chwezi-dev-engine

In the author's own words, from each skill's SKILL.md — showing the first 30 of 168 files.

• chwezi-dev-engine

  Top-level router for the Chwezi Core Systems web-development and software-engineering skills engine. Use for AI systems, SaaS, architecture, APIs, databases, security, frontend engineering, mobile, DevOps, reliability, product engineering, SDLC documentation, catalog maintenance, delivery evidence packs, routing fixtures, and world-class engineering quality gates.

  SKILL.md on GitHub (opens in a new tab)

• 00-meta-initialization

  Use when initialising documentation and a methodology roadmap for a brand-new project by detecting project type, selecting Waterfall, Agile, or Hybrid delivery, and generating the governed document sequence.

  00-meta-initialization/SKILL.md on GitHub (opens in a new tab)

• new-project

  Use when the task matches skill: new project scaffold and this skill's local workflow.

  00-meta-initialization/new-project/SKILL.md on GitHub (opens in a new tab)

• execution-plan-scripts

  Use when converting an approved long-running plan into self-contained execution prompts across sessions or workers. Covers dependency order, checkpoints, capability fallback and evidence handoff; use world-class-engineering for a single implementation slice.

  skills/execution-plan-scripts/SKILL.md on GitHub (opens in a new tab)

• ai-agent-commercial-operations

  Use when pricing, billing, refunding, recognizing revenue, or packaging commercial terms for agentic AI services, or when defining agent SLAs, customer commitments, SLA dashboards, breach credits, and service evidence.

  skills/ai/ai-agent-commercial-operations/SKILL.md on GitHub (opens in a new tab)

• ai-agent-compliance-controls

  Use when mapping AI agent operations to SOC 2, ISO 27001, HIPAA, audit logs, control testing, attestations, and compliance evidence, including drill-cadence evidence, approval-completeness (PI1.1) gap checks, and signed agent-memory erasure proofs.

  skills/ai/ai-agent-compliance-controls/SKILL.md on GitHub (opens in a new tab)

• ai-agent-governance-and-limits

  Use when defining agent budgets, step limits, reversibility, blast-radius controls, kill switches, and governance policy for agentic AI systems.

  skills/ai/ai-agent-governance-and-limits/SKILL.md on GitHub (opens in a new tab)

• ai-agent-multi-agent-coordination

  Use when designing systems where multiple agents collaborate on one task — supervisor/worker, debate, plan-execute, peer handoff. Covers handoff message contracts, shared scratchpad, conflict resolution, deadlock detection, and the cost/SLA discipline that keeps multi-agent setups from spiralling.

  skills/ai/ai-agent-multi-agent-coordination/SKILL.md on GitHub (opens in a new tab)

• ai-agent-observability-evaluation

  Use when measuring, evaluating, replaying, evidencing, or tracking success for AI agent tasks, steps, traces, and outcomes.

  skills/ai/ai-agent-observability-evaluation/SKILL.md on GitHub (opens in a new tab)

• ai-agent-runtime-architecture

  Use when designing multi-tenant agent runtimes, control loops, state machines, retries, idempotency, step caps, durable resumability, scheduling, cancellation, and workflow boundaries.

  skills/ai/ai-agent-runtime-architecture/SKILL.md on GitHub (opens in a new tab)

• ai-agent-safety-and-red-team

  Use when red-teaming agent tool and data perimeters for indirect prompt injection, action escalation, tenant exfiltration, unsafe tool chains, self-modification, or containment regressions.

  skills/ai/ai-agent-safety-and-red-team/SKILL.md on GitHub (opens in a new tab)

• ai-agent-tooling-and-hitl

  Use when designing agent tool catalogs, tool schemas, action gating, human approval, and human-in-the-loop control for agentic AI systems.

  skills/ai/ai-agent-tooling-and-hitl/SKILL.md on GitHub (opens in a new tab)

• ai-analytics

  Use when designing AI analytics, dashboards, SaaS AI metrics, NLP analytics, predictive analytics, or executive AI insight workflows. Orchestrates the former granular AI analytics skills as references.

  skills/ai/ai-analytics/SKILL.md on GitHub (opens in a new tab)

• ai-app-architecture

  Use when designing or building AI-powered application systems — choosing architecture style, selecting components, structuring the AI stack, making build-vs-buy decisions, and planning multi-tenant AI module gating

  skills/ai/ai-app-architecture/SKILL.md on GitHub (opens in a new tab)

• ai-cost-and-metering

  Use when modeling, metering, attributing, billing, or controlling AI usage costs across tenants, plans, features, providers, and agent workloads.

  skills/ai/ai-cost-and-metering/SKILL.md on GitHub (opens in a new tab)

