Ensuring the AI behaves predictably across sessions, edge cases, and modalities.
EnglishTurn-taking, repair sequences, grounding, and dialogue structure for human-AI interaction.
EnglishWhat happens when an agent fails — retry, fallback, escalate, or graceful degradation.
EnglishClassifying AI failures — hallucination, refusal, irrelevance, tone mismatch, latency.
EnglishReading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.
EnglishDesigning intervention points where humans review, approve, or redirect agent work.
EnglishCoordinating text, image, voice, and tool-use modalities in a single interaction.
EnglishMaking multi-agent workflows visible and debuggable for designers and developers.
EnglishDefining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.
EnglishHelping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.
EnglishInterpreting implicit and explicit feedback — edits, regenerations, abandonment.
EnglishTranslating organisational values and user expectations into system constraints.
English
AGENT SKILLS ATLASIncludes Behavioral Consistency, Conversation Patterns, Failure Recovery, and other workflows.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLISupported by the Atlas install prompt. Skill-specific requirements vary.
Pack stats
What’s included
Behavioral Consistency
Ensuring the AI behaves predictably across sessions, edge cases, and modalities.
Conversation Patterns
Turn-taking, repair sequences, grounding, and dialogue structure for human-AI interaction.
Failure Recovery
What happens when an agent fails — retry, fallback, escalate, or graceful degradation.
Failure Taxonomy
Classifying AI failures — hallucination, refusal, irrelevance, tone mismatch, latency.
Frustration Detection
Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.
Human in the Loop
Designing intervention points where humans review, approve, or redirect agent work.
Multimodal Orchestration
Coordinating text, image, voice, and tool-use modalities in a single interaction.
Observability Design
Making multi-agent workflows visible and debuggable for designers and developers.
Output Quality Rubrics
Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.
Trust Calibration
Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.
User Satisfaction Signals
Interpreting implicit and explicit feedback — edits, regenerations, abandonment.
Value Specification
Translating organisational values and user expectations into system constraints.
Quick start
- Choose your skills. Select individual skills above, or choose the entire pack.
- Copy the install prompt. Use Copy install prompt above the list.
- Send it to your agent. Paste into your coding agent’s chat. Your agent reviews the source and guides the setup.
Try it with your agent
After installation, start with a request like:
Use the Behavioral Consistency skill to help with my task.
Ask me for the context you need before getting started.Requirements & license
- A coding agent that can access your project files and install skills.
- Your agent checks each skill’s instructions, dependencies, and permissions during setup.
- Reported license for the skills shown: MIT. The author’s terms apply.