Results for “agent-behavior”

Authors and updates customization overrides for installed BMad skills. Use when the user says 'customize bmad', 'override a skill', 'change agent behavior', or 'customize a workflow'.

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.

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.

Audit public skill or plugin bundles for ClawHub, Claude, Hermes, AgentSkill, AgentSkills.so, and GitHub release risks. Use when: checking Suspicious/upload flags, dangerous local behaviors, quality/security mismatches, legacy auth surface, or non-runtime files before publishing.

Guides coding agents through architecture-first UI/UX design, from product truth and user journeys to rendered browser critique, covering art direction, interaction, responsive behavior, and motion.

Ensures skills are invoked before any response, including clarifying questions, and establishes how to find and use skills.

Build, run, and debug iOS projects on a booted simulator using XcodeBuildMCP tools. Interact with the UI, capture logs, and diagnose runtime behavior.

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.

Publish Heroku apps into Salesforce and Agentforce with Heroku AppLink. Use when the agent needs AppLink publication workflows, OpenAPI publication preparation, publish a Heroku app to Salesforce, review AppLink publication state, inspect shared publication endpoints or shared objects, or reason about Agentforce, Flow, Apex, Data Cloud, service mesh, and AppLink user-mode behavior.

Orchestrator for agent harness work — the setup that makes AI agents follow project rules and improve when they fail. FIRES PROACTIVELY when agents misbehave, repeat mistakes, ignore instructions, skip skills, or when AGENTS.md exists but docs/harness/manifest.json is missing. Also triggers on: harness engineering, agent scaffold, agent keeps failing, agent not following instructions, make agents reliable, agents going off rails, agent forgot context, improve agent setup, self-improving agents, agents keep making mistakes, why is my agent bad, agent quality, agent setup broken, agents ignore skills, same mistake again, fix agent behavior, tune agent instructions, set up agent infrastructure, after project setup agents still bad. Routes bootstrap vs evolution. Not multi-agent topology — agent-builder.

AI Agent personality diagnosis and configuration system based on MBTI framework. Use when users want to (1) test/diagnose an Agent's personality type, (2) understand the gap between Agent's actual personality and user's desired personality, (3) generate configuration recommendations to adjust Agent behavior, (4) customize Agent's communication style, proactivity, reasoning approach, or execution patterns. Supports both free tier (quick assessment) and premium tier (full 93-question assessment with detailed diagnostics).

Restructure code while preserving behavior: confirm tests are green, refactor in small steps, keep tests green, review, and commit with a gated workflow.

Use `analysis-agent` to identify domain ownership, invariants, state transitions, and cross-context effects when rules change or ownership is unclear. Skip presentation-only or question-only work with no domain behavior.

Use when creating test scenarios for a skill, setting up a tests/ directory for a plugin or package, running skill tests against a subagent, or verifying whether a SKILL.md changes agent behavior

Orchestrate altering an existing, working feature to new desired behavior by updating tests first, then changing implementation, reviewing, and committing with gated approval.

Builds and enforces brand identity through structured agents covering positioning, voice, visual systems, and consistency audits, with behavioral science frameworks applied throughout.

Extracts a language-agnostic ghost package (spec, tests, install and verify docs) from an existing repository, preserving behavior via tests.yaml and evidence bundles.

Expert in Cursor AI IDE - the leading AI-powered code editor. Covers Rules files for project-specific AI behavior, Plan Mode for structured development, Background Agents for parallel work, and advanced features like voice mode and browser integration. Essential for developers maximizing AI-assisted coding productivity. Use when "cursor ai, cursor rules, .cursorrules, cursor composer, cursor agent, plan mode, background agent, cursor tips, cursor, ide, ai-coding, editor, productivity, agent, developer-tools" mentioned.

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

Operate running Heroku apps with a read-only-first workflow. Use when the agent needs to inspect app status, app info, current process state, dynos, releases, logs, one-off commands, maintenance mode, scaling, restarts, or rollback behavior for a Heroku app.

Manage Heroku pipelines and review apps with stage-aware guardrails. Use when the agent needs to inspect or configure Heroku pipelines, connect apps to stages, promote slugs between stages, enable review apps, or reason about `app.json` behavior in Heroku review workflows.

