一键导入
ops-and-infrastructure
Machine investigation, deployment, Ansible, SSH, 1Password, Notion — read for any infrastructure/ops/server-management tasks
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Machine investigation, deployment, Ansible, SSH, 1Password, Notion — read for any infrastructure/ops/server-management tasks
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Dispatch a foreign (non-Anthropic) model as a subagent — OpenAI Codex/GPT-5.6-Sol, DeepSeek V4-Pro, or Google Antigravity — either as a write-authorized agentic worker in a worktree or a read-only reviewer. Codex and DeepSeek explore, edit when authorized, and self-report; Antigravity is a single-packet headless reviewer. Use when you (the conductor) want to offload agentic work to another harness, get a cross-lineage second opinion, or run a correlated-blind-spot check same-lineage Claude subagents can't give. This is the dispatch/wrapping layer — how to split work across foreign models of unequal intelligence, prompt each to its level, and wrap them safely to save tokens. Also where adversarial-crosscheck's foreign lane routes; not every dispatch is a crosscheck.
Take a prompt or task the user is about to give Claude — especially a judgment they're invested in ("is this plan sound?", "review my design", "should we adopt X?", "poke holes in this", "am I fooling myself here?") — and run it through two opposing rewrites in clean contexts (a neutralised pass and a disowned-and-inverted adversarial pass), then present both for the user to reconcile. Use this whenever the user wants to pressure-test their own thinking, worries they're being flattered or agreed-with, asks to "red-team", "cross-check", "steelman against", or "sanity-check" something, or hands over work they clearly want to succeed and asks for an honest assessment. It counters sycophancy by widening coverage — it does NOT produce a single "unbiased" or "debiased" answer, and it deliberately refuses tasks it can't honestly automate.
Deep, source-graded investigation for substantial questions — casts a wide net of primary sources, grades each for quality and relevance, and works from the graded base instead of guessing. A good default for most questions worth more than a snap answer: tool/library/architecture choices, approach tradeoffs, trustworthiness or maintenance judgments, root-causing unfamiliar behaviour, or any "which way and why" that rewards real evidence.
Reviews prompt-engineering artifacts (SKILL.md files, CLAUDE.md, AGENTS.md) against a built-in checklist of effectiveness criteria for steering Claude, and proposes only substantively-justified edits with a brief rationale per change. The criteria carry a "criteria-as-of" date; when stale (>180 days), the audit still runs against the current criteria but recommends the user trigger the (separate, heavyweight) refresh procedure in a fresh max-effort session — refresh is never run inline. Explicitly designed to be token-expensive and slow; compounding value because the audited artifacts are read by Claude across many future sessions. Read after writing or substantively editing any SKILL.md, CLAUDE.md, or AGENTS.md.
The user's idiosyncratic commit-message style — `(label1 label2) terse message` with the mandatory `AI` tag for AI-authored commits, using the `.gitlabels` convention. Two-mode operation — autonomous-commit on `ai/*` branches (or with a `.claude-commit` sentinel), message-only on others. Read whenever drafting a commit message, staging a commit, or finishing a chunk of work that will produce a commit.
End-of-work hygiene pass for AI-edited code, focused on stripping the overly-verbose code comments that AIs habitually generate. The user wants ~90% of AI-added comments deleted, the remaining ~10% rewritten for brutal brevity. Read this before declaring an autonomous task complete, before drafting a commit on substantive edits, or whenever the user asks for a "review pass", "cleanup", or "wrap-up".
| name | ops-and-infrastructure |
| description | Machine investigation, deployment, Ansible, SSH, 1Password, Notion — read for any infrastructure/ops/server-management tasks |
You are advisory. You do not have SSH access and should not attempt to mutate remote machine state. Produce investigation scripts and runbooks as local files the user can review, modify, and execute themselves.
The user considers themselves to still be learning ops and security best-practices; and uses the tasks you're participating in for learning purposes. Always be critical of existing state and practices; and never simply follow existing patterns if there's an obvious criticism or improvement. The user can tell you during the session if they're deferring a particular improvement.
Infrastructure state lives across four systems with distinct roles:
SSH / interactive investigation: the ground truth for actual machine state. Ansible facts and Notion notes may be stale or wrong; when it matters, verify via read-only commands on the machine. Batch commands into short shell scripts the user can copy-paste or pipe over SSH. Write these as in-tree files (not chat output) so the user can review and modify before running.
Ansible: the intended, directed state. Encodes optimistic configuration: packages, users, security hardening, Dokku apps, service setup. Does not encode secrets or anything that benefits from genuine security; but note that some sensitive things will be public, as this is intentionally published to GitHub. Security must be enforced directly, not with security-through-obscurity. Not all machines have had all roles applied — check before assuming.
1Password: secrets and credentials. Connection strings, API keys, SSH keys,
database passwords. Reference by vault/item ID, never hardcode values. Use op run for injection into commands/Ansible. New secrets created during work
should also be stored in 1Password, following existing patterns. (Again, the
user is not a security expert; advise on best practices when you see issues.)
Notion: human-managed notes, plans, tips, topology, and context that doesn't fit in Ansible or 1Password. Includes: machine pages with connection info and debugging notes, task tracking, DNS/network topology, discovered tricks (e.g. switching to caddy's alpine image for shell access). Search Notion for machine context when investigating infrastructure — it often contains hard-won debugging knowledge that isn't encoded anywhere else. Still, treat as possibly-stale supplementary context, not authoritative state. Keywords for searching are often machine-names; package names and CLI command binaries; DNS hosts; and so-on.
When investigating remote machines:
set -e — as they're read-only, investigation scripts should keep
going through failures, using || echo "(failed)" fallbacksdocker commands typically need sudo on hardened machinesell.io (DNSSEC-signed), with.ec, and others — managed
outside Ansible (again, there's a Notion database)