Top Agent Skills

Read-only queue dashboard for workspace-aware ship. Shows which VERSION slots are currently claimed by open PRs, which sibling Conductor workspaces have WIP work likely to ship soon, and what slot /ship would pick next. No mutations — just a snapshot. Use when asked to "landing report", "what's in the queue", "show me open PRs", or "which version do I claim next". (gstack)

Orchestrate multi-agent coding tasks via Claude DevFleet — plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports.

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Land and deploy workflow. Merges the PR, waits for CI and deploy, verifies production health via canary checks. Takes over after /ship creates the PR. Use when: "merge", "land", "deploy", "merge and verify", "land it", "ship it to production". (gstack)

Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, P

Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).

Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.

Generate test report. Use when user says "test report", "results summary", "test status", "show results", "test dashboard", or "how did tests go".

This skill should be used when working with Obsidian files including .md notes with wikilinks/callouts/properties, .base database views with filters/formulas, or .canvas visual diagrams. Routes to specialized sub-skills based on file type and task context. Features self-iterative learning that improves with use.

Performance baseline measurement and regression detection. Use when measuring perf before/after a PR, setting up baselines, investigating "feels slow" reports, validating launch performance targets, or comparing your stack against alternatives.

uv Python package manager — fast pip/venv replacement. Use when creating virtual environments, installing packages, managing dependencies, or running Python scripts with uv.

Routes tasks to locally installed CLI tools using semantic matching. Triggers on tasks requiring shell commands, file operations, code search, data processing, visualization, or external tool invocation. Uses cli-index for semantic routing.

A model in production is never just weights.

Plan before implementing - understand scope and approach with detailed guidance

**Monthly Partitions:**

Apply Arrange-Act-Assert pattern for clear, maintainable tests with detailed guidance

Comprehensive process for discovering and validating data model requirements before design

"Multi-cloud" is a loose word that hides four distinct architectures with

Debugging is the scientific method under time pressure.

The purpose of a postmortem is to change the system, and that only works if people

A VPC is a private IP space you own inside a region.

Before writing anything, answer three questions: who reads this, what do they

**Level 1: Executive Dashboard**

API Security (Minimal)

rate(http_requests_total[5m])

Disaster recovery has exactly two quantitative inputs, and every architectural

Both directions produce working layouts, but very different amounts of CSS.

Run this before anything else.

Comprehensive checklist for documenting follow-up work and testing needs after implementation

A git SHA pins the transformation.

Deterministic LLM-first SEO audits for websites, blog posts, and GitHub repositories. Use this when the user asks to "perform SEO analysis", "run SEO audit", "analyze SEO", "check technical SEO", "review schema", "Core Web Vitals", "E-E-A-T", "hreflang", "GEO", "AEO", or GitHub repository SEO optimization. For full/page/repo audits, run bundled scripts for evidence and return prioritized, confidence-labeled fixes.

OWL v5.2 — Pure contrarian. One scanner, one thesis: the crowd is wrong. Monitors crowding across top 30 assets (funding extremity, OI concentration, SM tilt). When crowding persists 4+ hours AND exhaustion signals fire (volume declining, price stalling, RSI divergence), enters AGAINST the crowd to ride the liquidation unwind. 1-2 trades per day max. Re-crowding exit: if the crowd comes back, thesis is dead, exit immediately. DSL High Water Mode (mandatory). The patient predator. v5.2: funding floor lowered from 20% to 12% so the five-factor scoring model actually runs. Added observability logging (top 3 crowding scores per scan cycle).

HYDRA v2.0 — Squeeze Detector. Finds crowded trades about to unwind. Funding extreme + SM positioned against the crowd + price starting to move. Goes opposite to the funding crowd. Only liquid assets ($20M+ volume). DSL exit managed by plugin runtime via runtime.yaml.

This skill should be used when the user asks about "my balance", "portfolio", "token holdings", "check balance", "how much do I have", "wallet balance", "what tokens do I own", "show my holdings", or any balance/portfolio query. Provides guidance on checking balances across chains.

This skill should be used when the user asks about "agent profile", "create profile", "update profile", "project profile", "bankr.bot/agents", "profile page", "project updates", or any agent profile management operation.

This skill should be used when the user asks to "swap tokens", "exchange ETH for USDC", "buy DEGEN", "sell tokens", "swap on Base", "trade crypto", "convert ETH to WETH", "exchange tokens", "token swap code", "0x routing", or any token swap operation. Also use for questions about ERC20 approvals, allowanceTarget, swap transaction execution, or building swap transactions with the Bankr SDK.

