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Found 1,299 Skills
Creates a comprehensive, context-rich implementation plan through deep codebase analysis, a short clarifying interview, and external research. Accepts a tracker ticket (a Jira/Linear/GitHub key or URL, fetched from the tracker) or a free-form feature request. Use when you have a ticket or feature and need a one-pass-ready plan before writing any code.
Use when asked to watch, babysit, audit, review, compare, or fix another agent's work from a Codex session ID, Claude Code session/transcript, chat/thread link, PR, branch, log, or pasted run summary. Monitor until the other agent is done or blocked, reconstruct what the user asked, inspect what the agent actually changed and verified, report gaps, and optionally make scoped fixes when the user authorizes repair.
Iterative diagnose-and-fix workflow that repeatedly uses the diagnose skill to find a proven problem, plan the smallest credible improvement, implement it, re-diagnose, and continue until no useful fix remains or user intervention is required. Requires the diagnose skill; selected installs must also install diagnose. Use when asked to keep debugging, fix and verify, iterate until satisfied, improve after diagnosis, or run a diagnosis/fix/verification loop.
AI Berkshire Skill: Buffett Value Investment Pre-Purchase Checklist. Source: skills/investment-checklist.md.
AI Berkshire Skill: Investment Thesis Tracker: Post-Purchase Discipline System. Source: skills/thesis-tracker.md.
AI Berkshire Skill: Earnings Report In-depth Analysis Team: Parallel Interpretation by Four Masters + Official Account Publishing. Source: skills/earnings-team.md.
Convene a structured LLM Council — five thinking-lens advisors (Red Team, First Principles, Expansionist, Outsider, Executor) plus anonymised peer review, forced debate on consensus, dual-chairman synthesis with dissent preservation, and optional Codex-powered Decision Science pass — to pressure-test high-stakes decisions. Adaptive modes (Quick/Standard/Deep) keep cost bounded; a persistent journal enables learning across runs. Mandatory triggers: /claude-council, "convene the council", "run this by the council", "I need the council", "council this", "pressure-test this", "stress-test this", "war room this", "debate this". Strong triggers: "I'm torn between X and Y", "this is a big decision", "help me think this through from multiple angles", "I need outside perspectives", "should I X or Y" (with real stakes — if binary with obvious answer, triage rejects per Step 1 rule 4). Do NOT invoke for factual questions, coding help, debugging, quick yes/no decisions, emotional support, or questions with one right answer — answer those directly. Optional suffixes: "with codex" enables Decision Science pass; "deep" forces Deep mode; "quick" forces Quick mode. Secondary invocation: /claude-council outcome <sha1> <note> records decision outcome. /claude-council meta runs journal meta-analysis.
Set up state-of-the-art context engineering for any repository. Analyzes codebase, generates multi-level CLAUDE.md/AGENTS.md hierarchy, hooks, session management, and token budgets. Based on 200+ sources including ETH Zurich, Anthropic, Google DeepMind, and Manus production data. For engineers who ship with AI agents.
The full lifecycle for agentic loops — recurring, scheduled AI agents packaged as a portable LOOP.md (the agenticloops.dev standard: a trigger + skills + a prompt in one file any harness can install and run on a schedule). Use this whenever the user wants to FIND, INSTALL, RUN, or BUILD a loop: "find a loop for X", "is there a loop that…", "install a recurring agent that does X", "run this loop", as well as "create a loop", "make an agentic loop", "write a LOOP.md", "turn this into a recurring agent", "schedule an agent", "set up a cron job for an agent", or any description of a repeating job they want an agent to do on a timer (a daily digest, a competitor watcher, a triage sweep, a report pipeline, "email me X every morning", "check Y every hour") — even if they never say the word "loop". Always search the directory first and install an existing loop when one fits; author a new LOOP.md only when nothing does. This is the loop-level analogue of skill-creator + find-skills combined. For an ad-hoc in-session multi-agent run (spawn, verify, panel, fan-out) use the `loops` skill instead; for authoring a reusable SKILL.md use skill-creator.
Extended `5dive` CLI recipes beyond the everyday core — see the `5dive-cli` skill first for spawning/messaging sibling agents and the basic task queue. Use THIS skill for hiring a ready-made persona off the agent market (`5dive market`, `hire --from-market`) or firing one (`5dive fire`), auth recovery (`error.class=auth_required`, `--defer-auth`, device-code login via `agent auth start/poll/submit`), BYO-provider agents (`--provider`), multi-account auth (`5dive account`), declarative fleets and company templates (`5dive up/down/ps/export`, `team import`), hosting a CrewAI crew (`5dive crew`), controlling agents on OTHER registered boxes (`5dive fleet`), recurring/scheduled work (`task add --recurring`, `5dive heartbeat`) and projects (`5dive project add`), building or editing multi-agent loops — a relay with optional human gates (`task loop start`) or a maker→verifier review loop (`task add --verifier`, `task reject`, `5dive loop` LOOP-7 verbs) — decomposing an outcome into a guardrailed task DAG (`5dive goal add`) or a self-steering objective bound to a live metric (`5dive objective`), compiling durable knowledge into the shared wiki (`5dive memory add`), org-chart writes (`5dive org set`), convening a governance vote (`5dive council`), reading fleet health / token burn / the daily standup (`5dive supervisor`, `5dive usage`, `5dive digest`), a machine-readable health check (`5dive doctor --json`, `5dive selfcheck --json`), a task's causal history (`5dive trace`), the current model id per alias (`5dive models`), Telegram/Discord pairing and shared team-bot setup, a delegated GitHub push-for-review (`5dive push`), or the onboarding wizard (`5dive company`).
Helps users discover and install agentic loops (recurring, scheduled AI agents) when they ask "find a loop for X", "is there a loop that…", "install a recurring agent that does X", "schedule an agent to do X", or want a repeating job run on a timer (a daily digest, a competitor watcher, a triage sweep, an every-morning report) — even if they never say the word "loop". Use this to SEARCH the agenticloops.dev directory and INSTALL an existing loop. This is the loop-level analogue of find-skills. For AUTHORING a new loop when none fits, use the fuller `loops` skill.
Spawn parallel adversarial subagents that DRIVE THE REAL SYSTEM (not reason from source) to break new code before it ships — schemas, contracts, parsers, serializers, protocols, state machines, security/redaction, freeze guards. Use as a standard step on any nontrivial feature/task once the implementation is green and BEFORE the final convergence (dootsabha) review. Each agent gets one disjoint attack surface + a "try to break X" charter; every finding is independently reproduced before it is believed, then fixed with a regression test. Trigger phrases include "adversarial review", "try to break this", "find the bugs I missed", "harden before done", "attack this schema/parser/contract", and the shux feature-protocol adversarial step.