LLM Mart Basic

Catches unusual performance changes across your accounts — CPC spikes, CVR drops, spend surges, impression collapses, CTR shifts — and flags them with context about what likely changed. The goal is to catch problems in hours instead of discovering them days later during a routine

Runs your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over

Compares your Meta ad sets and identifies where audiences overlap significantly, causing your ads to compete against each other in the same auctions. Tells you exactly which ad sets are cannibalizing each other and how much it's costing you in inflated CPMs. Platform: Meta.

Analyzes your campaign history, conversion volume, CPA targets, and auction dynamics, then recommends the right bid strategy for each campaign — manual CPC, target CPA, target ROAS, maximize conversions, or maximize conversion value. Not a blanket recommendation, but campaign-by-

Track how AI assistants describe your brand over time and catch wrong, outdated, or damaging claims. Claude builds your monitoring prompt set, compares runs week over week, and drafts the correction plan when answers drift. Platform: AI visibility.

Models what happens to your CPA, ROAS, conversion volume, and impression share when you increase or decrease budget by any amount. Uses your actual account data and historical diminishing returns patterns, not generic industry assumptions. Platform: Google and Meta.

Builds a consistent, filterable naming convention across your Google and Meta accounts based on your campaign types, objectives, targeting, and reporting needs. Makes filtering, reporting, and cross-platform analysis actually work instead of guessing what "Campaign_v3_Final_NEW"

Given your total budget, recommends the optimal split across Google Search, PMax, Meta prospecting, Meta retargeting, and any other active channels based on your last 60-90 days of marginal ROAS and CPA by channel. Tells you where each additional dollar produces the best return.

Find the exact pages AI assistants cite when answering questions in your category, and identify which ones you can get into. Claude clusters the cited sources, scores each by attainability, and outputs an outreach/content plan. Platform: AI visibility.

Takes your raw campaign performance data and writes the executive summary paragraph that goes at the top of the report. The plain English explanation of what happened, why it happened, and what's being done about it. The part every client actually reads. Platform: Google and Meta

Pulls competitor ads from Meta Ad Library and Google Ads Transparency Center, categorizes their messaging angles, formats, CTAs, and creative types, then identifies gaps in their approach you can exploit and patterns worth testing in your own campaigns. Platform: Meta.

Systematic competitive analysis covering positioning, messaging hierarchy, objection handling, and CTA strategy from landing page URLs or screenshots. Use when pasting a competitor URL, uploading competitor screenshots, requesting positioning analysis, or needing to understand co

Rewrite existing pages so AI assistants can extract, quote, and cite them. Claude restructures your content into answer-ready blocks — direct answers, comparison tables, stats with sources, schema — without wrecking the human reading experience. Platform: AI visibility.

Transform one long-form piece into multiple platform-specific content derivatives including LinkedIn posts, tweet threads, email snippets, ad hooks, and video scripts while maintaining voice consistency. Use when given a blog post, article, or pillar content to atomize across cha

Maps out how users move through your funnel from first ad click to conversion. Identifies where the biggest drop-offs happen, which campaigns contribute most at each stage, and how long the typical conversion path takes across different audience segments. Platform: Google and Met

When your CPA spikes, Claude breaks down exactly what caused it. It looks across your campaign data and isolates the contributing factors — audience fatigue, bid landscape shifts, creative decay, landing page conversion drops, budget distribution changes, or new competitor activi

Monitors your ads for early signs of fatigue before performance fully collapses. Tracks frequency buildup, CTR decay, CPM increases, and engagement drops across all active creatives and tells you which ones need rotation now vs next week. Platform: Meta.

Analyzes performance by day of week and hour of day across your campaigns. Identifies when your ads perform best and worst, recommends ad schedule adjustments with estimated savings, and tells you exactly what you'd give up by restricting hours. Platform: Google and Meta.

Analyzes how your campaigns perform across mobile, desktop, and tablet. Identifies where device performance diverges significantly and recommends bid adjustments, campaign splits, or creative/landing page changes to capture the gap. Platform: Google and Meta.