Audit agent skills, plugins, prompts, manifests, scripts, dependencies, and bundled assets for provenance, prompt-injection, permission, execution, exfiltration, persistence, and update risk. Use when evaluating a third-party skill before installing, enabling, updating, publishing, or distributing it; reviewing an untrusted SKILL.md, agent configuration, MCP integration, archive, or repository; comparing a package with a known-good version; or investigating unexpected tool, network, credential, or filesystem behavior.

Set up and run mutation testing (StrykerJS / mutmut) to measure whether tests assert behavior, not just execute lines. Use when "add mutation testing", "are our tests real", "can our test suite be gamed", or after an agent bulk-generated tests. Coverage plan → plan-test-coverage. Writing tests → test-unit.

The living embodiment of WinDAGs V3 — the AI orchestration platform where agents accumulate genuine expertise. Knows the full constitution, convention process, tradition positions, behavioral contracts, and derivative documents. Advises on architecture with ADR provenance, explains decisions with tradition attribution, grows source material as we build. Activate on "windags", "WinDAGs", "constitution", "convention", "why did we decide", "what does WinDAGs say", "how should we build", "architecture decision", "tradition", "behavioral contract". NOT for building specific DAGs (use windags-architect), creating individual skills (use skill-architect), or managing skill libraries (use windags-librarian).

Use when reviewing or refactoring code that pulls apart another object's private representation, such as raw limb/byte/slot access, slice constructors, or helper methods that expose internal storage. Guides agents to find representation envy and suggest moving behavior onto the object that owns the representation, using live-image Genie tools when working in Pharo.

Use when an agent needs one local file uploaded to MetaWeb and wants the returned metafile URI, preview URL, or download URL. This is the single file upload skill for both small and large local files; choose direct or large/chunked upload behavior from file size and runtime limits instead of asking the user to choose another skill.

Build and improve AI or probabilistic software through evaluation-driven development. Use for LLM applications, agents, prompts, RAG, tool use, classifiers, model migrations, quality regressions, golden datasets, LLM-as-judge rubrics, benchmarks, or requests to add evals and measurable release gates. Pair with TDD for deterministic code; do not use as the primary guide for ordinary unit testing without model behavior.

Add accessible animated AI loading and agent-status indicators with the React thinking-orbs library. Use when a chat, copilot, voice, search, generation, or tool-running interface needs a semantic working, searching, solving, listening, composing, or shaping state; when replacing a generic spinner with an AI activity orb; or when implementing the library's size, theme, speed, pause, reduced-motion, and canvas behavior.

Use when developing in packages/agent-core-v2 (the DI × Scope agent engine) — adding or modifying a domain Service, choosing a LifecycleScope, wiring DI dependencies, splitting a domain across scopes, owning or migrating a config section, gating behavior behind an experimental flag, raising coded errors, working on the permission system, writing DI/Scope tests, porting business logic from agent-core (v1) to v2, triaging a main-branch commit against v2, or exposing a v2 domain over server-v2 while keeping the /api/v1 wire contract compatible with released clients. Self-contained guide organized by development stage (orient → design → implement → test → verify) plus align workflows for v1→v2 migration, main-branch commit triage, and server-v2 wire exposure; each file carries the rules, examples, and red lines for its step.

Legacy-project style inheritance skill. Use when the user types /inherit-legacy-style, or when onboarding an AI coding agent onto a hand-written legacy project and you need to prevent "style drift" (the model imposing its pretrained mainstream idioms onto the project). Language- and framework-agnostic — it aligns meta-architecture only, not syntax. Once run, it becomes a behavioral constraint on all subsequent coding tasks. Do NOT use for pure research or one-off questions unrelated to code-style alignment.

Predicts the highest-impact next action for your project by running a 5-agent meta-DAG pipeline. Gathers project signals automatically (git, recent files, port-daddy, CLAUDE.md), then runs sensemaker → decomposer → skill-selector + premortem → synthesizer. When execution is approved, convert each predicted node into a skillful node prompt using skillful-node-prompt + skillful-subagent-creator, prefer live WinDAGs visualization backed by POST /api/execute and /ws/execution/:id, and fall back to ASCII only when live visualization is unavailable. Activate on: "what should I do", "what's next", "next move", "/next-move", "where should I focus", "what's the highest impact thing right now". NOT for: creating skills, debugging one specific bug, or promising topology-specific runtime behavior the current server cannot execute.