peterbamuhigire
UserSoftware Dev't Engine for coding. Production-grade skills system for web apps & mobile architecture, APIs, databases, web, mobile, AI, security, UX, SaaS, and ERP engineering. Encodes reusable engineering judgment through layered baseline and specialist skills to help generate secure, scalable, maintainable, high-performance software systems.
Categories
Indexed Skills (49)
new-project
Use when the task matches skill: new project scaffold and this skill's local workflow.
00-meta-initialization
Use when starting project documentation to detect project type, select Waterfall, Agile, or Hybrid delivery, and generate the documentation roadmap.
ai-agent-approval-audit-completeness
Use when checking that every irreversible agent action has signed approval evidence linked to the action audit log, and producing an approval-gap register or audit evidence pack.
ai-agent-commercial-operations
Use when pricing, billing, refunding, recognizing revenue, or packaging commercial terms for agentic AI services and outcomes.
ai-agent-compliance-controls
Use when mapping AI agent operations to SOC 2, ISO 27001, HIPAA, audit logs, control testing, attestations, and compliance evidence.
ai-agent-drill-evidence-and-cadence
Use when capturing kill-switch, red-team, and eval-drift drills as audit-ready compliance evidence — minimum-cadence enforcement, pass/fail recording, and cross-link to the incident drill skill. Turns drills from operational exercises into SOC 2 / ISO 27001 / HIPAA evidence rows with signed packs.
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.
ai-agent-memory-erasure-proof
Use when proving agent-memory erasure was complete and verifiable for GDPR / CCPA / POPIA / KE DPA requests — the 9-step cascade verification job emits a signed-off evidence pack. Pairs with `ai-agent-memory` (three-tier memory + erasure cascade) and `saas-tenant-data-portability-and-erasure` (tenant-level erasure pipeline).
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.
ai-agent-observability-evaluation
Use when measuring, evaluating, replaying, evidencing, or tracking success for AI agent tasks, steps, traces, and outcomes.
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.
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.
ai-agent-sla-and-customer-commitments
Use when defining agent SLAs, customer commitments, SLA dashboards, credits, support promises, and service evidence for agentic AI products.
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.
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.
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
ai-cost-and-metering
Use when modeling, metering, attributing, billing, or controlling AI usage costs across tenants, plans, features, providers, and agent workloads.
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.
ai-entitlements-and-feature-gating
Use when defining or enforcing plan-based AI feature access, model tiers, context limits, quotas, tool permissions, tenant overrides, and upgrade behaviour.
ai-evaluation
Use when setting up quality assurance for AI features — defining evaluation criteria, measuring output quality, using AI-as-judge, monitoring production AI, detecting drift, and building user feedback loops
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.
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.
ai-incident-response
Use when preventing, detecting, triaging, communicating, recovering from, or reviewing AI incidents, AI errors, RCA, and postmortems.
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.
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.
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).
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.
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
ai-rag-patterns
Use when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking strategies, hybrid search, re-ranking, vector databases, evaluation, agentic RAG, multimodal RAG...
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.
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.
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.
android-data-persistence
Use when designing Android local storage, Room migrations, DataStore preferences, or offline API synchronisation; use android-development for broader application structure.
android-development
Use when building or reviewing native Android applications with Kotlin, Compose, Hilt, and layered architecture; use android-data-persistence or android-tdd for focused data and test work.
android-tdd
Use when developing Android behaviour test-first across Kotlin, Room, repositories, ViewModels, and Compose; use android-development for architecture not centred on a test workflow.
api-design-first
Use when designing HTTP APIs with OpenAPI, versioning, authentication, rate limits, idempotency, error contracts, and observability requirements.
distributed-systems-patterns
Use when designing or reviewing multi-service, message-driven, or eventually consistent systems. Covers service boundaries, consistency tradeoffs, event workflows, outbox and inbox patterns, sagas, ordering, and idempotency.
ecommerce-platform-audit-requirements
Use when scoping or specifying an e-commerce platform, payment, API, security, AI, data protection, integration, and remediation audit for SMEs or cross-border digital trade programmes.
graphql-patterns
Use when designing or operating GraphQL APIs with schemas, resolvers, authorization, pagination, federation, code generation, and hostile-input controls.
microservices-architecture
Use when designing, reviewing, or refactoring microservice boundaries, communication, service ownership, deployment independence, resilience, and distributed data flows. Load absorbed microservices fundamentals, models, communication, and resilience references as needed.
system-architecture-design
Use when defining or reviewing software architecture for web apps, mobile backends, SaaS platforms, APIs, distributed systems, or major features. Covers bounded contexts, module decomposition, contracts, failure handling, ADRs, and scalability tradeoffs.
validation-contract
Use when authoring or normalising a specialist skill, or preparing to ship a feature or release — defines the seven evidence categories every specialist skill must declare against and provides the canonical Release Evidence Bundle template. The contract spine that turns scattered validation skills into a coherent ship-readiness check.
database-design-engineering
Use when designing or reviewing relational or document-backed data architecture for SaaS platforms, ERP systems, APIs, analytics stores, or mobile sync. Covers domain modeling, tenancy, indexing, migrations, integrity, retention, and performance tradeoffs.
database-reliability
Use when defining database SLOs, error budgets, backup verification, capacity policy, incident response, game days, or MySQL and PostgreSQL on-call practice.
mysql-engineering
Use when designing, implementing, or reviewing MySQL application schemas, SQL, indexes, constraints, stored routines, and production query patterns. Load absorbed MySQL best-practice, data-modeling, and advanced-SQL reference files as needed.
mysql-operations
Use when administering, tuning, backing up, restoring, monitoring, or troubleshooting MySQL production systems. Load absorbed MySQL administration and query-performance reference files for operational runbooks, indexes, replication, and incident response.
postgresql-engineering
Use when designing, implementing, or reviewing PostgreSQL application data models, SQL, indexes, constraints, extensions, server-side routines, and production query patterns. Load the absorbed PostgreSQL reference files for fundamentals, advanced SQL, schema patterns, and server programming.
postgresql-operations
Use when administering, tuning, backing up, restoring, monitoring, or troubleshooting PostgreSQL production systems. Load the absorbed PostgreSQL administration and performance reference files for operational runbooks, query tuning, vacuum, replication, and incident response.
skills-web-dev
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.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.