llm-out-of-the-loop
Skill9 starsChanged 4 months agoLicence not identifiedView on GitHub
Architect any system that produces a consequential output so that the LLM stays entirely out of the decision path and only narrates an already-sealed result. Use this whenever an LLM is anywhere near a verdict, score, classification, ranking, risk number, approval, or any output that triggers an action or becomes evidence; when designing where the model "sits" in a pipeline; when someone proposes letting the model "decide", "judge", "classify", or "score"; and when you need the model to explain or write up a result without being able to change it. Push to use this whenever an LLM and a consequential output appear in the same system, even if the user hasn't framed it as an architecture question.
In this repository91 other files it gives its agents.
- abductive-engineeringSkill
- acquisition-order-of-volatilitySkill
- agent-trust-boundariesSkill
- alert-triage-economicsSkill
- assume-breach-modelingSkill
- atomic-state-mutationSkill
- attack-surface-triageSkill
- audit-before-patchSkill
- authorization-surface-mappingSkill
- beyond-the-fixSkill
- beyond-the-sinkSkill
- claim-provenance-disciplineSkill
- client-side-trust-boundarySkill
- cloud-architecture-selectionSkill
- cloud-control-plane-reasoningSkill
- codebase-health-assessmentSkill
- concurrency-reasoningSkill
- container-trust-boundarySkill
- containment-under-uncertaintySkill
- control-effectiveness-vs-existenceSkill
- credential-material-triageSkill
- crypto-misuse-reasoningSkill
- data-leakage-huntingSkill
- data-provenance-mappingSkill
- daubert-defensible-writingSkill
- debt-closure-disciplineSkill
- deception-signal-qualitySkill
- decision-record-disciplineSkill
- dependency-provenanceSkill
- destination-driven-constructionSkill
- detection-engineeringSkill
- deterministic-coreSkill
- diagnosing-bugsSkill
- discriminating-proofSkill
- distributed-write-atomicitySkill
- dont-fall-in-love-with-the-bugSkill
- dual-use-behavior-adjudicationSkill
- exploitability-triageSkill
- falsifiable-testingSkill
- finding-custodySkill
- forensic-logging-designSkill
- forensic-persistenceSkill
- frontend-accessibility-by-constructionSkill
- frontend-component-boundariesSkill
- frontend-performance-budgetsSkill
- frontend-testing-strategySkill
- git-disciplineSkill
- honest-degradationSkill
- honest-technical-oratorySkill
- hypothesis-driven-huntingSkill
- incident-timeline-reconstructionSkill
- intel-source-evaluationSkill
- invariant-huntingSkill
- irreversible-action-gateSkill
- known-defect-dispositionSkill
- language-selectionSkill
- live-contract-compatibilitySkill
- model-evaluation-disciplineSkill
- non-overwhelming-uxSkill
- oracle-driven-fuzzingSkill
- The first 60 shown.
The file
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- 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.

