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jdinkla

@jdinkla source repo

27 published skills

  1. Story Agentic AI · jdinkla
    Assess whether a user story is suitable for implementation by an autonomous AI agent (Claude with tool use, etc.) — scores across 8 criteria including decomposability, tool availability, determinism, and observability. Use when the user asks "can an AI agent build this?", wants to decide between human vs. agent implementation, or is planning agent workloads.
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  2. Story Bias Audit · jdinkla
    Audit an AI/ML or data-driven user story for algorithmic bias and discrimination — identify bias types, proxy variables, protected groups affected, fairness metrics, regulatory requirements (EU AI Act, NYC LL144, EEOC, etc.), and testing recommendations. Use when the user asks about algorithmic fairness, bias audit, AI ethics review, or discrimination risk on a feature.
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  3. Story Edge Cases · jdinkla
    Adversarial edge-case hunting — identify 8–15 scenarios that could break a feature, thinking like an attacker and chaos engineer, across input validation, state, concurrency, integration, user behavior, data, and performance. Use when the user asks "what could break this?", wants edge cases, or needs adversarial/chaos testing ideas.
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  4. Story A11Y Linter · jdinkla
    Check a user story against WCAG 2.2 standards — find accessibility issues with cited WCAG criteria, conformance level (A/AA/AAA), impact, affected users, code examples, and assistive-tech considerations. Use when the user asks for an a11y review, accessibility check, WCAG audit, or to catch a11y issues before implementation.
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  5. Story Pii Scanner · jdinkla
    PII/privacy scan of a user story — identify personal data collected/processed, assess whether a DPIA is required, map GDPR/CCPA/HIPAA/PCI-DSS obligations, data subject rights, cross-border concerns, and 4–8 privacy recommendations. Use when the user asks about privacy risk, PII handling, DPIA, GDPR compliance, or data protection review.
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  6. Story User Journey · jdinkla
    Map a user story to the broader user journey(s) it touches and surface gaps, broken handoffs, dead ends, or fragmentation. Use when the user asks to check journey fit, find missing steps, understand where a story sits end-to-end, or spot workflow fragmentation.
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  7. Story Nonfunctional · jdinkla
    Extract 6–12 specific, measurable nonfunctional requirements (performance, scalability, security, reliability, accessibility, observability, usability, maintainability) for a user story, with priority and rationale. Use when the user asks about NFRs, quality attributes, SLAs, or "what should this thing do besides the functional part?".
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  8. Story Business Value · jdinkla
    Articulate the business value of a user story across user, business, strategic, and technical dimensions — with evidence, success metrics, and risk of inaction. Use when the user asks "why does this matter?", wants a value statement, needs to pitch a story to stakeholders, or asks for success metrics.
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  9. Story Implementation · jdinkla
    Produce a rough technical implementation plan for a user story — 6–12 tasks organized by phase (Data/Backend, Frontend, Integration, Testing, Deployment) with effort estimates, technical considerations, and risks. Use when the user asks "how would we build this?", wants a task breakdown, or needs an implementation sketch before starting work.
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  10. Story Synthetic Data · jdinkla
    Specify the mock/synthetic test data needed to thoroughly test a user story — entities, scenarios (happy/edge/error/perf), constraints, generation approach, and seed SQL. Use when the user asks "what test data do we need?", wants a data fixture plan, or is setting up a test environment.
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  11. Story Persona Empathy · jdinkla
    Run a Persona Empathy Mapping analysis on a user story — simulate 2–4 distinct user personas and critique whether the story actually solves their real pain points. Use when the user asks to validate a story from user-perspective, run empathy mapping, check who the story serves, or ask "does this actually solve real user problems?"
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  12. Story Failure Scenarios · jdinkla
    Identify 6–10 failure modes for a user story across infrastructure, data, integration, user error, business logic, security, and performance — with triggers, impact, detection, recovery, and prevention. Use when the user asks "how could this fail?", wants resilience/SRE thinking, or is planning graceful-degradation behavior.
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  13. Story Visual Regression · jdinkla
    Predict which existing screens or components may break visually based on the CSS/component changes described in a user story — affected screens, risk levels, affected visual elements, screenshot checklist. Use when the user asks about visual regression risk, which screens to screenshot-test, or what downstream UI to re-check after a style/component change.
