Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add magnus919/agent-skills --skill artifact-pyramidsgit clone --depth 1 https://github.com/magnus919/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/magnus919/agent-skills/artifact-pyramids)<a href="https://agentmods.dev/skills/magnus919/agent-skills/artifact-pyramids"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/artifact-pyramids/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/magnus919/agent-skills/artifact-pyramids"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/artifact-pyramids.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.02316 |
| Opus 5.5 | $0.00020 | $0.00926 |
| Sonnet 5.5 | $0.00010 | $0.00463 |
| Haiku 4.5 | $0.00005 | $0.00232 |
Grade A, and why
artifact-pyramids scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to artifact-pyramids — 27 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
┌──────────────┐
│ 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.
What ships with it
21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/artifact-inventory.md 919 B
- assets/pyramid-template.md 968 B
- evals/evals.json 7.7 KB
- LICENSE 1.0 KB
- README.md 2.4 KB
- references/artifact-pyramid-framework.md 3.4 KB
- references/canonical-article.md 1.3 KB
- references/composite-pyramid-synthesis.md 3.5 KB
- references/delegation-context-template.md 3.0 KB
- references/flat-to-pyramid-migration.md 3.1 KB
- references/intellectual-lineage.md 5.0 KB
- references/methodology-to-pyramid-mapping.md 3.3 KB
- references/nested-pyramid-pattern.md 6.2 KB
- references/output-classification-framework.md 9.3 KB
- references/pipeline-stages.md 6.6 KB
- references/provenance-artifacts.md 3.3 KB
- references/quality-gates.md 4.4 KB
- references/source-index.md 371 B
- references/synthetic-example.md 6.8 KB
- scripts/extract-atoms.py 8.3 KB runs code
- scripts/pyramid-status.sh 7.2 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +3 lines · +16 tokens per session 510045111b0a
- 1mo ago First seen · 149 lines · 33 tokens per session scan A 0ba2b630afed
artifact-pyramids is a skill published in the GitHub repository magnus919/agent-skills (151 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 2,316 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to artifact-pyramids, differing in 27 lines, and is treated as a copy.
Other skills, from other repositories
cocoreview
CocoReview — structured code review with six-severity findings vocabulary, progressive disclosure architecture, and universal anti-pattern baseline. Invoked via $review [file] [--complexity] [--security] [--architecture] [--language ].
cocoharvest
Decompose an approved plan into parallel workstreams, assign specialist personas, classify stages as HITL or AFK (CocoLens), generate flow.json stages with checkpoints and dual-file state, and create per-stage prompt files. Includes adaptive parallelism, stall detection, shell identity injection, and consecutive…
spec
Enter the Spec phase of CocoBrew. Guides the developer through structured requirements capture: goal, success criteria, constraints, personas involved, data sources, and deliverables. Writes spec.md to .cocoplus/lifecycle/ and creates a git commit.
discuss
Run a structured decision-capture dialogue before $plan — locks implementation choices (model, evaluation methodology, accuracy threshold, scope boundaries) into discuss.md to prevent silent decision drift during planning. Supports --red-team flag for adversarial post-PASS challenge session.
cocotrace
Build and query the SHA-256 requirements-to-implementation traceability graph across lifecycle artifacts.
cocoscout
Relevance-ranked context loading — Tier 2 async subagent (Haiku, <5s) that fires after Tier 1 deterministic checks in UserPromptSubmit. Injects ranked context from CocoGrove, CocoContext, Environment Inspector, Prompt Studio, and CocoDream.