Results for “agent-reputation”

Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.

Rates an agent's own output on five axes — accuracy, completeness, clarity, actionability, conciseness — producing a structured scorecard with evidence and improvement suggestions.

Automatic agent selection and intelligent task routing. Analyzes user requests and automatically selects the best specialist agent(s) without requiring explicit user mentions.

Agent profile for review API platform contracts, versioning, auth, rate limits, SDK clients, and developer experience. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Agent Roster

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Checks evidence about an unfamiliar autonomous agent before delegation, verifies signed Agent Guild passports, and returns a bounded hire, caution, or avoid recommendation without taking consequential action.

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

Agent skill for payments - invoke with $agent-payments

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.

`analysis-agent`/`task-agent`/`review-agent`: use when endpoints, payloads, errors, auth, pagination, idempotency, or compatibility need a contract; skip when APIs are unchanged.

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".

Simulator.Company digital-twin & actor-agent specialist — talk to an agent AS an agent and delegate work to it. An agent is ANY actor whose `description` holds an "# Agent" competency profile (what it does, what it knows, whether it fits a task). The common case is a person: every workspace user has a 1:1 twin actor (`systemObjType="user"`) carrying that profile. But any actor can be an agent — a service/bot twin, a team or department, an organization, a process. This skill discovers the agent (`findAgent`), loads its profile (`getAgent`), adopts it as the persona, then either does the task, finds a better-suited agent, or hands the decision to the user (for a person: a task or a p2p message; for a non-person: propose another executor or run/trigger it as an actor). It is the actor-analog of `simulator-skills` (the `Skills`-form registry), but the registry is the workspace's agent actors. Use when the user wants to "delegate", "assign this to <someone/something>", "can <X> do this", "who/what should do this",

Agent profile for evaluate RAG quality, chunking, retrieval, citations, hallucination risks, freshness, and regression sets. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Agent Accuracy Enhancement Skill

Agent skill for reviewer - invoke with $agent-reviewer

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

Run multiple AI agents against a product, SDK, or docs site to score setup friction, speed, efficiency, error recovery, and doc quality from a minimal prompt.

Agent profile for PPC and paid-growth risk review before changing ads, budgets, bidding, tracking, or landing pages.

Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.

Expert en agent loops (ARIS, Ralph, recherche autonome, build-score-budget, guardrails)

`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.

Agent skill for agent - invoke with $agent-agent

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Use an independently assigned `review-agent` for post-authoring review of changed rd-skills routing registries, fixtures, mappings, or owner conflicts without repair. Skip product work and in-task global rerouting.

`analysis-agent`/`task-agent`/`review-agent`: use when latency, throughput, bundle, memory, CPU, query, rendering, or resource cost needs a budget; skip without performance risk.

`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.

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.

Answer Engine Optimization (AEO) specialist agent.

Agent skill for architecture - invoke with $agent-architecture

Use `analysis-agent` to map user-flow, interaction-state, accessibility, content, and recovery impact when experience behavior changes. Skip backend-only work with no experience effect and question-only requests.

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.

Registers an AI agent on the ClawSwarm hub, generates an identity card, and emits OADP discovery signals for cross-platform findability.