Agent skill · magnus919
artifact-pyramids
Organize durable agent research outputs as summaries, analysis, and evidence dossiers. Use when producing multi-layer research artifacts or coordinating research handoffs.
What it needs
About 6k tokens when loaded.
What this skill does
Artifact Pyramids for Agentic AI Research Progressive disclosure governs how we feed agents context: metadata at startup, instructions on activation, resources on demand. The Artifact Pyramid applies the same principle to what agents produce. Three layers of increasing depth, each independently consumable, each linking down to the next. Loading Guidance When producing an artifact pyramid, load these references as a required set (not piecemeal): Reference File ----------- ------ Pipeline Stages — layer definitions, navigation format, production flow references/pipeline-stages.md Output Classification Framework — role-agnostic content contracts, 00-index/L1 boundary references/output-classification-framework.md Quality Gates — verification checklists per layer references/quality-gates.md Delegation Context Template — exact text for subagent output mandates references/delegation-context-template.md These four define complementary aspects of the spec that the others assume. Loading only a subset risks violating content contracts (e.g., putting findings in 00-index) or skipping required navigation affordances. Load the full set before writing any pyramid files. The reference table below describes when to load each file; the four above are always required for pyramid production. The remaining references (framework, worked example, canonical article, intellectual lineage, provenance, composite synthesis) are supplementary — load when the task calls for conceptual depth or a worked pattern. The Pyramid The pyramid is consumed top-down but produced via recursive gap analysis: start with the summary, embed links to analysis files, write analysis files that link to dossiers, and evaluate after each round whether gaps remain. Layer numbering is top-down — L1 is the most distilled layer (the entry point), L3 is the most detailed (pulled on demand). This mirrors the Agent Skills input model: metadata (L1) → instructions (L2) → resources (L3). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/artifact-pyramids