shannon-execution-verifier

Use to systematically validate documents, stories, or processes against defined checklists. Ensures quality and completeness.

Execute one feature (FEAT-XXX) at a time using docs/forge/ideas/<IDEA_ID>/latest/tasks.md as the source of truth. Creates a short workspace checklist and tracks progress so reruns continue automatically.

<objective>

The ONLY way to interact with existing projects. Load when user references ANY project by name, ID, or number. Includes: continue, resume, status, progress, check, review, work on [existing project]. NEVER read project files directly.

Execute approved task specifications sequentially with TDD, comprehensive testing, and validation. This skill should be used for implementing tasks from approved specs with full audit trail.

This skill should be used when executing tasks from ai-state/active/tasks.yaml sequentially. It loads tasks, gathers context, implements features with phase-appropriate testing, updates task status in tasks.yaml, organizes tests into ai-state/regressions/ folders, and logs all operations to operations.log. Use after write-plan creates tasks.yaml or when resuming development work.

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Complete development lifecycle for GitHub/local issues - branch, implement, test, PR, merge with quality gates

executing-hms-runs

Execute a discrete GitHub issue from a detailed plan file (gi_*.md). Use when implementing a specific, well-defined task with an existing implementation plan in doc/plans/issues/. Handles status tracking, verification, and issue lifecycle.

Use when orchestrating parallel phases in plan execution - creates isolated worktrees for concurrent task execution, installs dependencies, spawns parallel subagents, verifies completion, stacks branches linearly, and cleans up (mandatory for ALL parallel phases including N=1)

<user_command>/kata:execute-phase</user_command>

Detailed execution logic for the executing-plans skill

Verification, drift detection, and snapshot logic for executing-plans

<user_command>/kata:quick-task</user_command>

Use when orchestrating sequential phases in plan execution - executes tasks one-by-one in main worktree using git-spice natural stacking (NO manual upstack commands, NO worktree creation, tasks build on each other)

Autonomously execute exploratory implementation of one approach from spike definition, working independently until natural stop. Use when partner provides spike definition and assigns you an approach number to explore in isolation, when comparing multiple implementation alternatives, or when evaluating technical feasibility before committing to an approach

Execute tasks from both GitHub issues and Basic Memory artifacts using unified workflow - supports intelligent source detection and dual-channel status management

Use when creating files, implementing features, or executing development tasks - guides shannon do command usage, dashboard integration, caching behavior, when to use vs shannon exec or shannon analyze

Analyze control flow, concurrency models, and event architectures in agent frameworks. Use when (1) understanding async vs sync execution patterns, (2) classifying execution topology (DAG/FSM/Linear), (3) mapping event emission and observability hooks, (4) evaluating scalability characteristics, or (5) comparing execution models across frameworks.

Handle common execution failures with specialized recovery strategies. Fix syntax errors, import/dependency issues, path/file problems, permission denial, and connection timeouts. Use proactively when encountering errors or as automatic recovery mechanism.

Create execution roadmaps for projects. Use when planning multi-phase projects or feature rollouts. Covers phased delivery and milestone planning.

Execution status management and reporting for implementation plugin. Use when initializing execution tracking, updating task status, generating progress reports, calculating completion metrics, tracking layer execution, monitoring feature implementation progress, or when user mentions execution status, progress tracking, or implementation monitoring.

<purpose>

Implement approved plans into production-ready code. Use when user wants to build, implement, code, or execute an approved plan. Activates when user says 'let's build', 'implement this', 'start coding', or 'execute the plan'.

Transform data into compelling executive narratives using the What/Why/Next framework from Gartner research

Executor package architecture for ChainGraph flow execution engine. Use when working on packages/chaingraph-executor, execution services, DBOS workflows, event bus, task queues, tRPC routes, or execution-related database operations. Triggers: executor, execution, service, worker, queue, event bus, dbos, workflow, tRPC execution, execution-api, execution-worker.

Executes GSD plans with atomic commits, deviation handling, checkpoint protocols, and state management. Spawned by execute-phase orchestrator or execute-plan command.

Creates hands-on student exercises and practice activities for learning prompt engineering. Use when the user asks to create student activities, practice exercises, or hands-on learning tasks. Generates exercises with examples, solutions, and differentiation.

Designs deliberate practice exercises applying evidence-based learning strategies like retrieval practice, spaced repetition, and interleaving. Activate when educators need varied exercise types (fill-in-blank, debug-this, build-from-scratch, extend-code, AI-collaborative) targeting learning objectives with appropriate difficulty progression. Creates exercise sets that apply cognitive science principles to maximize retention and skill development. Use when designing practice activities for Python concepts, creating homework assignments, generating problem sets, or evaluating exercise quality.

Break a high-level backlog item into executable sub-items

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Working expectations and documentation practices. Use when capturing learnings or understanding how to work with this codebase.

Analyze GRPO training runs for learning dynamics and pipeline performance. Use when diagnosing training issues, reviewing Elo progression, checking throughput, or updating experiment results.

Generates a rigorous experiment design given a hypothesis. Use when asked to design experiments, plan experiments, create an experimental setup, or figure out how to test a research hypothesis. Covers controls, baselines, ablations, metrics, statistical tests, and compute estimates.

Comprehensive guide to A/B testing, multivariate testing, statistical significance, and experiment analysis for data-driven product decisions

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Use when theoretical calculations need experimental validation, protocols must be designed with clear hypotheses and success criteria, or resource requirements (equipment, materials, expertise) must be estimated for proposed experiments

Do experiment-driven research (hypotheses → minimal repros → evidence) and continuously improve research skills + tooling. Use when behavior is uncertain, contested, or performance-sensitive.

Design YAML expertise file structures for agent experts. Use when creating mental models for domain-specific agents, defining expertise schema, or structuring knowledge for Act-Learn-Reuse workflows.

Explain all bluera-base plugin functionality in human-readable format

Explain how code works in detail. Use when trying to understand unfamiliar code, complex logic, or system architecture.

Use when explaining code, technical concepts, or implementation decisions. Provides structured approach to creating clear, understandable explanations tailored to the audience.

Systematic methodology for developing reliable exploits from vulnerability discovery to weaponization

Develop working exploits using pwntools. Includes exploit template and common patterns.

Exploit researcher persona specializing in attack surface analysis, exploit scenario generation, and vulnerability chaining

Comprehensive knowledge about vulnerability exploitation and initial access. Provides expertise on finding and adapting exploits, adapting proof-of-concepts, gaining shells, and capturing user flags. Covers reverse shells, file uploads, SQL injection, and RCE vulnerabilities.

Interactive code review through conversation. HOUSTON guides review, spawns specialized agents, and helps create Beads for issues found.

Select mode, delegate to analysis skill.