ReddRadar
Topic: babysitter
2,051 skills in this topic.
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design-review-gate
Parallel design review by 6 specialist agents (PM, Architect, Designer, Security Design, UX, CTO) with mandatory unanimous approval.
a5c-ai/babysitter 514
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external-tool-coordination
Coordinate external AI tool integration (OpenAI Codex, Google Gemini) for cross-model adversarial review and delegated implementation.
a5c-ai/babysitter 514
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knowledge-curation
Context priming before work (bd prime) and self-reflection after completion to extract patterns, gotchas, and decisions into the knowledge base.
a5c-ai/babysitter 514
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orchestrated-execution
Execute work units through the rigorous 4-phase Metaswarm cycle (Implement -> Validate -> Adversarial Review -> Commit) with independent quality gate enforcement.
a5c-ai/babysitter 514
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plan-review-gate
Adversarial plan review by 3 independent reviewers (Feasibility, Completeness, Scope & Alignment) before presenting to user.
a5c-ai/babysitter 514
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pr-shepherding
Monitor PR lifecycle from creation through merge including CI monitoring, review comment handling, thread resolution, and merge readiness verification.
a5c-ai/babysitter 514
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work-unit-decomposition
Decompose implementation plans into discrete work units with enumerated DoD items, file scope declarations, dependency mapping, and human checkpoint flags.
a5c-ai/babysitter 514
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brainstorming
Clarify vague requirements through exploratory questioning and option generation before committing to research or implementation.
a5c-ai/babysitter 514
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code-review
Structured code quality assessment with Conventional Comments format, scaled review depth, and soft-gating verdicts preserving user autonomy.
a5c-ai/babysitter 514
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codebase-research
Systematic codebase exploration following the Iron Law - understand the problem before exploring code. Four phases with file-finder and web-researcher agents.
a5c-ai/babysitter 514
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decision-documentation
Create Architecture Decision Records (ADRs) documenting significant technical choices with context, options, consequences, and sequential numbering.
a5c-ai/babysitter 514
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finishing-work
Final completion discipline including summary generation, plan document updates, and confirmation that all success criteria from the original plan are satisfied.
a5c-ai/babysitter 514
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plan-implementation
Disciplined execution of approved plans with step-by-step verification, phase checkpoints, failure investigation, and mandatory code/security reviews.
a5c-ai/babysitter 514
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plan-writing
Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.
a5c-ai/babysitter 514
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security-review
Security vulnerability assessment identifying OWASP risks, injection vectors, authentication issues, and data exposure with severity classification.
a5c-ai/babysitter 514
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systematic-debugging
Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.
a5c-ai/babysitter 514
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test-driven-development
Test-first development practice where test specifications are written before production code, integrated into plan tasks as mandatory first sub-steps.
a5c-ai/babysitter 514
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verification
Verification-before-completion discipline ensuring all success criteria are met, tests pass, and reviews complete before declaring work done.
a5c-ai/babysitter 514
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agent-booster
WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.
a5c-ai/babysitter 514
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anti-drift
Hierarchical coordination and drift detection with frequent checkpoints, shared memory coherence validation, role specialization enforcement, and short task cycles.
a5c-ai/babysitter 514
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consensus-mechanisms
Multi-protocol consensus for agent swarms supporting Raft leader election, Byzantine fault tolerance, Gossip state propagation, and CRDT conflict-free merging.
a5c-ai/babysitter 514
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security-hardening
AIDefence security layer with prompt injection blocking, input validation, sandboxed execution, output sanitization, and STRIDE threat modeling.
a5c-ai/babysitter 514
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self-optimization
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
a5c-ai/babysitter 514
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smart-routing
Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.
a5c-ai/babysitter 514