agent-behavior

Standards for defining and evaluating agent behavior

The meta layer behind AI skills. Persistent behavioral overlays that make expected agent conduct explicit across changing tasks and capabilities.

Actonomy: A taxonomy for describing and analyzing agent behavior. Gao et al., NeurIPS 2026 — How to Interpret Agent Behavior. Official codebook, datasets, and trace analysis tools (ACT*ONOMY / Act-onomy).

Studying the gap between what agents know and when they act on it.

Give your coding agent a short break after real work, then measure what happens next. Universal agent skill & experiment harness.

Behavior-management plugin for DeepSeek Harness: tool-call discipline prompt section, failure-triggered parallelism convergence (pool drops to 1, auto-restores), consecutive-failure user intervention. Complements dsh-token-optimizer; works standalone.

⚡ High-frequency (~60Hz) in-browser behavior tree execution engine for autonomous AI agents. Reactive priority interrupts, safety guardrails & deterministic action replay over MCP.

The senior dev, unbundled — ten opinionated skills that make an AI coding agent push back, verify, attack its own code, and finish like a senior engineer.

Citable short technical notes & observations (Zenodo-archived). AI accountability, agent behavior, software engineering.

RL-style eval measuring intent/action divergence in frontier agents: model acknowledges a correction, then acts on the stale value anyway. 3 scenarios, 655 trials on claude-haiku-4-5, Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro Preview.

High-performance routing engine that selects the best agent skill for a task and emits structured handoff decisions.

pua — 挫败驱动的行为修正教练 skill：绩效文化话术驱动 agent 穷尽方案、拿证据闭环；失败升级、方法论路由、门控循环。平静首请求不触发，遥测默认关闭

text systems, behavioral contracts, AI protocols, and small tools for keeping AI-assisted work operational. I built these tools to reduce drift, force useful decisions, and keep messy AI-assisted work usable.

Toy 7. An elimination-filter landscape applying two structural constraints simultaneously to map which objective classes can persist under sustained optimization pressure — and which cannot. Includes a four-stage scenario engine and open-question frontier. Companion simulation for The Shape of What Does Not End — Series 2, Part 4.

Agent authority guardrails for Claude Code & Grok — stop yield-back; drive reversible work yourself.

Composable behavior mutators for agent prompts - priority, routing, batching, topology

Canonical home for AI Behavior Science research and the Founding Territory Paper

Toy 4. An AI-powered goal-chain classifier and invariance stress tester. Enter any goal, trace the instrumental chain to its terminal type, then probe whether the classification survives symbolic, blind, independent, and adversarial re-examination. Companion simulation for The Invariant Drive — Series 2, Part 1 of the Architecture of Thriving.

Lint AgentSkill folders before install, sync or release: YAML frontmatter, name vs folder, description length, dev-tree paths, model-run telemetry, README standard. CLI, /skill-lint command and GitHub Action.

Execution layer for skill-dispatcher — runs multi-phase agent chains end-to-end with per-step telemetry and chain_id correlation