MoonPay PayBox payment vault for AI agents — official MCP at https://api.paybox.sh/mcp, OAuth 2.1, scoped credentials, and Cheshire mcp-server proxy tools (get_paybox_*).

Entry-point skill for Phoenix perpetuals through Vulcan/Rise SDK inside solana-clawd. Use before answering or acting on Vulcan, Phoenix DEX, Solana perps, paper trading, live trading, margin, TP/SL, TWAP, grid, TA strategies, or perps agent setup.

Discord Actions

Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.

Control Eight Sleep pods (status, temperature, alarms, schedules).

Use this skill for any request involving cryptocurrency market data, coin prices, trading volume, market cap, OHLC charts, historical data, exchanges, derivatives, NFTs, DeFi, on-chain token data, liquidity pools, DEX data, or anything powered by CoinGecko or GeckoTerminal APIs. Trigger this skill whenever the user asks about crypto prices, token data, market trends, or wants to call any CoinGecko or GeckoTerminal endpoint — even if they don't explicitly say "CoinGecko". Also trigger when the user asks about API keys, rate limits, or authentication for these APIs.

Run Codex CLI, Claude Code, OpenCode, or Pi Coding Agent via background process for programmatic control.

Make paid API requests to x402-protected endpoints. Automatically handles payment with your local wallet.

Search and analyze your own session logs (older/parent conversations) using jq.

Prediction market arbitrage & alpha scout. Searches Polymarket and Kalshi for the same event, runs cross-platform arb math (including fees), and ranks opportunities by profitability. Use when asked to "find arb", "scout markets", "find edge", or scan a specific topic across prediction markets.

Evaluate Google AI Overview presence and click-cannibalization risk for BID-passing Pleasur.ai Stage 01 keyword candidates using current Ahrefs evidence. Use after BID validation and before prioritization, in either presence-only or human-approved deep-completeness mode.

Bind content to live product proof and an accountable author voice.

Produce an answer-first outline that makes the research decision executable.

Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments. Triggers Greptile review, fixes all actionable comments, pushes/re-shelves, re-triggers review, and repeats. Use when the user wants to fully optimize a PR/MR/CL against Greptile's code review standards.

Route one Paperclip child stage of the PLE new-content routine, producing hash-verifiable evidence and a fail-closed handoff.

Knowledge base from "Oversubscribed: How to Get People Lining Up to Do Business with You" by Daniel Priestley. Use when applying Priestley's frameworks for demand generation, the Campaign Driven Enterprise (CDE) method, signalling, the 7-Hour Rule, products-for-prospects, becoming oversubscribed, or building a Key Person of Influence brand — or when studying or referencing the book.

Layer 1c of the keyword research pipeline — SKIPPED on the current Semrush MCP (it exposes no AI-visibility / AI-toolkit reports, so there is no multi-engine citation-gap source). Retained as a logged no-op pass-through; never fabricate aio_gap rows. Revisit only if Semrush ships AI-toolkit reports on MCP.

Off-page playbook (Lesson 10, outreach). Turns a vetted link-prospect list into them-focused outreach drafts that earn links without spamming. Picks the RIGHT people (those who LINKED to or MENTIONED our topic — never "people who tweeted it"), gives each a real "excuse" (fresh-angle / new-proof / ego-bait), keeps to ≤1 follow-up, and never makes a pushy link ask. Drafts only — every outward send is operator-gated. Outreach is a tool, not a strategy.

Compare an existing article's section coverage to the current SERP (Semrush phrase_organic + Firecrawl page extraction) and to the related-term / question landscape (phrase_related + phrase_questions). Find topics ranking pages cover that the article doesn't, and propose new sections.

Skeptical pushback on the outline before drafting starts. Asks whether the outline is MECE, BLUF, problem-agitate-solution; whether it covers the topic better than the SERP top-5; whether sections duplicate; whether each section earns its visual. One revision pass on FAIL (BLOG_AGENT_OUTLINE_REVISION_BUDGET, default 1).

Layer 1a of the keyword research pipeline. Generates seed keywords (broad niche terms) and modifiers (best, how-to, vs, calculator, etc.) from brand-config so the keyword multiplier has a real foundation to expand from. Replaces the "AI gives you generic keywords" problem with the structured 10-second hero prompt.

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).

After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.

Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.