agent-loop-skills

Loop until it's better — drop-in agentic loops, packaged as open-standard Agent Skills.

Autoresearch · scientific writing · data analysis · code/SQL/prompt optimization · red-teaming — each a generic, reusable loop you bind to your own task at invocation time, that iterates against a real signal until the work is actually better.

A real run. The tournament-autoresearch loop on a CIFAR-10 model under a fixed 5-epoch budget — competing agents propose a change each step, a self-calibrating judge keeps the winners (green) and discards the regressions (gray): 0.734 → 0.798 val_acc, hands-off, 7 of 11 kept. Full ledger: showcase/tournament-autoresearch. Far from SOTA by design — a deliberately tiny CNN at 5 epochs on a laptop GPU (Apple MPS). The demo is the loop's decision-making, not the absolute accuracy.

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

The loops

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