Claude Skills by markoblogo

Design, audit, or improve agentic systems and agent harnesses. Use for agent MVP blueprints, tool design, permission models, planning and goal loops, context compaction, memory, skills, connectors, observability, evals, safety, and provider-neutral agent architecture.

Review a codebase for architecture deepening opportunities: shallow modules, weak seams, scattered domain logic, low testability, high coupling, and AI-navigability friction. Use when the user asks for architecture improvement, refactoring opportunities, modularity, test seams, or maintainability review.

Convert books, papers, long documents, or prior reading notes into compact reusable agent skills. Use when the user provides a PDF, EPUB, DOCX, HTML, Markdown, text file, article bundle, or structured notes and wants reusable workflows, mental models, frameworks, checklists, anti-patterns, or a public/private SKILL.md artifact rather than a summary.

Create a single working brief before substantial implementation, audit, or migration work. Use when tasks are non-trivial, risks are real, or the session is likely to drift without one source of truth for scope, non-goals, verification, and done criteria.

Verify frontend changes in a real browser, including layout, console errors, responsive states, network failures, screenshots, and interactions. Use after local or deployed web UI changes, visual fixes, forms, navigation, asset updates, canvas or 3D work, or whenever browser verification should replace static guessing.

Preserve working state across long sessions and context compaction by checkpointing decisions, edited files, blockers, open hypotheses, and unresolved errors into compact resumable artifacts. Use on long coding tasks, audits, research, or any session at risk of losing state after compaction.

Analyze or improve codebase complexity and performance hotspots without broad rewrites. Use when reviewing nested loops, repeated scans, N+1 queries, rendering churn, excessive recomputation, slow tests, large-input paths, memory pressure, or requests to reduce complexity while preserving behavior and tests.

Audit claimed delivery against the starting baseline and the current working tree. Use when a task has declared deliverables, when a long run might say 'done' before shipping real changes, or when final verification must check the actual artifact set rather than only the transcript or commits.

Run a baseline preflight before long implementation or autonomous execution. Use when a task has multiple phases, mandatory commands, or high risk of thrashing against an already-broken repo state. Deduplicate the minimum useful checks, surface red baseline conditions early, and require an explicit user decision before proceeding past known breakage.

Critique and polish frontend interfaces before shipping, with focused review of hierarchy, typography, spacing, states, color, and UX clarity. Use when a page, component, or app surface feels unclear, inconsistent, flat, awkward, under-tested, or not yet production-grade and needs design verification instead of a broad rewrite.

Establish frontend design context before implementation. Use when a project lacks a clear PRODUCT.md or DESIGN.md, when the model needs to distinguish brand surfaces from product UI, or when design direction keeps drifting because audience, register, anti-references, and visual constraints are not explicit.

Create, inspect, and apply DESIGN.md brand kits for brand-aware frontend work. Use when building or reviewing landing pages, frontend UI, pitch pages, redesigns, client audits, brand-matched prototypes, or researching a potential client/company where website identity, design tokens, typography, layout, components, or do/don't guidance should shape the output.

Debug coding failures through reproduction, ranked hypotheses, narrow fixes, and verification. Use when a bug, regression, flaky test, performance failure, or inconsistent behavior is broken, slow, throwing, or hard to reproduce and the task needs disciplined diagnosis instead of guessed fixes.

Stress-test a plan, feature, or design against the repo's existing docs, domain language, ADRs, and context files. Use when a task is ambiguous, terminology is fuzzy, architecture trade-offs matter, or the user wants alignment before implementation. Ask one question at a time, inspect the codebase when answers are already present, and sharpen the project's language as decisions crystallize.

Keep repo-local agent context accurate after code and workflow changes. Use when docs/ai, AGENTS.md, runbooks, or context entrypoints have drifted from the codebase, after major feature work, after architecture changes, or when an agent keeps rediscovering the same repo facts. Refresh durable context, split groups only when needed, and preserve compact read order.

Plan and run large Codex tasks with compact dynamic workflow packets, risk gates, integration, and verification. Use when the user explicitly asks for dynamic workflows, packets, subagents, swarm, orchestration, long-running audits, multi-track coding/research work, job or client search agent design, migrations, repo-wide reviews, or tasks where independent discovery, implementation, review, and verification tracks would reduce drift.

Research and answer evidence-sensitive questions with source, date, unit, and operand discipline. Use for web research, PDF/document QA, policy or finance facts, tables, time series, calculations from retrieved evidence, exact-answer extraction, and any task where wrong source selection, stale facts, nearby table confusion, or formatting errors would matter.

Separate workflow instructions from safety, authority, escalation, and validation rules. Use when an assistant, agent, or skill pack is getting muddled because process steps, permissions, forbidden actions, and review gates are mixed together.

Audit and improve web quality across accessibility, performance, UX, privacy, and browser security. Use for frontend audits, client audits, redesigns, landing pages, production-readiness passes, Core Web Vitals, keyboard navigation, privacy reviews, CSP/cookie/header checks, and responsive UI verification.

Audit where tokens are being wasted across startup context, shell output, repeated file reads, bloated agent docs, oversized summaries, and compaction loss. Use when token budget is tight, sessions are degrading, or an agent setup needs measurement-driven optimization.

Reduce output-token usage without losing technical accuracy. Use when the user asks to save tokens, be terse, stay brief, use caveman mode, use compressed answers, minimize chatter, or when the session is context-tight and the task does not need long prose.

Reduce token waste in long coding-agent sessions by avoiding repeated reads, noisy output, oversized context, broad rewrites, and unnecessary narration. Use on long tasks, heavy repos, multi-file audits, shell-heavy debugging loops, or whenever token budget is a practical delivery constraint.

