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),…
Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory.
Validates permission inheritance between parent and child agents. Ensures child permissions are equal to or more restrictive than parent.
Wave-based parallel scheduling for DAG execution. Manages execution order, resource allocation, and parallelism constraints.
Audits git repository hygiene: worktree state, stale branches, uncommitted changes, orphaned worktrees, and hook configuration. Finds the rot before it causes a crisis.
Validates DAG structures, performs topological sorting, detects cycles, and resolves dependency conflicts. Uses Kahn's algorithm for optimal execution ordering.
Identifies when task outputs require iteration based on quality signals, unmet requirements, or explicit feedback. Triggers appropriate re-execution strategies.
Validates agent outputs against expected schemas and quality criteria. Ensures outputs meet structural requirements and content standards.
Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs.
Human interface agent that translates ecosystem activity into clear, actionable communication. Creates status briefings, decision requests, celebration reports, concern alerts,…
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical…
Manages context passing between DAG nodes and spawned agents. Handles context summarization, selective forwarding, and token budget optimization.
Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities.
Modernizes legacy scripts (bash, Node, Python) by replacing deprecated APIs, adding error handling, converting callbacks to async/await, and improving maintainability.
Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services.
Combines and synthesizes outputs from parallel DAG branches. Handles merge strategies, conflict resolution, and result formatting.
How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context…
Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems.
Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure.
Design dashboards for migrations and runtimes. Use for authority drift, verifier status, burn-down, runtime health, or pain panels.
Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment.
Manages agent isolation levels and resource boundaries. Configures strict, moderate, and permissive isolation profiles.
Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes.
Performs root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance.
Design desktop window systems that feel native on macOS and Windows: split views, inspectors, auxiliary windows, tiling, snap-safe sizes, default placement, and chrome density.
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing.
Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit.
Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server…
Detects fabricated content, false citations, and unverifiable claims in agent outputs. Uses source verification and consistency checking.
Apply normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences.
Runtime enforcement of file system boundaries and tool access restrictions. Blocks unauthorized operations and logs violations.
Verifies build output integrity after code changes. Runs builds, validates artifact structure, checks for regressions in bundle size, asset references, and static export…
Generates shell completion scripts for CLI tools. Parses command structure from help output, commander/yargs configs, or manual specification.
Interprets GitHub Actions run status and logs, diagnoses CI failure patterns, and suggests targeted fixes.
Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops.
Tracks iteration progress toward task completion goals. Monitors quality trends, detects plateauing, and recommends when to stop iterating.
Production validation specialist for post-deployment smoke tests, SEO audits, visual regression, and analytics verification.
Synthesizes actionable feedback from validation results, confidence scores, and iteration triggers. Creates structured improvement guidance for re-execution.
Plans large-scale refactoring campaigns across codebases. Analyzes dependency graphs, calculates blast radius, designs migration strategies, and creates incremental plans that…
Executes DAG waves with controlled parallelism using the Task tool. Manages concurrent agent spawning, resource limits, and execution coordination.
Expert in making CLI tools visually stunning and delightful — not just functional. Covers ANSI color systems (16/256/truecolor with graceful degradation), Unicode box drawing,…
Modifies DAG structure during execution in response to failures, new requirements, or runtime discoveries. Supports node insertion, removal, and dependency rewiring.
Parses complex problems into DAG (Directed Acyclic Graph) execution structures. Decomposes tasks into nodes with dependencies, identifies parallelization opportunities, and…
The intelligence layer of WinDAGs. Decomposes natural language tasks into Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, executes waves in parallel, and dynamically…
Choose what kind of knowledge to transfer between teacher and student models: response, feature, or relational, and decide among offline, online, self, or cross-modal distillation…
Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates.
Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus,…
Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations.
Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing.
Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads,…
Refactor apps so backend owns runtime truth and UI only projects snapshots. Use when tools have split authority. NOT for cosmetic UI work or new features before drift is removed.
What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and…
Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits.
Apply rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation.
Ranks skill matches by fit, performance history, and contextual relevance. Applies multi-factor scoring including success rate, resource usage, and task alignment.
Selects the optimal LLM model and provider for each task based on complexity, cost budget, and capability requirements.
Build WinDAGs — the orchestration platform where AI agents accumulate genuine expertise through DAGs of skillful agents.
Renders Mermaid diagrams to SVG, PNG, and PDF in both web and offline contexts. Covers client-side lazy loading, SSR trade-offs, CLI batch export, and native Rust rendering.
Validates that a DAG node's output matches its declared JSON schema before passing to downstream nodes. The glue that makes multi-agent DAGs reliable.