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SkillsMP has collected 143 skills from peterbamuhigire/skills-web-dev. Open a skill to review its source and details.
peterbamuhigire/skills-web-devShowing 40 of 143 collected skills.
Use when designing per-tenant rate limits and quotas — algorithm choice (token bucket, sliding window, leaky bucket, fixed window), where to enforce (edge, gateway, service, DB connection pool, queue), per-plan / per-tier limits, soft vs hard caps,…
Use when implementing enterprise auth on a multi-tenant SaaS — SAML 2.0 and OIDC SSO with per-tenant IdP configuration, SCIM 2.0 user provisioning/deprovisioning, custom-domain support with automated TLS, IP allowlists per tenant, audit-log API, and the…
Use when designing the GDPR/POPIA/CCPA-compliant data export (right to portability) and erasure (right to be forgotten) workflows for a multi-tenant SaaS — cascade through every data store including warehouse/backups, retention policy, requester verification,…
Conduct a comprehensive implementation status audit of any software project. Produces structured documentation in docs/implementation/review-{date}/ with gap analysis, schema audit, integration status, completion blueprint, and prioritized action...
Guided interview to create comprehensive project requirements documentation (requirements.md, business-rules.md, user-types.md, workflows.md) for a new SaaS project. Use before bootstrapping the SaaS Seeder Template.
Use when authoring a new skill, normalising an older skill, or reviewing a skill PR — defines the repository-wide house style (frontmatter, decision rules, anti-patterns, references), the output contracts each baseline-skill type must produce, and the input…
Use when creating or upgrading skills in this repository. Covers repository-specific frontmatter rules, progressive disclosure, reference-file strategy, validation, and the quality bar required for production-grade engineering skills.
Use when designing, building, reviewing, or upgrading production software systems that must be secure, performant, maintainable, scalable, and user-centered. Apply before writing specs, code, architecture, APIs, databases, mobile apps, SaaS platforms, or ERP…
Generate a Data Protection Impact Assessment (DPIA), Uganda DPPA 2019-compliant. Use when producing or reviewing a data protection impact assessment, a privacy impact assessment, when uganda-dppa-compliance flags [DPIA-REQUIRED], or when processing…
Use when proving that every irreversible agent action in the audit window had a documented, signed approval — completeness check (gap-detection job), approval-evidence cross-link to the hash-chained action audit log, and evidence pack for SOC 2 Processing…
Use when pricing, billing, refunding, recognizing revenue, or packaging commercial terms for agentic AI services and outcomes.
Use when mapping AI agent operations to SOC 2, ISO 27001, HIPAA, audit logs, control testing, attestations, and compliance evidence.
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 /…
Use when defining agent budgets, step limits, reversibility, blast-radius controls, kill switches, and governance policy for agentic AI systems.
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…
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…
Use when measuring, evaluating, replaying, evidencing, or tracking success for AI agent tasks, steps, traces, and outcomes.
Use when hardening agentic features against agent-specific attack surfaces — indirect prompt injection (via tool output, retrieved chunk, web page), action escalation (chain a low-privilege tool's output into a high-privilege tool's args), tenant data exfil…
Use when defining agent SLAs, customer commitments, SLA dashboards, credits, support promises, and service evidence for agentic AI products.
Use when designing agent tool catalogs, tool schemas, action gating, human approval, and human-in-the-loop control for agentic AI systems.
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.
Use when modeling, metering, attributing, billing, or controlling AI usage costs across tenants, plans, features, providers, and agent workloads.
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.
Use when designing how AI features are unlocked by plan tier — which model tier (flagship vs distilled), context-length limits, generations/day, tools available to the agent, KB ingestion size, gated AI features (per-feature toggle by plan). Covers…
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
Use when rolling out AI features safely in a multi-tenant SaaS — feature flags scoped per tenant/user, percentage rollouts gated by eval and SLO budget, canary cohorts, A/B testing of prompts/models, automatic rollback on quality regression, tenant-level…
Design a single AI-powered feature end-to-end — model selection, prompt engineering, context window, output schema, fallback behaviour, human oversight, and UX integration. Invoke for each opportunity identified in ai-opportunity-canvas.
Use when preventing, detecting, triaging, communicating, recovering from, or reviewing AI incidents, AI errors, RCA, and postmortems.
Integrate LLMs into any application — OpenAI, Anthropic Codex, DeepSeek, and Gemini APIs directly (no framework required), streaming responses, function calling/tool use, embeddings and semantic search, multi-model routing, prompt caching, rate...
Use when designing or building the LLM gateway — the single outbound surface from the SaaS to all LLM providers. Covers provider abstraction, model selection per tenant tier, fallback chains, retries, per-tenant rate limiting and token caps, request signing,…
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…
Systematically discover and rank AI use cases for any software project or module. Produces a prioritised AI Opportunity Register with business impact, implementation effort, and cost estimates. Invoke after any project description or module...
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...
Use when designing or building an AI-enhanced web app (Next.js + Vercel AI SDK, MCP tools, multi-provider chat/RAG) — produces the module gate, token-ledger + budget schema, provider abstraction, and output guardrails. Specialises the integration patterns in…
Build production AI agents with the OpenAI Agents SDK (Python) — 6 core primitives (Agent, Runner, Tools, Handoff, Guardrails, Tracing), multi-agent patterns (Centralized, Hierarchical, Decentralized, Swarm), dynamic/deterministic orchestration...
Android data persistence standards with Room as primary local storage and custom API backends for cloud sync. Covers SharedPreferences, DataStore, Room (entities, DAOs, relations, migrations), file storage, offline-first architecture, and...
Android Test-Driven Development standards. Enforces Red-Green-Refactor cycle, test pyramid (70/20/10), layer-specific testing strategies, and CI integration. Use when building or reviewing Android apps with TDD methodology.
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.
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…
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.