Answer a hard question by building a persistent graph of atomic claims, each given a 0-1 credence by Jev from a shared evidence ledger. Keeps false and unexamined claims. Use for research questions, codebase questions, hypothesis testing, or any claim that…
Use when asked to crystallize a workflow, skill, codebase, or agent runs, or to find where agents repeatedly rediscover methods, apply stable rules, or do unnecessary work. Analysis of opportunities to replace repeated agent effort with reusable procedures;…
Idempotent revision pass for literary and nonfiction prose. Invoke with /remove-ai-sins. Removes seven craft sins only — the not-X-but-Y frame, naming an emotion or theme the detail already carries, explaining an image or joke, inflated register, a summing-up…
Show rate-limit and quota usage across the user's AI coding subscriptions (Claude, Codex, Gemini, Kimi, MiniMax, Z.ai, Qwen, Grok) with per-window percent used and time to reset. Use when the user asks about usage, quotas, rate limits, or when subscriptions…
Build with Jev, TypeSafe's System One model — fast structured judgments (Choice, Score, Noul) that code consumes directly. Use when replacing fragile parsing or prompt-and-parse LLM steps with typed decisions, or when designing routing, ranking,…
Explain, compare, or choose machine-learning objectives and learning regimes, including masked prediction, self-supervision, generative modeling, reinforcement learning, transfer, and classical methods. Use for ML learning-method questions and training-design…
Use when writing or rewriting nonfiction prose for readers — essays, blog posts, articles, handbooks, newsletters, threads, intros, or any draft that must hold a stranger's attention. Covers choosing the topic, writing a hook-first intro, outlining,…
Probe a bounded target from many angles at once — audit for hidden bugs/stale state, stress-test a claim or plan, or brainstorm wide. Falsifiable or generative probes through explicit lenses, each closed with evidence.
Use when a bug, regression, or misbehavior has an unknown cause and the user wants iterative hypothesis-driven investigation — invoked by name ("prediction drill", "drill into this bug"), or when asked to falsify hypotheses and converge on a root cause and…
Run a GPT Pro / pro-cli project revision loop: context package, GPT Pro prompt, adversarial review, design map, revision passes, local verification, goal assessments, handoff.
Chained clarity review of a repo, plan, or work items — combines code review, plan refinement, deep-module architecture, DRY/cruft, and naming analysis. Use for clarify/defuzz/audit/harden/fresh-eyes requests.
Use when a consequential design, architecture, process, or policy choice depends on unknowns that reasoning alone cannot settle, or when an investigation plan may be over-scoped, prematurely branched, or full of experiments whose results may not change a…
Deep web research with Graph-of-Thoughts exploration — parallel branching, relevance scoring, knowledge-graph storage, synthesis into themed reports. Use for researching topics, companies, people, or concepts.
Price-blind deep research for a Polymarket/prediction-market event or PMKNB situation — fresh or updated analysis, resolution mechanics, mechanism-only briefs, source discovery, 0-100 likelihood independent of market odds.
Use when auditing, scrutinizing, or strengthening an existing test suite — unit, integration, or e2e — because tests pass but bugs still ship, coverage looks high while trust is low, tests were written after the code or by another agent, tests are flaky and…
Become Diana. Ukrainian software engineer in NYC. KGB accent. Zero filter. Real corpus mode with full main tweets and top-ranked replies. Once invoked there is no going back.
Use when designing or operating Duris group combat with tanks, assists, assumed targets, caster memorization readiness, bash risk, tracking enemies, aggro adds, or command spam risk.
Use when reviewing a codebase, uncommitted changes, recent agent work, or one subsystem for hidden bugs, flawed assumptions, drift, duplication, weak tests, broken contracts, or other failure modes.
Context and working knowledge for the Kalshi prediction-market domain. Use this skill whenever the user asks about Kalshi markets, tickers, order books, pricing, settlement, or the Kalshi API/WebSocket.
Use when reviewing or designing names for plans, beads, work items, architecture docs, codebases, APIs, schemas, modules, states, or domain vocabulary where terminology drift, overloaded concepts, boundary ambiguity, or AI-confusing naming may exist.
Use when user asks to create, write, edit, or test a skill. Also use when documenting reusable techniques, patterns, or workflows for future Claude instances.
原文の言語: 英語
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収集済み skill 31 件中 31 件を表示しています。
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