Full SEO workflow covering technical audits, content gaps, backlink opportunities, on-page fixes, and content briefs. Use when given a site and target keywords to get complete SEO analysis and actionable content plans. End-to-end SEO in one skill. Platform: Google.

Fourteen posts of being wrong in production, compressed to checkboxes

Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds

Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.

Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.

A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.

A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.

None of the safety came from the model. It came from six boring habits.

Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.

Six hours in, one step left, everything green, and the incident that didn't happen

CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.

Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it

An API request routing a model's tool call through an approval gate to a remote MCP server

31 config keys, two audits, and why the first one was wrong in both directions

The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.

Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.

Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.

"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks

stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.

The most important rule wasn't about what I could change. It was about what I was allowed to display.

Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.

Repeatable pass upgrading an Angular admin dashboard into a compact black-and-cyan developer-cockpit PWA

Run the drift-detection checklist (incl. agent-drift signals); report + fix in-turn

Orchestrate the final review fan-out (integration + diversity + risk + release readiness)

Run the AI-augmented lint self-improvement loop on the current project. Scans `.lint-history/` for recurring violation patterns (≥3 hits in 30d window), drafts a Claude-ready prompt to author a new semgrep rule for the top candidate, and surfaces the proposal under `.lint-history/proposals/<ts>.md`. Non-blocking analysis. See rules/lint-doctrine.md § Self-improving.

Bootstrap industry-leading lint+autofix+commit-hygiene stack on the current project. Drops in lefthook, oxlint, ESLint, Prettier, Stylelint, markdownlint, ruff, shellcheck, shfmt, yamllint, hadolint, actionlint, jscpd, knip, semgrep, gitleaks, commitizen + git-cz-emoji (emoji-mandatory commits), and semantic-release. Idempotent — re-runs upgrade safely. See rules/lint-doctrine.md.

Surface all retrospective documents with key shape metrics; compare arcs deliberately.

Progressive multimedia enrichment pass — add high-value audio/video/image/interactive to a site, run again and again

Run the autonomous-engineering operating loop on a task (plan→implement→verify→repair→report)

Capture the cumulative output of a /loop arc into a single auditable retrospective document; scans the heymegabyte-claude-skills plugin for modified files, categorizes by directory, counts LOC delta, extracts tool counts from MCP servers, and writes a timestamped report to retrospectives/

Turn a comma-separated list of file paths into a fully structured Pattern A agent brief — ordered writes, per-file schemas, and a verification step baked in.

Turn Pattern B from agent-resilience-discipline into a one-keystroke agent brief for a single-file deliverable < 300 lines.

Chain the full Superpowers process flow — brainstorm → plan → worktree → build → review → finish — on one slash command

Generate a timestamped arc retrospective from the past 7 days of git history in `~/.agentskills`.

Review ~/.claude/CLAUDE.md + rules for contradictions, stale guidance, duplication; consolidate

Batch-run all LLM eval cases in tools/evals/cases/*.json; aggregate pass/fail, cost, regression vs last run; exit nonzero in CI mode

One-line SaaS — from a description, scaffold a complete CF-native multi-tenant SaaS (Hono + D1 + Drizzle + Better Auth + Stripe + shadcn) deployed to a real URL

Unified supply-chain audit. Checks GitHub Actions SHA-pinning (`sha-pin:check`), package.json git+https deps (per `no-gitlab-megabytelabs-deps` semgrep), gitleaks scan, and trufflehog verified-only sweep. Surfaces any tag-mutable, git-URL, or secret-exposed surface. Per rules/ai-agent-security.md § Supply chain.

Run a learning pass after a major run; fold reusable lessons into global config

Summarize recent CHANGELOG.md entries for context restoration. Parses the canonical heading shape `## YYYY-MM-DD — pass-N — summary`. Filters: last N (default 10), YYYY-MM date prefix, or "today". Supports --json for machine-readable output.

Run quality-scores + token-budget + dep-graph, interpret results, flag missing budgets, orphans, and oversize skills

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