agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Refines ideas iteratively. Refine ideas through structured divergent and convergent thinking. Use "idea-refine" or "ideate" to trigger.
KFL2 (Kubeshark Filter Language) reference. This skill MUST be loaded before writing, constructing, or suggesting any KFL filter expression. KFL is statically typed — incorrect field names or syntax will fail silently or error. Do not guess at KFL syntax without this skill loaded. Trigger on any mention of KFL, CEL filters, traffic filtering, display filters, query syntax, filter expressions, write a filter, construct a query, build a KFL, create a filter expression, "how do I filter", "show me only", "find traffic where", protocol-specific queries (HTTP status codes, DNS lookups, Redis commands, Kafka topics), Kubernetes-aware filtering (by namespace, pod, service, label, annotation), L4 connection/flow filters, time-based queries, or any request to slice/search/narrow network traffic in Kubeshark. Also trigger when other skills need to construct filters — KFL is the query language for all Kubeshark traffic analysis.
Pre-merge review checklist based on recurring AI reviewer feedback patterns
Compose custom agents from base traits, voices, and specializations for specialized perspectives. Create agent teams for parallel work. USE WHEN create custom agents, spin up agents, specialized agents, agent personalities, available traits, compose agent, agent profile, spawn parallel agents, launch agents, agent teams, swarm.
Compose CUSTOM agents from Base Traits + Voice + Specialization for specialized perspectives. USE WHEN create custom agents, spin up agents, specialized agents, agent personalities, available traits, list traits, agent voices, compose agent, load agent context, agent profile, spawn parallel agents, launch agents. NOT for agent teams/swarms (use Delegation skill → TeamCreate).
Meta-prompting system that generates optimized prompts using templates, standards, and patterns. Produces structured prompts with role, context, and output format. USE WHEN meta-prompting, template generation, prompt optimization, programmatic prompt composition, render template, validate template, prompt engineering.
Objective eval metrics via code/model/human graders with pass@k/pass^k scoring. USE WHEN eval, evaluate, test agent, benchmark, verify behavior, regression test, capability test, run eval, compare models, compare prompts, create judge, create use case, view results, failure to task, suite manager, transcript capture, trial runner.
Manages AI SDK model configurations - updates packages, identifies missing models, adds new models with research, and updates documentation
Generate financial statements (income statement, balance sheet, cash flow) with period-over-period comparison and variance analysis. Use when preparing a monthly or quarterly P&L, closing the books and need to flag material variances, comparing actuals to budget, building a financial summary for leadership review, or looking up GAAP presentation requirements and period-end adjustments.
Decompose financial variances into drivers with narrative explanations and waterfall analysis. Use when analyzing budget vs. actual, period-over-period changes, revenue or expense variances, or preparing variance commentary for leadership.
Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.
Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Guidance for composing well-formatted, effective Slack messages using mrkdwn syntax