Agent skill

Artifact Pyramids

by magnus919 in magnus919/hermes-profiles

Progressive disclosure for what AI agents produce. An agent skill from magnus919/hermes-profiles.

MITAuto-check passedAgent Workflows

Install Artifact Pyramids

skills CLI
$ npx skills add magnus919/hermes-profiles --skill artifact-pyramids -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install magnus919/hermes-profiles artifact-pyramids --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/magnus919/hermes-profiles.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/artifact-pyramids .claude/skills/artifact-pyramids && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
artifact-pyramids
GitHub stars
289
Token cost
~2.5k tokens
SKILL.md length
921 words
Files
25 (incl. scripts, references, assets)
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

Progressive disclosure for what AI agents produce. An agent skill from magnus919/hermes-profiles.

  • Works in 7 steps: Progressive disclosure is symmetric. The… → Each layer is independently consumable.… → Navigation is explicit. Every file… → …
  • Agent Workflows work in your project
  • SKILL.md covers Loading Guidance, The Pyramid, The Navigation Mechanism and Reference Files, plus 6 more sections

What it does

Artifact Pyramids is an agent skill from magnus919/hermes-profiles. Progressive disclosure for what AI agents produce. Structure research outputs across three layers of increasing depth — Summary (key findings), Analysis Collection (per-dimension files), and Detailed Dossiers (source excerpts, raw data, transcripts) — so downstream agents and humans consume only as deeply as they need. Load this skill when organizing research outputs, building multi-agent research pipelines, or designing agent collaboration protocols.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 26 other files, including scripts, reference files and assets (for example `README.md`, `assets/artifact-inventory.md` and `assets/pyramid-template.md`). Compatibility notes: Agent-agnostic — concepts apply to any AI agent workflow. Scripts require Python 3.9+ and a POSIX shell.

It sits in Agent Workflows. The repository describes itself as: Curated Hermes Agent profiles for specialist swarms — opinionated, Hermes-optimized, artifact-pyramid native. The licence is MIT.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/artifact-pyramids”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Agent-agnostic — concepts apply to any AI agent workflow. Scripts require Python 3.9+ and a POSIX shell.

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Progressive disclosure is symmetric. The same three-tier model governs what agents consume (metadata → instructions → resources) and what…
  2. Each layer is independently consumable. A product-manager agent reads only the L1 summary. A data-scientist agent reads a single L2…
  3. Navigation is explicit. Every file carries a SOURCES section with absolute paths and descriptions — not footnotes, but agent navigation…
  4. Depth varies by mission complexity. A simple brief may produce only L1 + 2 analysis files. A competitive landscape may need all three…
  5. Quality gates are directional. Material moves from L3 (sources) toward L1 (summary) only when it meets the gate for the target layer.
  6. 03-dossiers/ is flat — no subdirectories. The dossier layer is a flat reference library. Organize with epoch-prefixed or category-prefixed…
  7. Root-level files are amended; lower-level files are fixed. In multi-epoch or multi-phase systems, 00-index.md, 01-summary/findings.md, and…

What it can do on your machine

Read from SKILL.md and the folder at commit 867a555. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Agent-agnostic — concepts apply to any AI agent workflow. Scripts require Python 3.9+ and a POSIX shell.

    From compatibility in the SKILL.md frontmatter.

Context cost

Artifact Pyramids loads about 2.5k tokens when it runs, and up to ~20k if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 921 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~20k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from magnus919/hermes-profiles at commit 867a555, republished under its MIT licence (© magnus919). 921 words, ~2,545 tokens.

Download SKILL.mdSave it as .claude/skills/artifact-pyramids/SKILL.md (or your agent's skills folder). This skill also uses 24 other files; get the full folder from GitHub.
name
artifact-pyramids
description
Progressive disclosure for what AI agents produce. Structure research outputs across three layers of increasing depth — Summary (key findings), Analysis Collection (per-dimension files), and Detailed Dossiers (source excerpts, raw data, transcripts) — so downstream agents and humans consume only as deeply as they need. Load this skill when organizing research outputs, building multi-agent research pipelines, or designing agent collaboration protocols.
compatibility
Agent-agnostic — concepts apply to any AI agent workflow. Scripts require Python 3.9+ and a POSIX shell.
license
MIT
metadata.spec-version
0.0.3
metadata.source
https://github.com/groktopus/artifact-pyramids
metadata.canonical-article
https://www.groktop.us/artifact-pyramid-progressive-disclosure/

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):

ReferenceFile
Pipeline Stages — layer definitions, navigation format, production flowreferences/pipeline-stages.md
Output Classification Framework — role-agnostic content contracts, 00-index/L1 boundaryreferences/output-classification-framework.md
Quality Gates — verification checklists per layerreferences/quality-gates.md
Delegation Context Template — exact text for subagent output mandatesreferences/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

      ┌──────────────┐
      │  L1 SUMMARY  │  One file: research question, key findings,
      │    🎯        │  most important implications. Links to L2 files.
      └──────┬───────┘
      ┌──────┴───────┐
      │  L2 ANALYSIS │  Per-dimension files: market, competitive,
      │  COLLECTION  │  technical feasibility, risk. Self-contained,
      │   🧩        │  each links to L3 dossiers.
      └──────┬───────┘
      ┌──────┴───────┐
      │  L3 DOSSIERS │  Source excerpts, raw data tables, interview
      │    📦        │  transcripts, methodology notes. Reference
      └──────────────┘  library, pulled on demand.

