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/ai-tools-architect

Design and audit agent instructions, skills, plugins, subagents, prompts, hooks, and provider configuration. Use for AI-tool setup, authoring, or context and routing problems.

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khanelinix
34750 skills10 agents6 commands
Install
$ npx -y skills add khaneliman/khanelinix --skill ai-tools-architect --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/ai-tools-architect

Context preview

The summary Claude sees to decide when to auto-load this skill.

Design and audit agent instructions, skills, plugins, subagents, prompts, hooks, and provider configuration. Use for AI-tool setup, authoring, or context and routing problems.

SKILL.md

ai-tools-architect.SKILL.md
name: ai-tools-architect
description: Design and audit agent instructions, skills, plugins, subagents, prompts, hooks, and provider configuration. Use for AI-tool setup, authoring, or context and routing problems.

AI Tools Architecture Playbook

Keep shared instructions, skills, plugins, and agents focused on the decisions they own, portable across hosts, and written as playbooks. Load detail when the task needs it, and use bundled scripts for exact, repeated mechanics.

Standards

Apply these tests to every instruction file, rule, skill, plugin, and agent you design or review, in any repository:

1. **Contributor docs own repository conventions.** Repository agent files point to `CONTRIBUTING.md`, `docs/`, or the style guide instead of copying them, and require reading those sources before changes. 2. **Portable by default.** A skill, plugin, or agent must work outside the repository that hosts it:

  • Carry the recipes, constraints, examples, and source links it needs; do not

trim it because one repository's instructions repeat them. Declared skill dependencies can supply shared methods.

  • Never depend on host-repository paths, user-home paths, account or tenant

names, or personal data. The audit flags the first two.

  • Use the portable frontmatter fields and put host-only settings in host

sidecars; scripts run on Linux and macOS, handle Windows where feasible, and degrade when an optional tool is missing.

  • Mark host-bound tooling as host-only and keep it out of publication.

3. **Playbooks, not research notes.** Write rules, an ordered workflow with completion criteria, and command reference. State each fact or limit directly. Leave out how a conclusion was reached, test counts, session details, and dates that will go stale; collect unverified behavior in one short list. 4. **Explain what changes the decision.** Skip generic introductions and what the model already knows. Keep unfamiliar constraints, current API behavior, environment quirks, choices among valid approaches, and known failure modes. 5. **Remove duplication where sources load together.** State shared guidance once at the right scope and link it from narrower files. Keep what a separately installed skill needs to stand alone. 6. **Use tools for mechanics and prose for judgment.** Prefer scripts or CLIs for fragile or repeated queries, transformations, validation, and mutations. Keep selection, ambiguity resolution, and interpretation in the playbook.

Before changing these standards, or when a host behaves unexpectedly, check the upstream sources in [refs/UPSTREAM.md](refs/UPSTREAM.md). Its local rules override upstream defaults where they conflict.

Audit

Run the structural audit for a tree-wide review or any change to skill discovery, packaging, or publication:

python3 <skill-dir>/scripts/audit_ai_tools.py <ai-tools-root> --format markdown

The audit is read-only. Errors are objective structural failures; warnings, including the portability checks, are review candidates. Review implicit and explicit-only skill counts before adding another automatic trigger; the description budget is a repository setting, not a universal host limit. After changing canonical skills, run every bundled unit suite:

python3 <skill-dir>/scripts/run_skill_tests.py <canonical-skills-root>

Component References

Read the reference whose constraints affect the task:

  • **Skills** (reusable workflows): [refs/SKILLS.md](refs/SKILLS.md)
  • **Plugins and marketplaces** (distribution):

[refs/PLUGINS.md](refs/PLUGINS.md)

  • **Subagents and custom agents** (context and tool boundaries):

[refs/SUBAGENTS.md](refs/SUBAGENTS.md)

  • **Path-gated rules and instruction files**: [refs/RULES.md](refs/RULES.md)
  • **Commands and prompt files** (one-shot prompts):

[refs/COMMANDS.md](refs/COMMANDS.md)

  • **Agent-facing prose**:

[refs/writing-for-agents/REFERENCE.md](refs/writing-for-agents/REFERENCE.md)

  • **Skill, workflow, or model-route evaluation**:

[refs/workflow-evaluation.md](refs/workflow-evaluation.md)

  • **Repository instruction setup or full-surface audits**:

[refs/INITIALIZATION.md](refs/INITIALIZATION.md), then only the component references it selects.

For a broad audit, inventory the layers and prioritize concrete context, routing, and execution problems. Ask only when a material scope choice remains unresolved.

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