Results for “gates”

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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

Implements age-gating mechanisms for online services to restrict access based on user age. Covers hard gates versus soft gates, neutral age prompts, re-verification triggers, circumvention prevention, and regulatory requirements under GDPR, COPPA, UK Online Safety Act, and DSA. Keywords: age gate, age restriction, neutral prompt, children, online services, access control.

`analysis-agent`/`task-agent`/`review-agent`: use for structure, workspace or package bootstrap, config, scripts, or quality gates; skip without an initialization decision.

Design task-local harnesses, eval gates, and reusable skill extraction for adaptive agent workflows.

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

Launches coordinated agent teams for tasks requiring persistent cross-talk, shared state, and lifecycle management, with team design, advisor coordination, and quality gates.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Patterns, conventions, and guardrails for the closed-loop systems in AegisNex. Covers the AI intelligence graph, Guardian auto-restart, multi-agent orchestration, incident lifecycle, self-improvement memory, and risk/policy gates.

Everything Claude Code (ECC) — agent harness configuration layer for Claude Code and other AI coding IDEs. Augments agents with skills, hooks, persistent memory, model routing, and quality gates without modifying the underlying model.

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Runs a multi-agent academic research pipeline covering systematic review, LaTeX paper writing, peer review, and end-to-end orchestration with integrity gates.

Agent profile for review prompt injection, tool boundaries, untrusted content, data exfiltration, and human approval gates. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Portkey AI gateway — unified LLM API, load balancing, fallbacks, caching, guardrails, observability

How Hermes talks to Gas Town from outside the Mayor session — listing beads, slinging work to rigs, sending mail, escalating, all via the `gt` MCP server (Ophis Cobra→MCP bridge). Use whenever the user asks Hermes to dispatch agent work, check what the swarm is doing, or coordinate across rigs.

Adds input and output guardrails to an agent, including validation, content filtering, domain scoping, and rate limiting, with TypeScript examples and wiring instructions.

Unified SaaS integration gateway via api.aisa.one (AISA gateway, v3.1): manage OAuth auth for third-party SaaS apps (Gmail/Slack/GitHub/Notion etc.), tool execution, tool-router sessions, triggers, webhooks, MCP servers, and usage stats. Use when connecting third-party SaaS accounts, running cross-SaaS tools, managing MCP servers, setting up triggers, or checking usage. Keywords: SaaS gateway, connect app, OAuth link, run tool, auth config, tool router, MCP server, trigger, webhook, connected account, usage stats

Use `analysis-agent` to analyze permissions, secrets, sensitive data, trust boundaries, and injection; `task-agent` to implement controls; and `review-agent` to assess evidence. Skip self-review and no-trust-impact work.

Verify supply chain integrity for AI agent plugins, tools, and dependencies by generating SHA-256 manifests, detecting tampered files, auditing dependency pinning, and enforcing promotion gates.

Enhances plain-language requests into structured, reviewable prompts and inspects a self-hosted MCP gateway with provider-free defaults.

Use `task-agent` for bounded logging changes or `review-agent` to independently assess placement, schema, severity, redaction, correlation, and signal tradeoffs. Skip work with no logging impact and self-review requests.

ScepticAgent internal architecture reference. Use whenever the user asks how the extension works, wants to add a new AI provider, add a new highlight category, debug communication between components, understand the agent loop or streaming, or work with provider routing and the Gemini CORS proxy.

Unified LLM Gateway - One API for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more with a single API key. Use when: the user needs model routing, provider setup, or Chinese LLM access guidance.

Designs and governs recursive self-improvement loops where an agent mines its own failures and proposes edits to its own harness, prompts, or workflow, covering acceptance gates, diversity preservation, and the optimization ladder.

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.

Design and implement clear, modular, resilient software and create executor-grade implementation plans with explicit scope, drift checks, verification gates, tests, STOP conditions, dependencies, and delegated-work review. Use for development, refactoring, architecture, implementation planning, safe migrations, handoffs to another engineer or agent, or verifying delegated code changes.

Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows. Use when deciding which agent actions require review, adding approve/reject or dual-control flows, preventing unauthorized autonomous effects, creating decision records, reducing rubber-stamping, or recovering safely from rejected, expired, or failed actions.

Audits natural-language programming artifacts such as SKILL.md, AGENTS.md, CLAUDE.md, slash commands, plugin manifests, hooks, rules, and prompt files. Use when reviewing AI-agent repositories, checking manifest-vs-disk consistency, scoring skill or agent quality, adding NL artifact CI gates, or diagnosing vocabulary and version drift across Claude Code, Codex, Cursor, Gemini, and Antigravity-style projects.

Use to design support career progression with IC and lead tracks, skill gates instead of tenure alone, and paths that do not treat leaving the phones as the only promotion. Trigger for "career pathing", "progression framework for support", "IC track", "how do agents get promoted", "support ladder", "team lead vs senior agent", or fixing promotion bottlenecks and title inflation.