Results for “agent-behavior”

`analysis-agent`/`task-agent`/`review-agent`: use when form fields, validation, async checks, submit states, or recovery change; skip when form behavior is unaffected.

Debug and modify agent streaming behavior, covering SSE and plain-text transports, ChatKit events, and session continuity.

Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.

`analysis-agent`/`task-agent`/`review-agent`: use when Go context, goroutine, channel, error, interface, or resource semantics affect behavior; skip generic or non-Go work.

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

`analysis-agent`/`task-agent`/`review-agent`: use when behavior needs deterministic seams for time, randomness, UUIDs, collaborators, or external I/O; skip when seams are adequate.

Designs or reviews Agentforce agent actions, covering Flow, Apex invocable, and prompt-template actions, including naming, input/output contracts, confirmation, and error behavior.

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith. Analyze agent behavior, investigate errors, and review tool calls and performance metrics.

`task-agent`/`review-agent`: use when image layers, build context, runtime user, secrets, health checks, shutdown, or provenance change; skip when container behavior is unaffected.

`analysis-agent`/`task-agent`: use when tokens, components, responsive behavior, accessibility, or variants need consistency rules; skip when UI system rules are unaffected.

Agent skill for agent - invoke with $agent-agent

Add governance, safety, and trust controls to AI agent systems with policy enforcement, intent classification, and audit trails.

Decision-theoretic framework for agents embedded within the environments they model and act upon

`analysis-agent`: use when actors, goals, preconditions, triggers, paths, guarantees, postconditions, or acceptance traces need modeling; skip when no use-case decision exists.

Agent skill for researcher - invoke with $agent-researcher

Operate and extend barretlee/agent-pulse, the evidence-backed AI industry intelligence system: inspect source catalog and lifecycle, collect and normalize signals, bind evidence, cluster Events, evaluate system health, generate Scout hypotheses, export the privacy-safe public site, and verify release gates. Use when the user asks to run, configure, debug, extend, or explain Agent Pulse, its collectors, Control Room, narratives, Scout, or GitHub Pages output. Triggers on: agent-pulse, Agent Pulse, evidence-backed intelligence, source catalog, signal collection, Event clustering, source audit, Scout opportunity, public export, weekly brief, or AI industry intelligence pipeline.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

Use this skill for crawler behavior, log files, sitemap/indexation analysis, crawl-budget diagnostics. Trigger when the task involves agent profile work related to Crawler Analyst Agent, implementation, audits, debugging, strategy, or validation.

`analysis-agent`/`task-agent`: use when a use case coordinates authorization, domain work, transactions, or external effects; skip transport, storage, and rule-only work.

Analyzes repeated agent failures or noisy routing and proposes evidence-backed changes to instructions, skills, gates, or runbooks.

Use `analysis-agent` when engineering intent lacks desired behavior, boundaries, constraints, or completion signals. Skip requests with an accepted Engineering Brief and no-repo direct-answer work.

Error Recovery Playbook

Agent validation v4.3.0 — Make agents act effectively by disabling harmful actions, lowering gates, and injecting cross-run learning

`analysis-agent`/`task-agent`/`review-agent`: use for recurrence guards on known defects, incidents, or escaped failures; skip speculative risk without a prior failure mechanism.

Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".

Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Enforces a machine-readable behavioral contract for multi-agent development, mandating specification-first workflows, git checkpoints, context lineage, and quality gates.

`analysis-agent`/`task-agent`: use when actor journeys change entries, branches, interruption, recovery, authority, or side effects; skip route/state-only work.