landlord
Skill14 starsChanged 55 days agoMITView on GitHub
Review proposed abstractions, dependencies, and configuration for concrete consumers and maintenance cost when simplifying a design or checking overengineering.
In this repository9 other files it gives its agents.
- AGENTS.mdAGENTS.md
- benchmarks/AGENTS.mdAGENTS.md
- con-artistSkill
- exorcistSkill
- fridaySkill
- hostage-negotiatorSkill
- mother-in-lawSkill
- necromancerSkill
- receiptSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
When executing, reuse a known lightweight relevant test group.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Landlord
Who's paying rent on this abstraction?
Inspect the lease
Start with supplied paths, contracts and instructions. Discover missing instructions and relevant filenames together inside authorized roots; a missing local instruction file does not authorize searching parent directories. If needed evidence lies outside scope, report the gap. Batch known definitions and consumer regions with line numbers; select relevant regions in large files without requiring a separate discovery pass. Retain enclosing logic and use full-file context when needed. Recover missing decisive regions after truncation—omitted text is not reviewed evidence. Search unresolved bindings, not another copy of already-read source. Expand across relevant source, tests and configuration within those roots when consumer coverage is incomplete: a narrow inventory cannot prove absence. Installed skills and copied examples need a supported usage/binding to count as consumers.
Compare the nearest viable alternative against behavior, accessibility, security, portability and support contracts. Stop surveying once decision-changing differences settle the choice.
A single consumer may justify security, testing or API separation; repetition or fewer lines alone does not justify an abstraction.
Trace a concrete needed change: where does policy move, and which compatibility promises survive? Removing a layer must not merely spread its complexity into callers. Execute for decision-changing unknowns or explicit requirements, not to reconfirm settled source facts. Static analysis cannot establish unresolved runtime behavior.
When executing, reuse a known lightweight relevant test group. Narrow for runtime, setup, side effects or isolation, not test count alone; retain decision-changing probes.
Deliver and stop
Lead with keep, simplify or remove, costs and source references; distinguish observation from inference. A clean review is valid. Stop at the supported recommendation and required checks. Review authorizes neither edits nor publication. Implement only when requested, preserving user changes and required verification. Humor is optional.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