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).

The Navigation Mechanism

Every file at every layer carries, at the bottom, an explicit SOURCES section with absolute path references and descriptions:

SOURCES (LAYER 2 NAVIGATION)
research/analysis/market-position.md
 -> Competitor mapping and market share analysis supporting Section 2
research/analysis/technical-feasibility.md
 -> Architecture evaluation supporting Section 3
research/dossiers/competitor-profiles.md
 -> Raw competitor data dossiers

These aren't footnotes. They are navigation affordances for agent consumers. Each description answers the question the consuming agent asks before loading: what will I find if I go deeper?

Reference Files

ReferenceLoad whenFile
Framework & SymmetryYou need the full conceptual foundation — the asymmetry problem, multi-agent routing, DIKW relationshipreferences/artifact-pyramid-framework.md
Pipeline StagesYou're building or auditing a pyramid — detailed definitions per layer, navigation format, production flowreferences/pipeline-stages.md
Quality GatesYou need to verify an artifact meets the standard for its layerreferences/quality-gates.md
Worked ExampleYou want to see a complete synthetic walkthrough of all three layersreferences/synthetic-example.md
Canonical ArticleRead the published groktop.us piece — the Layer 3 artifact that defines the conceptreferences/canonical-article.md
Intellectual LineageHow software architecture documentation (4+1 Views, C4, arc42, ADRs) independently discovered progressive disclosure under different names — and what the Artifact Pyramid generalizes beyond themreferences/intellectual-lineage.md
Methodology-to-Pyramid MappingHow specialist profiles map domain-specific methodologies into the universal pyramid structure, with dimension boundary rulesreferences/methodology-to-pyramid-mapping.md
Output Classification FrameworkRole-agnostic — three questions any specialist can ask to map their outputs into the correct layer: who consumes it, how often, what question it answers. Includes per-layer content contracts to eliminate duplication between 00-index and L1 files.references/output-classification-framework.md
Composite Pyramid SynthesisHow to merge multiple subagent pyramids into a root-level composite pyramid — orchestrator flow, SOURCES convention, worked example from jobs-finder pipelinereferences/composite-pyramid-synthesis.md
Delegation Context TemplateYou're delegating research to subagents and need the exact text to include in context strings to ensure artifact-pyramid outputreferences/delegation-context-template.md
Flat-to-Pyramid MigrationYou're converting existing flat JSON outputs to artifact-pyramid format — the pattern for L1/L2/L3 structure, 00-index rules, and downstream consumer fallback readsreferences/flat-to-pyramid-migration.md
Nested Pyramid PatternYou're designing a single system that produces multiple artifact streams over time (multi-phase, multi-epoch) — avoid scatter, nest epochs under a single root pyramidreferences/nested-pyramid-pattern.md
Show full SKILL.md (345 more words)Show less

Scripts

ScriptLoad whenFile
pyramid-statusYou want to audit an existing research directory for structural coveragescripts/pyramid-status.sh
extract-atomsYou have raw source text and need candidate atomic claimsscripts/extract-atoms.py

Templates

TemplateLoad whenFile
Project ScaffoldYou're starting a new research project and need the index skeletonassets/pyramid-template.md
Artifact InventoryYou need to track what exists at each layer across a projectassets/artifact-inventory.md

Quick Start

bash
# Scaffold a new research project with all three layer directories
mkdir -p my-project/{01-summary,02-analysis,03-dossiers}
cp assets/pyramid-template.md ./my-project/00-index.md

# Check structural coverage of an existing project
scripts/pyramid-status.sh ./my-project

# Extract candidate atoms from source text
scripts/extract-atoms.py ./my-project/03-dossiers/source-1.txt

Project Structure

my-project/
├── 00-index.md              # Project scaffold (from template)
├── 01-summary/              # L1: one file — key findings, implications, links to L2
├── 02-analysis/             # L2: per-dimension files (market, competitive, technical)
├── 03-dossiers/             # L3: source excerpts, transcripts, raw data, methodology
└── artifact-inventory.md    # Cross-layer tracking (from template)

The numbered prefixes mirror the pyramid's top-to-bottom orientation: 01 is most consumed, 03 is pulled on demand.