Execute feature work and bug fixes with a tracer-bullet red-green-refactor loop. Use when building behavior that should be guided by tests, when fixing regressions, or when the user wants TDD or test-first execution. Prefer public interfaces, vertical slices, and behavior-focused tests that survive refactors.

Go up one layer of abstraction and explain how a local piece of code fits into the larger system. Use when the current focus is too narrow, the user is unfamiliar with an area, an audit needs broader context, or a refactor should be explained through modules, seams, and callers instead of line-by-line detail.

Edit, repair, or generate spreadsheet workbooks with structure-preserving Python. Use for Excel or spreadsheet tasks involving .xlsx/.xlsm files, formulas, lookup tables, summaries, formatting preservation, target ranges, workbook QA, or any task where cells must be computed, written, and verified rather than guessed from previews.

Turn the current conversation, repo understanding, and clarified decisions into a PRD. Use when a vague feature, client request, internal roadmap item, or discovery thread needs to become a durable product artifact. Prefer repo/domain language, explicit scope, implementation decisions, testing decisions, and issue-tracker-neutral output.

Evolve Codex skills and agent instructions with a SkillOpt-style loop. Use when creating, refining, or auditing local skills, AGENTS.md rules, prompt playbooks, or reusable agent workflows after repeated task experience, especially when the user asks to "improve your skills", "capture this lesson", "make this reusable", or "update agent instructions".

Reduce shell and tool-output token waste by preferring targeted commands, diff-only views, error-first logs, narrow slices, counts, and concise summaries instead of large raw stdout dumps. Use on coding, debugging, audits, CI triage, or repo exploration when command output is likely to dominate token usage.

Reduce shell output token waste on git, file reads, searches, test runs, linters, logs, and other noisy developer commands with RTK-style filtering. Use when shell-heavy work is flooding the session with logs, diffs, or command output that should be compacted before it reaches the model.

Design role-specific or workflow-specific agent skill packs without collapsing everything into one prompt. Use when creating assistant layers for teams, products, functions, or operating roles. Define base-vs-difference layers, pack boundaries, references, triggers, and rollout order.

Triage bugs, enhancements, and backlog items through a small state machine that works with or without a formal issue tracker. Use when reviewing incoming requests, preparing work for an agent, deciding whether more info is needed, or keeping a multi-project backlog healthy.

Debug repositories with a hypothesis ledger, checked locations, and loop-breaking discipline. Use when tests fail, CI fails, behavior regresses, local tooling errors, flaky bugs appear, or an investigation risks repeated searches, repeated commands, circular hypotheses, or unverified fixes.

Recover from failed execution or verification with a bounded three-step loop: retry, focused fix-spec, then honest handoff. Use when an agent is likely to spin on the same failure, when flaky environment issues blur the signal, or when you need a disciplined stop condition instead of endless optimistic retries.

Lightweight grilling session for brainstorming, product clarification, and early design shaping. Use when the user wants fast alignment but there is not yet enough repo context to ground a heavier doc-based review. Ask one question at a time, propose a recommended answer, and keep momentum high.

Build throwaway prototypes to answer a specific product, UI, state-machine, data-model, or workflow question before committing production code. Use when the user asks to prototype, explore variants, sanity-check logic, try designs, make a playable mock, or learn quickly with code that will be deleted or absorbed.

Bootstrap usable project context before deep implementation work. Use when entering a new or unfamiliar repo, installing agent workflows into an existing codebase, auditing stale docs, or preparing a project for repeatable long-running agent work. Detect the stack, ask the user about the project, summarize understanding, propose compact context artifacts, and update only with approval.

Audit a private skill, prompt pack, or assistant workflow before publishing it. Use when extracting public reusable method from client, product, team, or repository-specific agent instructions. Mark private vs mixed vs reusable content, rewrite the mixed layer, and gate release on decontextualization.

Break a PRD, plan, or spec into independently executable vertical slices. Use when a product artifact needs to become agent-ready or human-ready implementation tickets, especially for client, product, and multi-track project workflows. Prefer thin end-to-end slices, explicit blockers, and issue-tracker-neutral output.

Review code or diffs specifically for needless complexity, replaceable dependencies, dead flexibility, and wrappers over stdlib or native platform behavior. Use when the user asks what can be deleted, simplified, inlined, replaced with stdlib/platform features, or whether a change is over-engineered.

Build the smallest correct code diff without overengineering, broad refactors, unnecessary dependencies, or speculative abstractions. Use when the user wants a minimal patch, small reviewable diff, stdlib-first implementation, or shortest-path fix that still preserves security, accessibility, trust-boundary validation, and data safety.

Build or refine small production-ready Lottie animations from SVGs, logos, UI states, loaders, and branded motion assets with a local preview harness, explicit inputs, and output verification. Use when the user asks for Lottie, JSON animation, SVG reveal animation, logo animation, or lightweight motion assets for product surfaces.

Design and evolve agent skills into cost-bounded loops, workflows, scripts, and memory updates. Use when creating or auditing reusable agent methods, converting prompts or skills into workflows, designing open or closed agent loops, defining supervisor/evaluator contracts, or deciding whether repeated agent work should become a skill, checklist, script, workflow, or autonomous loop.

Restructure agent-facing docs so the always-loaded context stays small and the rest is loaded on demand. Use when a repo has bloated AGENTS.md or CLAUDE-style docs, too many always-loaded notes, stale session history, oversized runbooks, or high startup token waste for coding agents.

Build or improve frontend pages, apps, landing pages, pitch pages, redesigns, dashboards, and prototypes with strong hierarchy, responsive layout, real visual assets, ergonomic controls, and verification. Use when frontend quality, brand fit, product clarity, or design implementation matters.