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  14. Story Auto Documentation · jdinkla
    Pre-draft documentation updates for a user story — Overview, User Guide, API Reference, Configuration, Changelog — ready for Confluence, Notion, README, or API docs. Use when the user asks "draft the docs for this", wants release notes, or needs markdown-ready documentation before a feature ships.
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  15. Story Feedback Sentiment · jdinkla
    Design a post-release feedback-sentiment-monitoring strategy for a user story — sources, sentiment indicators, categories, alert rules, response templates, and reporting cadence. Use when the user asks how to monitor user reception, track sentiment, handle feedback, or set up post-launch listening.
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  16. Story Stakeholder Impact · jdinkla
    Identify which downstream teams and stakeholders (support, sales, ops, legal, DevOps, security, etc.) need to be informed or involved when a story ships, with communication plan and timeline. Use when the user asks "who else needs to know about this?", wants a rollout comms plan, or is preparing a cross-functional release.
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  17. Story Telemetry Predictor · jdinkla
    Predict exactly which events, metrics, dashboards, and A/B tests to track for a user story — tying each back to the business value it should prove. Use when the user asks "what should we track?", wants telemetry / analytics plans, success metrics, dashboards, or KPIs for a feature.
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  18. Story Alternative Solutions · jdinkla
    Propose 2–4 alternative technical/UX approaches to deliver the same business value, with tradeoff analysis, effort estimates, and a recommendation. Use when the user asks "what are other ways to solve this?", wants to explore options before committing to an approach, or needs a build-vs-buy-vs-hybrid comparison.
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  19. Story Requirements Breakdown · jdinkla
    Split a large user story into 3–7 smaller, independently deliverable sub-stories with acceptance criteria, dependencies, and a suggested implementation order. Use when the user says "this story is too big", asks to split/break down a story, or needs help slicing an epic into sprint-sized pieces.
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  20. Story QA · jdinkla
    Identify comprehensive test coverage for a user story — 8–15 test cases across unit/integration/E2E levels and happy/edge/error/security/performance types. Use when the user asks for a QA plan, test coverage analysis, "what should we test?", or wants a test-pyramid-balanced list of test cases.
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  21. Story Invest · jdinkla
    Evaluate a user story against the INVEST criteria (Independent, Negotiable, Valuable, Estimable, Small, Testable) with pass/partial/fail ratings, concrete suggestions, and a coaching note. Use when the user asks for an INVEST review, wants to score a story, or asks "is this story ready/well-formed?".
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  22. Story Gherkin · jdinkla
    Translate a user story into 2–5 Gherkin (Given/When/Then) BDD scenarios covering happy path, alternatives, edge cases, and error conditions. Use when the user asks for Gherkin / BDD scenarios, Cucumber steps, acceptance test scenarios, or "write this as Given/When/Then".
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  23. Story Kickoff · jdinkla
    Produce a development-readiness checklist for a user story across Clarity, Scope, Dependencies, Design, Technical, and Team dimensions — marking each item ready/needs-attention/blocked. Use when the user asks "is this story ready to start?", wants a kickoff checklist, or is about to pull a story into a sprint.
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  24. Story Summary · jdinkla
    Generate a 30-second quick-reference summary of a user story — one-liner, what/who/why, key features, acceptance summary, complexity indicator, and tags. Use when the user wants a TL;DR, backlog-scanning summary, or handoff-ready synopsis of a story.
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  25. Story Security · jdinkla
    Full application-security analysis of a user story — OWASP Top 10 coverage, XSS/CSRF/IDOR, authn/authz, crypto, input validation, compliance (GDPR/PCI/HIPAA), with 6–12 findings including attack scenarios, severity, mitigations, and CWE/OWASP references. Use when the user asks for a security review, threat model, pen-test input, or OWASP check on a story.
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  26. Story Doc Drift · jdinkla
    Predict where implementation is likely to drift from the original user-story requirements and design detection + reconciliation strategies across READMEs, ADRs, API docs, runbooks, user guides, and changelogs. Use when the user asks about doc drift, keeping docs in sync with code, scope-creep detection, or post-release documentation reconciliation.
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  27. Story Questions · jdinkla
    Surface unknowns, assumptions, and clarifying questions for a user story before implementation begins — categorized and prioritized. Use when the user asks "what's missing from this story?", "what questions should I ask?", or wants to find hidden assumptions before development.
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