Key Principles

  1. Progressive disclosure is symmetric. The same three-tier model governs what agents consume (metadata → instructions → resources) and what they produce (summary → analysis → dossiers).
  2. Each layer is independently consumable. A product-manager agent reads only the L1 summary. A data-scientist agent reads a single L2 analysis file. A verifier reads L3 dossiers.
  3. Navigation is explicit. Every file carries a SOURCES section with absolute paths and descriptions — not footnotes, but agent navigation affordances answering what will I find if I go deeper?
  4. Depth varies by mission complexity. A simple brief may produce only L1 + 2 analysis files. A competitive landscape may need all three layers with multiple files per layer.
  5. Quality gates are directional. Material moves from L3 (sources) toward L1 (summary) only when it meets the gate for the target layer.
  6. 03-dossiers/ is flat — no subdirectories. The dossier layer is a flat reference library. Organize with epoch-prefixed or category-prefixed filenames (epoch-1-validation-edit-3.json), not nested directories. Subdirectories inside 03-dossiers/ violate the flat-file contract and break the SOURCES navigation path.
  7. Root-level files are amended; lower-level files are fixed. In multi-epoch or multi-phase systems, 00-index.md, 01-summary/findings.md, and 02-analysis/ trajectory files grow as new data arrives. They are rewritten to reflect the current state. Files in 03-dossiers/ and per-category analysis files below 02-analysis/ are created once and never modified — they represent a fixed point in time.

When NOT to use

  • Single-turn Q&A with no research artifacts to preserve
  • Tasks producing only ephemeral output (one-off calculations, quick lookups)
  • Workflows where the source material IS the final output (no synthesis needed)

© magnus919, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 24 other files (scripts, references, assets) in skills/artifact-pyramids of magnus919/hermes-profiles.

  • SKILL.md
  • LICENSE
  • README.md
  • assets/artifact-inventory.md
  • assets/pyramid-template.md
  • references/artifact-pyramid-framework.md
  • references/canonical-article.md
  • references/companion-session-summary.md
  • references/composite-pyramid-synthesis.md
  • references/delegation-context-template.md
  • references/flat-to-pyramid-migration.md
  • references/intellectual-lineage.md
  • references/methodology-to-pyramid-mapping.md
  • references/nested-pyramid-pattern.md
  • references/output-classification-framework.md
  • references/pipeline-stages.md
  • references/provenance-artifacts.md
  • references/quality-gates.md
  • references/skillopt-baseline-pyramid.md
  • … and 6 more

Open the folder on GitHubat commit 867a555

Compare with similar skills

Artifact Pyramids next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Using Superpowersfarm-fe/farm5.6k36 repos~1.4kAutomated safety check: PassMIT
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Skill CreatorAzure/azqr79689 repos~8.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Artifact Pyramids

What does Artifact Pyramids do?

Progressive disclosure for what AI agents produce. An agent skill from magnus919/hermes-profiles. Artifact Pyramids is an agent skill from magnus919/hermes-profiles. Progressive disclosure for what AI agents produce.

When should I use Artifact Pyramids?

Artifact Pyramids fits situations like: agent Workflows work in your project.

How do I install Artifact Pyramids in Claude Code?

Run `npx skills add magnus919/hermes-profiles --skill artifact-pyramids -a claude-code`. Or copy the skill folder (skills/artifact-pyramids in magnus919/hermes-profiles) into .claude/skills/artifact-pyramids in your project. Claude Code loads it when a task matches its description.

How do I install Artifact Pyramids in Codex?

Run `npx skills add magnus919/hermes-profiles --skill artifact-pyramids -a codex`. Or copy the skill folder (skills/artifact-pyramids in magnus919/hermes-profiles) into .agents/skills/artifact-pyramids in your project. Codex loads it when a task matches its description.

Can I use Artifact Pyramids in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add magnus919/hermes-profiles --skill artifact-pyramids -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/artifact-pyramids, .gemini/skills/artifact-pyramids, .github/skills/artifact-pyramids and .opencode/skills/artifact-pyramids in your project.

What does Artifact Pyramids need to run?

SKILL.md names no scripts, command-line tools or credentials: Artifact Pyramids is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): Agent-agnostic — concepts apply to any AI agent workflow. Scripts require Python 3.9+ and a POSIX shell..

Does Artifact Pyramids access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Artifact Pyramids safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Artifact Pyramids use?

Artifact Pyramids is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Artifact Pyramids use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 18k tokens, read only when the agent opens those files.

What are the alternatives to Artifact Pyramids?

Skills that share tags, products or a category with Artifact Pyramids: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Artifact Pyramids?

magnus919 (a GitHub user) maintains it in magnus919/hermes-profiles, which has 289 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on June 27, 2026.

Source: magnus919/hermes-profiles on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.