planning-with-files
Optional persistent file-based planning for multi-phase or long-running work that benefits from recovery across sessions or…
Design and audit agent instructions, skills, plugins, subagents, prompts, hooks, and provider configuration. Use for AI-tool setup, authoring, or context and routing problems.
$ npx -y skills add khaneliman/khanelinix --skill ai-tools-architect --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/ai-tools-architectContext 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.
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.
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.
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:
trim it because one repository's instructions repeat them. Declared skill dependencies can supply shared methods.
names, or personal data. The audit flags the first two.
sidecars; scripts run on Linux and macOS, handle Windows where feasible, and degrade when an optional tool is missing.
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.
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>
Read the reference whose constraints affect the task:
[refs/PLUGINS.md](refs/PLUGINS.md)
[refs/SUBAGENTS.md](refs/SUBAGENTS.md)
[refs/COMMANDS.md](refs/COMMANDS.md)
[refs/writing-for-agents/REFERENCE.md](refs/writing-for-agents/REFERENCE.md)
[refs/workflow-evaluation.md](refs/workflow-evaluation.md)
[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.
Run packages directly with: Or install from the packages output. For example:
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