• ai-economic-value-engine

  Use when discovering, designing, prioritizing, or auditing AI-powered products for measurable business value. Applies to AI opportunity mapping, ROI cases, product strategy, client workshops, and deciding whether an AI feature should be built.

  skills/ai/ai-economic-value-engine/SKILL.md on GitHub (opens in a new tab)

• ai-evaluation

  Use when building an AI/LLM evaluation harness, golden datasets, quality metrics, judge calibration, regression gates, production monitoring, drift detection, or user-feedback loops; use ai-llm-integration for provider calls.

  skills/ai/ai-evaluation/SKILL.md on GitHub (opens in a new tab)

• ai-feature-rollout-and-experimentation

  Use when planning a controlled AI-feature rollout or experiment in multi-tenant SaaS: tenant/user feature flags, percentage and canary cohorts, holdout metrics, A/B tests of prompts or models, eval and SLO gates, automatic rollback, tenant opt-out and consent, or shadow mode for risky changes.

  skills/ai/ai-feature-rollout-and-experimentation/SKILL.md on GitHub (opens in a new tab)

• ai-feature-spec

  Use when specifying one AI-powered feature end to end, including model choice, prompt and context contracts, output schema, fallbacks, human oversight, UX states, and evaluation.

  skills/ai/ai-feature-spec/SKILL.md on GitHub (opens in a new tab)

• ai-incident-response

  Use when preventing, detecting, triaging, communicating, recovering from, or reviewing AI incidents, AI errors, RCA, and postmortems.

  skills/ai/ai-incident-response/SKILL.md on GitHub (opens in a new tab)

• ai-llm-integration

  Use when integrating an LLM provider into an application with streaming, structured outputs, tool calls, embeddings, multi-model routing, retries, caching, and usage metering, or when choosing regex, an LLM, or a hybrid pipeline to extract fields from structured text such as forms, invoices, or scraped listings.

  skills/ai/ai-llm-integration/SKILL.md on GitHub (opens in a new tab)

• ai-model-gateway

  Use when designing or building an LLM gateway for provider abstraction, tenant-aware routing, fallbacks, quotas, residency, audit logging, cost capture, and kill-switch enforcement.

  skills/ai/ai-model-gateway/SKILL.md on GitHub (opens in a new tab)

• ai-observability-and-debugging

  Use when building the observability stack for AI features in a multi-tenant SaaS — prompt/response tracing, semantic logging, replay tooling, "show me why this answer", per-stage latency/cost breakdown, ticket→trace tie-back, and dashboards that answer the operational questions (which tenant, which feature, which prompt version, which model).

  skills/ai/ai-observability-and-debugging/SKILL.md on GitHub (opens in a new tab)

• ai-opportunity-canvas

  Use when discovering and ranking AI use cases for a project or module and producing an opportunity register with impact, effort, cost, risk, and evidence gaps.

  skills/ai/ai-opportunity-canvas/SKILL.md on GitHub (opens in a new tab)

• ai-prompt-engineering

  Use when writing, refining, or structuring prompts for AI-powered app features — system prompts, user prompt templates, few-shot examples, chain-of-thought, prompt versioning, and defensive prompting

  skills/ai/ai-prompt-engineering/SKILL.md on GitHub (opens in a new tab)

• ai-rag-patterns

  Use when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking, hybrid search, re-ranking, vector databases, evaluation, agentic and multimodal RAG, and bounded iterative retrieval for context-starved subagents.

  skills/ai/ai-rag-patterns/SKILL.md on GitHub (opens in a new tab)

• ai-security

  Use when securing an AI/LLM-powered feature against prompt injection, cross-tenant data leakage and tenant isolation failures, jailbreaks, and adversarial inputs. Covers PII scrubbing before model calls, output validation, rate limiting, audit logging, and DPPA/GDPR compliance for AI data flows.

  skills/ai/ai-security/SKILL.md on GitHub (opens in a new tab)

• ai-web-apps

  Use when designing or building an AI-enhanced web app with chat, RAG, MCP tools, streaming, provider abstraction, feature gates, token budgets, and output guardrails.

  skills/ai/ai-web-apps/SKILL.md on GitHub (opens in a new tab)

• coding-agent-optimization

  Use when auditing or tuning a development machine's Claude Code or Codex setup for context, model, subagent, permission, or token efficiency, deciding when to compact a long session, or running independent lanes in parallel safely. Analyse local capabilities first; apply only supported reversible changes.

  skills/ai/coding-agent-optimization/SKILL.md on GitHub (opens in a new tab)

• openai-agents-sdk

  Use when building Python agents with the OpenAI Agents SDK, including runners, tools, handoffs, guardrails, tracing, memory, multi-agent topology, and deterministic orchestration.

  skills/ai/openai-agents-sdk/SKILL.md on GitHub (opens in a new tab)

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