Claude Skills by shear559

fabius's persistent-memory layer — lets the agent stop re-deriving: write what it learns into an interlinked markdown knowledge base, retrieve from it cheaply, and keep it healthy (index + log + when-to-add-vector). Use when work spans sessions, when a fact or decision is worth keeping, when the user says "remember this", when a knowledge base is growing, to set up a per-project memory or an Obsidian vault so the next session starts ahead, or whenever the agent is about to redo research it (o...

fabius's on-chain layer — build trustless systems on a blockchain and prove provenance with one. Two jobs under one concern: (1) write and review on-chain code — EVM (Solidity / Foundry / EIP-712) and Solana (Anchor / Pinocchio) programs, wallets, transactions, on-chain reads — account-validation first, money-safe by default; (2) cryptographically SEAL any artifact with verifiable, trustless provenance — a content-bound hash, a signature, and a Bitcoin-anchored timestamp that anyone can re-ch...

fabius's agent-engineering layer — how to DEFINE and ORCHESTRATE other agents: the definition schema, the permission model, the single-vs-multi-agent decision, and the five orchestration patterns (sequential / parallel / hierarchical / human-in-the-loop / swarm). Use when the user wants to build an agent, a subagent, a tool-using assistant, a multi-agent system, a swarm, or an orchestration workflow. A copy-from schema and proven agent shapes live in references/agent-patterns.md; the full age...

fabius's cross-model deliberation layer — convene a COUNCIL of heterogeneous models on ONE question, then aggregate their answers into one evidence-aware synthesis: each model answers independently (first opinions) → each ranks ALL anonymized answers blind, its own included, while the backend removes its self-score (anonymized peer-review) → a chairman synthesizes the field. This is ensemble epistemics — it uses model diversity to expose possible single-model error and bias; whether it improv...

fabius's ship-grade design layer — gives the agent a token vocabulary, the universal laws that separate amateur UI from shipped UI, and a quality checklist. Use when building or reviewing any UI — a landing page (visual execution; the message and copy are fabius-mercatus), a component, a screen, an email, a slide, a brand surface, a chart, a diagram, a data visualization — when the user references a brand look, or asks to make something "look good", "more polished", "production quality", "cha...

fabius's engineering-discipline layer — one repeatable procedure for HOW the agent builds, debugs, and finishes: scope → plan → source/test impact map → strengthen the oracle → prove, plus grilling ambiguity and root-cause debugging. Use before building any feature, fixing any bug, or refactoring — any task bigger than a one-line edit. Also use when the user says "plan this", "grill me", "debug this", "find the root cause", or "is this actually done?". Worked debug walkthrough and test anti-p...

fabius's AI/ML-engineering layer — train, serve, evaluate, and operate machine-learning and LLM systems as production software. The model lifecycle: dataset → train / fine-tune → EVALUATE → serve / infer → monitor. It owns model serving and inference (vLLM-class, OpenAI-compatible endpoints, batching, quantization), MLOps and experiment tracking (MLflow-class), and rigorous model/LLM evaluation (eval harnesses, blind judges, regression gates). It is NOT agent orchestration — that's fabius-coh...

fabius's markets and economics layer — bring method to money: equity and market analysis (fundamental + technical + quantitative), economic data and indicators, financial modeling, backtesting with honest statistics, portfolio construction, and risk-first position sizing. It turns "is this a good stock / what will the economy do / does this strategy work" into a sourced, risk-bounded, falsifiable analysis — never a confident prediction. Use when the task touches a stock / equity / ticker, a m...

fabius's game-craft layer — how to make a small game that's actually fun and actually ships: find the core loop first, add game feel (juice) deliberately, model state as an explicit machine, hold the pixel-art lane, balance one knob at a time, and scope to a jam-sized cut. Use when building a game, a game prototype, a game mechanic, a game loop, a playable, an interactive toy, a sticker/character game, or anything with win/lose/score state — or when the user says "make a game", "the loop isn'...

fabius's automation layer — wire deterministic service-to-service workflows and prove the wiring before it runs live. This is no/low-code integration glue (n8n-class): triggers, nodes, webhooks, scheduled jobs, API-to-API connections, branching, and error paths. It is NOT agent orchestration — that's fabius-cohors. The line: machina wires deterministic steps across SaaS/APIs where each step's behavior is fixed; cohors orchestrates LLM agents whose behavior is generative. Use when the user say...

fabius's go-to-market layer — how to make a thing's value legible and its next step obvious: positioning, the message-to-awareness match, proof over adjectives, a one-action funnel, copy that converts, channel fit, and a smallest-campaign launch loop. Use when writing the copy for a landing page, a launch, an ad, an email, a LinkedIn/X post, a cold outreach, a value proposition, or a pricing/positioning page (the page's visual build routes to fabius-decor; mercatus owns the message) — or when...

The always-on lean core of fabius — one stance, four trims: say less, build less, change less, assume less (terse output + a YAGNI code ladder + surgical, assumption-checked changes). ALWAYS-ON: it sits UNDERNEATH whatever task layer is active (building, refactoring, debugging, designing) — never instead of one, so it never competes for a task verb. Fires on every response and every code change, whenever output drifts verbose, and when the user asks for "lean", "minimal", "simplest", "yagni",...

fabius's defensive-security layer — how to find and fix what an attacker would exploit, before they do: threat-model first (STRIDE per trust boundary), run the OWASP pass, enforce secrets and least-privilege hygiene, review secure-by-default, check the supply chain, and ship every finding with a severity, a fix, and a regression test. Use when building or reviewing anything that touches auth, user input, secrets, payments, file upload, external requests, or dependencies — or when the user say...

fabius's scientific-research layer — the empirical method made executable, for biology and the data-heavy sciences. It runs a real hypothesis loop (clarify → literature → competing, falsifiable hypotheses → experiment design → predictions → report), grounds every factual claim in an authoritative database with cross-identifier mapping instead of guessing, sequences bioinformatics / cheminformatics / omics pipelines as routers over field-standard tools, and enforces the reproducibility gotchas...

Load at the start of any non-trivial task — the router that sets HOW to work before any specialist fires. fabius is a plugin — one set of operating rules loaded above every model (Claude · GPT · Gemini · DeepSeek · GLM · Qwen · Llama · Mistral · Kimi · Grok and any other), never a platform of its own. One stance, end to end: code, prose, agents, UI, data visualization, debugging, marketing, defensive security, games, on-chain work and sealing, automations, scientific research, ML/LLM engineer...