niels-emmer
UserMyACE makes your AI coding agent's rules, skills, and workflows portable.
Categories
Indexed Skills (45)
agent-design-principles
A checklist for designing agent personas, skills, and multi-agent pipelines that stay reliable as they grow — grounded in the 12-factor-agents principles.
data-classification-guide
Public / internal / sensitive data tiers and what's allowed to touch an AI prompt, a log line, or a committed file at each tier.
secrets-scan-checklist
Common credential shapes to recognize in a diff (API keys, tokens, connection strings, private key blocks) and the escalation rule to follow when one is found.
security-audit-checklist
A PASS/FAIL/N/A checklist covering injection, authorization, secrets handling, dependency risk, and input validation for reviewing a diff or codebase.
threat-modeling-quickstart
A lightweight what-could-go-wrong / who-could-exploit-it / what's-the-blast-radius pass for any new network-facing interface, data store, or trust boundary.
api-design-checklist
Naming, status-code, error-shape, and versioning conventions for keeping an API internally consistent as it grows.
backend-test-patterns
Guidance for covering the failure and edge paths of a backend change — bad input, concurrent writes, partial failure — not just the happy path.
migration-safety
Checklist for shipping schema migrations that roll back cleanly and won't lock up a production table.
documentation-standards
Structure, tone, and formatting conventions for writing technical docs that stay accurate and get actually read.
agent-instruction-drift-check
Procedure for comparing an agent-facing instruction doc (AGENTS.md/CLAUDE.md-style) against the actual current code and configuration to find and flag mismatches.
readme-structure
A solid default README shape (what it is, quickstart, key commands, where to find more) and guidance on when a project has outgrown a single README.
accessibility-checklist
A concrete, checklist-driven pass for semantic HTML, ARIA, keyboard navigation, contrast, alt text, and focus management.
visual-verification-workflow
The procedure for actually confirming a UI change in a real or preview browser — golden path, an edge case, console errors, and responsive/dark-mode checks — before calling it done.
exception-documentation
The template for documenting a deliberate, approved deviation from an infrastructure invariant — rationale, compensating control, approver, and expiry — instead of a silent workaround.
iac-security-checklist
A PASS/FAIL/N/A checklist covering network exposure, identity and secrets, state protection, and tagging — run before any infrastructure change is proposed for apply.
resource-naming-and-tagging
A concrete, cloud-agnostic naming and tagging template for infrastructure resources — resource type, workload, environment, region, plus mandatory ownership and cost tags.
well-architected-pillar-review
A discipline for mapping any nontrivial infrastructure change to the standard architecture pillars — security, reliability, cost, operational excellence, performance — regardless of cloud provider.
architecture-review
When a design needs a second look before implementation starts, and what to actually evaluate when giving it one.
code-standards
Naming, structure, and consistency conventions for keeping a codebase readable and predictable as it grows.
memory-system
Maintain a tiered, file-based memory system so project context and decisions survive across sessions instead of being rediscovered or contradicted each time.
plan-tracking
Keep a running, visible record of task/plan state across a multi-step session so nothing silently gets dropped or forgotten.
security-checklist
A structured PASS / FAIL / N/A checklist covering common risk categories for reviewing a diff before merge.
test-patterns
What "well-tested" actually means — covering edge cases and failure modes deliberately, not just confirming the happy path runs once.
git-workflow
Lightweight branch naming and pull-request-vs-direct-push guidance for fast solo/small-team iteration.
android-testing
JUnit + Robolectric unit tests, Compose UI tests, screenshot tests, and Espresso for legacy views.
jetpack-compose-conventions
State hoisting, remember/derivedStateOf patterns, modifier ordering, and preview annotations.
play-store-readiness
App signing, keystore management, API level targeting, privacy policy, and review guideline checks.
cicd-pipeline-design
Stage isolation, caching, secret injection, artifact promotion, and rollback strategy for CI/CD pipelines.
container-build
Multi-stage builds, distroless base images, layer caching, and vulnerability scanning for production containers.
observability-stack
Structured logging, RED/USE metrics, distributed tracing, and alerting thresholds for production services.
component-conventions
Guidance on composing, naming, and sizing components, designing their props, and keeping their state as local as possible.
end-to-end-testing
E2E test patterns — what to test, how to structure tests, environment management, avoiding flakiness.
full-stack-error-handling
Error propagation patterns — backend error shape to API response to frontend fetch wrapper to UI state.
app-store-readiness
Privacy manifest, code signing, capability declarations, screenshot automation, and review guideline checks.
ios-testing
XCTest unit tests, XCUITest UI tests, snapshot testing, and performance baselines for iOS apps.
swiftui-conventions
@State/@Binding/@ObservedObject usage, view composition, preview-driven development.
build-tool-conventions
Maven/Gradle standard layout, dependency management, plugin versioning, and reproducible builds.
layered-architecture
Controller → service → repository layering, DTO boundaries, @Transactional placement, and exception hierarchy.
spring-test-pyramid
Unit, integration, slice, and E2E test patterns for Spring Boot applications.
data-validation
Data quality checks to run before modeling — distribution summaries, leakage detection, class balance.
experiment-tracking
How to properly set up experiment logging — what to log, how to name runs, how to compare and recover results.
model-evaluation
Evaluation methodology — split strategy, metric selection, baseline comparison, failure mode analysis.
reproducibility-standards
Seeding, dependency pinning, environment capture, and data versioning for fully reproducible ML workflows.
commit-format
A simple Conventional Commits style for clear, scannable commit history.
governance
MyACE project-specific governance rules. Data classification, ADR process, documentation maintenance, dependency compliance, and audit trail. Load at session start for any work on this project.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.