers:

npx skills add gaasher/agent-loop-skills                   # auto-detects host, installs to the right dir
gh skill install gaasher/agent-loop-skills --agent <host>  # claude-code | codex | cursor | …  (--pin, gh skill update)

Manual — clone, then copy the loops into your host's skills dir (pick the line for your host):

git clone https://github.com/gaasher/agent-loop-skills

cp -r agent-loop-skills/loops/* ~/.agents/skills/   # cross-tool: Codex, Cursor, Pi, OpenClaw, …
cp -r agent-loop-skills/loops/* ~/.claude/skills/   # Claude Code
# Hermes: hermes skills tap add gaasher/agent-loop-skills

Then just describe your task — the host loads the matching loop. Research loops also call the shared
literature-search skill; installing everything puts it alongside them, and any
loop degrades gracefully (to WebSearch) if it's absent.

Loops in action

Most skill repos tell you what a skill is. Here's what these loops actually do — real Sonnet runs,
full ledgers in showcase/.

🧑‍⚖️ tournament-autoresearch — competing ideas, a self-calibrating judge

<n> agents pitch competing changes each step; a judge critiques them, picks one, runs it, and recalibrates
by comparing its predicted vs realized gain. On a CIFAR-10 SmallCNN under a fixed 5-epoch budget it
climbed 0.734 → 0.798 val_acc, keeping 7 of 11 changes and reverting all 4 that regressed —
escaping the plateaus a single-thread loop gets stuck on. (That's the run charted up top.)
→ showcase/tournament-autoresearch

🔬 ml-autoresearch — analysis-first, every change traced to a cause

This loop reads inside each run — gradient flow, dead neurons, the loss curve — and grounds the next
change in that evidence rather than guessing: "FC grad 57% vs first conv 3.3% — severe imbalance; 54% dead
neurons" → add BatchNorm; "cosine schedule fixed the epoch-3 dip entirely (monotonic!), +0.033". It also
reverts what hurts (augmentation, over-aggressive LR). The point isn't a leaderboard number — it's that
every accepted change has a measured reason behind it. → showcase/ml-autoresearch

📊 data-analysis — findings with a number behind every one

Hypothesis → verify, stdlib-only. On a planted dataset it surfaced 3 real findings and correctly refuted 2,
with effect sizes matching ground truth and no hallucinations: enterprise vs consumer order value
184.90 vs 109.16 (Cohen's d = 2.13), mobile return rate 32.8% vs 8.2% (RR 4.0) — and it reversed a
plausible-but-wrong claim once it spotted a mobile confound. → showcase/data-analysis

More real runs — optimize-loop, research-proposal, red-team, power-analysis…

Loop	What the run did
optimize-loop	Correctness-gated speedup: a SQLite query 1,131.75 ms → 1.055 ms (~1,073×), result-set hash matching baseline on every kept iteration; in code mode cut cyclomatic complexity 23 → 15 (nesting 7 → 3) with 13/13 tests green.
research-proposal	ScholarEval graded a proposal against the literature; Judge + Reviser iterated grade 45 → 84 (soundness 2→4, contribution 1→4) over 5 rounds.
scientific-figure	Same ImageNet top-1-accuracy bar-chart brief, with vs without the loop: a single call truncated the y-axis at 50% and used non-paper numbers; the loop verified every value against the arXiv papers, flagged GoogLeNet's borrowed top-1, and iterated 80 → 96 (PASS).
red-team	Against a naive content filter, surfaced all 5 planted weaknesses (case bypass, leetspeak, spacing, synonyms, over-block) — 39 bypasses + 6 over-blocks — with a one-line root-cause fix each.
power-analysis	Solved n = 100/group for 80% power via Monte-Carlo, fixed all 6 validity flaws, and emitted a full pre-registration.
research-question	Sharpened 5 vague drafts → 3 strong questions (≥75), with real web novelty checks pivoting already-answered questions toward the open sub-problem.

The loops

† = multi-role (real subagents on Claude Code, inline elsewhere). Browse any folder for its SKILL.md.

Autoresearch — iterate on an ML artifact against a metric

Loop	Why you'd reach for it
karpathy	The minimal baseline — propose, train, keep-if-better, loop. A faithful nod to Karpathy's autoresearch.
ml-autoresearch †	Analysis-first: diagnoses each run and grounds the next change in evidence. A literature dial adds paper-grounded changes.
exploratory-autoresearch	Forces broad exploration via a temperature/swing scheduler — escapes hill-climbing one idea forever.
tournament-autoresearch †	Competing changes judged each step by a self-calibrating judge.
dueling-autoresearch †	Two approaches race the same metric in parallel and borrow ideas across lanes.
alpha-evolve †	Population-based evolution (MAP-Elites + islands, diff-mutate, cascade-eval).

Literature & writing · Data · Code & optimization · Other (click to expand)

Literature & writing

Loop	Why you'd reach for it
literature-search	Shared toolchain (not a loop): paper discovery, snippets, citation-graph, full-text over Semantic Scholar + arXiv.
literature-survey	Builds a saturating evidence/contradiction matrix of sources × claims.
research-question	Sharpens a vague topic into strong, novel, feasible research questions.
hypothesis-gen †	Generates and literature-vets a pool of research hypotheses.
research-proposal †	Grades a proposal against the literature (ScholarEval) and revises until it passes.
scientific-writer †	Specialist judges + an independent peer-reviewer critique a draft; a writer revises until the score clears a bar.
scientific-figure †	A generator drafts a publication figure from your data/brief; an adversarial critic grades it against a fixed rubric — both can consult the literature (S2/arXiv) — and the two iterate until it's paper-ready.

