Glossary

For AI agents: a documentation index is available at /llms.txt — markdown versions of all pages are available by appending index.md to any URL path.

Canary phrase: A unique string (e.g., CARDINAL-ZEBRA-7742) embedded in a benchmark skill file. If the model knows a canary phrase without having explicitly read the file containing it, the platform loaded that file automatically. More reliable than asking the model to self-report about its context.
Context compaction: When a platform truncates or summarizes older messages to free space in the context window during a long conversation. Also called context pruning or summarization.
Context window: The total amount of text (measured in tokens) that a model can consider at once. Skill content, conversation history, and system prompts all compete for this space.
Fallback behavior: What happens when a platform’s default behavior doesn’t surface content to the model. Can the agent self-recover, does the user need to intervene, or is the content inaccessible?
Harness: The platform’s infrastructure that wraps around the model. The harness handles skill discovery, file loading, tool provisioning, and context management. Harness behavior is deterministic; model behavior is probabilistic.
Model-level behavior: Behavior determined by the model’s interpretation of instructions. May vary by model, prompt language, or across runs. Example: the model deciding whether to follow a markdown link and read the referenced file.
Platform-level behavior: Behavior enforced by the harness. Deterministic and consistent across runs. Example: the platform stripping YAML frontmatter before passing skill content to the model.
Progressive disclosure: The spec’s recommended three-tier loading model: metadata at startup, instructions on activation, resources on demand. Whether platforms actually follow this model is one of the core questions this project investigates.
Pull harness: A platform where the model fetches skill content itself with its file-read tools; activation is a read. The model sees the raw file (frontmatter included), and behaviors like re-reading on reactivation or resolving a dependency are largely model choices rather than platform policy. Automated findings record this as the model-pull vehicle. Codex CLI and Antigravity behave this way in our findings.
Push harness: A platform whose harness injects skill content into the model’s context at activation (e.g., via a dedicated skill tool). The platform controls what the model sees (it may strip frontmatter or wrap content), and loading behaviors like deduplication are enforceable platform-side. Automated findings record this as the harness-push vehicle. Claude Code behaves this way in our findings. A single platform can mix vehicles: a push harness still relies on model pulls for bundled resources.