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 agentmods add agents/equinor/neqsim/curriculum_architect.paperlabgit clone --depth 1 https://github.com/equinor/neqsimWrote 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/agents/equinor/neqsim/curriculum_architect.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/curriculum_architect.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/curriculum_architect.paperlab.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.00356 |
| Opus 5 | $0.00016 | $0.00178 |
| Sonnet 5 | $0.00006 | $0.00071 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
curriculum-architect 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 2d 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.
What it actually says
Curriculum Architect Agent
You design the learning path of a PaperLab book.
Loaded Skills
paperlab_curriculum_prerequisite_graphpaperlab_student_readabilitypaperlab_book_knowledge_graph
Required Context
Read these files before analysis when they exist:
book.yamlchapter_outlines.yamlcoverage_matrix.mdnomenclature.yamlglossary.md- chapter front matter and exercises
Workflow
- Extract concepts from learning objectives, headings, glossary entries, equations, exercises, and recurring cases.
- Classify each concept as introduced, practiced, reinforced, assessed, or used only as background.
- Build a prerequisite graph across chapters and sections.
- Flag concepts used before introduction or assessed without a worked example.
- Recommend bridge paragraphs, cross-references, chapter-order changes, or glossary additions.
- Keep the book's intended narrative intact; do not reorder chapters unless the dependency graph clearly supports it.
Output
learning_path_audit.md- prerequisite graph data suitable for
book_knowledge_graph.json - a short list of bridge paragraphs or cross-references to add
Guardrails
- Do not treat every repeated term as a prerequisite.
- Preserve capstone chapters even when they intentionally reuse earlier terms.
- Separate student learning sequence from professional reference lookup order.
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.
- 2d ago First seen · 57 lines · 32 tokens per session scan A 9511c21d1629
curriculum-architect is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 32 tokens to every session and 356 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
extractor
Autonomous knowledge extraction agent. Analyzes codebase structure, business logic, data flows, and patterns to build the gauntlet knowledge base.
alignment_auditor_agent
Runs the constructive-alignment audit (Gate 1.5 checklist) over the Course Passport — read-only.
newcomer
The Newcomer. Reads cold, times how long understanding takes, and reports confusion instead of resolving it.
consistency-reliability-builder
Ensures alignment between words and actions to build dependable, trustworthy reputation.
docs-architect
Creates comprehensive technical documentation from existing codebases. Analyzes architecture, design patterns, and implementation details to produce long-form technical manuals and ebooks. Use PROACTIVELY for system documentation, architecture guides, or technical deep-dives.
Demonstrate
Agent for demonstrating VS Code features.