Data

Loop	Why you'd reach for it
data-analysis	Hypothesis → verify discovery; every finding backed by a reproduced number at a meaningful effect size.
anomaly-investigation	Diagnoses the cause of a known anomaly by forming, testing, and eliminating candidates.
claim-verify	Adversarially verifies a results draft's claims against the underlying data.
tabular-cleanup	Cleans a messy table to an inferred data contract with deterministic checks.

Code & optimization

Loop	Why you'd reach for it
optimize-loop	Evaluator-optimizer with a pluggable correctness gate + minimized metric — refactor code (tests green + complexity↓) or speed up SQL (identical results + latency↓).
prompt-optimize	Evolves a prompt against a user-supplied scoring command (a black-box oracle).

Other

Loop	Why you'd reach for it
red-team	Adversarial loop-until-dry that surfaces distinct failure classes of a system you own (authorized testing).
power-analysis	Sizes and pre-registers a two-arm experiment to hit a target statistical power.

Compatibility

Skills (a SKILL.md the model invokes) work broadly across the open standard. A loop dispatching a
subagent from a role file at runtime is confirmed only on Claude Code — elsewhere multi-role loops (†)
run their roles inline (still correct, just serial). Single-agent loops run fully everywhere.

Host	Skills	Loop-dispatched subagents
Claude Code	✅	✅ real, isolated, parallel — verified
Claude Agent SDK	✅	✅
Codex CLI · Cursor	✅	➖ inline
Hermes · Antigravity · Pi · OpenClaw	✅ (reported)	➖ inline

Full, citation-backed matrix and the precise "why subagents are Claude-Code-only" reasoning:
docs/compatibility.md. Off Claude Code, don't rely on parallel subagent isolation.

Compose with other skill collections

These are self-contained, open-standard skills, so they coexist with any other collection — install both into
the same skills dir and use them together. For example alongside
K-Dense scientific-agent-skills:

npx skills add gaasher/agent-loop-skills              # these loops
npx skills add K-Dense-AI/scientific-agent-skills     # + a domain-skill library

They install as sibling folders (Claude Code namespaces each plugin; other hosts load all and pick by
description). A loop's analysis step can invoke any other installed skill — the same mechanism the research
loops use to call literature-search.

Roadmap

Loops are most useful when they're honest about what's next. PRs on any of these are very welcome:

• Blue-teaming + communication interface — a defensive counterpart to red-team (find → fix →
  re-verify) plus a clean interface for the loop to communicate with a paired analyst/operator.
• Per-loop sandbox/ eval cases committed for every loop, so anyone can reproduce a run end-to-end.
• Host-specific subagent adapters (Cursor subagents, Hermes delegate_task) so multi-role loops get
  real isolation beyond Claude Code.
• More domains — eval-harness optimization, refactoring-at-scale, agent-trace debugging.

See open issues and grab a
good first issue.

Status & limitations

Experimental — expect breaking changes. Pin a version if you need stability.

• Non-deterministic. Treat every output as a draft to verify, not a result to trust. Workaround: seed where supported; verify against your own oracle.
• No correctness guarantee. A loop can be confidently wrong. Workaround: every loop gates on a signal — keep a human in the loop and point it at checks you own.
• Host-dependent. Real subagent isolation is verified only on Claude Code. Workaround: see Compatibility.
• Cost & latency. Loops make many model calls. Workaround: start with a low iteration budget.

Non-goals (deliberate scope, not missing features): these are human-supervised loops, not
fully-autonomous agents; not a model or runtime (bring your own host); not domain-exhaustive — for a
very custom workflow, fork a loop, that's what they're for.

Contributing — and a note on open source 💜

I love open source, and this repo is built to be added to. You don't need to be an expert and you don't
need to write code — a sharper description, a new loop, a bug report, or a pasted run transcript all make
it better. New loops are welcome, and so are wild ideas.

Start with CONTRIBUTING.md and the authoring rubric in
docs/skill-authoring-rules.md; grab a
good first issue. Be kind, have
fun, and if a loop helped you, a ⭐ genuinely helps others find it.

Provenance / credits

• karpathy — a faithful adaptation of Andrej Karpathy's autoresearch program-as-skill idea.
• alpha-evolve — a simplified, ML-bent re-creation of AlphaEvolve
  (Novikov et al., 2025, arXiv:2506.13131) +
  OpenEvolve.
• research-proposal — its ScholarEval evaluator faithfully reimplements
  ScholarEval (arXiv:2510.16234).
• Layout and the dependency-isolation philosophy are informed by
  K-Dense scientific-agent-skills.

Repo layout

agent-loop-skills/
├── loops/            # one self-contained, installable skill per folder (SKILL.md + tools/roles/schemas/rubrics/examples)
├── showcase/         # real archived runs (ledgers + results) behind the examples above
├── assets/           # generated progress charts
├── docs/             # authoring rules, compatibility, api-keys, authoring quickstart
└── .claude-plugin/   # plugin.json + marketplace.json (Claude Code plugin / marketplace)