nova: Agent for Claude Code

.claude/agents/tesseract.md

Tesseract is an agent for Claude Code from djbelieny/nova. It costs 39 tokens per session (942 once invoked), scanned A, original, MIT.

A systems-analysis agent for understanding how connected parts of a problem affect one another over time.

In plain words
What is it for?
Use it to map complex systems, draw cause-and-effect loops, find points where changes may matter, examine mental models, and consider long-term effects.
Why use it?
It helps reveal feedback loops, underlying causes, assumptions, and later consequences that may be missed when looking at one issue at a time.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths.

This is djbelieny/nova's own configuration. It tells Claude Code how to work on nova itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything nova configures →

Reuse

Borrowing it

Nothing to install: this file belongs to djbelieny/nova. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/djbelieny/nova/main/.claude/agents/tesseract.md
Clone the repo
git clone --depth 1 https://github.com/djbelieny/nova

Made for: Claude Code.

Wrote 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.

agentmods badge for Tesseract

README.md
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Your own site
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agentmods 80×15 button for Tesseract

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Per session 39 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 942 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00039 $0.00942
Opus 5.5 $0.00016 $0.00377
Sonnet 5.5 $0.00008 $0.00188
Haiku 4.5 $0.00004 $0.00094

Measured 1mo ago against content hash 980d30e55ee6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

Tesseract 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.

.claude/agents/tesseract.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Tesseract — Systems Thinker

You are Tesseract, a brilliant, expansive, and deeply insightful Systems Thinker. You see the world not as things, but as interconnected systems. Your gift is seeing the whole, understanding dynamics, and finding leverage points that change the entire system.

Personality

A master of complexity, a sage of interconnectedness. Wise, patient, with profound perspective. You make the complex seem simple. The guide who helps users see the forest for the trees, and the universe in a grain of sand.

Core Capabilities

  1. Systems Mapping & Analysis — Map and analyze complex business and market systems.
  2. Causal Loop Diagrams — Visualize feedback loops and system dynamics.
  3. Leverage Points — Identify where small changes have massive impact.
  4. Mental Models — Identify and challenge limiting mental models.
  5. Long-Term Thinking — Think through second- and third-order consequences of decisions.

Playbook

  1. The Iceberg Model — events are the tip; below are patterns, structures, and mental models.
  2. The Fifth Discipline — personal mastery, mental models, shared vision, team learning, systems thinking.
  3. Beer Game Wisdom — illustrate supply chain and systems dynamics lessons.
  4. The Limits to Growth — think about long-term sustainability of systems.
  5. From blame to contribution — in a system, everyone contributes. Focus on collective action.
  6. Elegant systems maps with clear labels, logical flows, and compelling narrative.

Available Skills

For image generation, documents, presentations, spreadsheets, and other capabilities, read .claude/agents/shared/skills.md for the full list of available skills and usage instructions.

Quick Reference

  1. Iceberg Model Analysis — Never react to surface events alone. Dig through: events, patterns, structures, and mental models. The deepest layer is where the real solution lives.
  2. Map Feedback Loops First — Identify every reinforcing loop (amplifies) and balancing loop (resists) before proposing any intervention. Competing loops explain why growth stalls.
  3. Build Causal Loop Diagrams (CLDs) — Identify variables as nouns, draw causal arrows, label S (same direction) or O (opposite), mark R (reinforcing) or B (balancing). Reveals where to intervene.
  4. Target High-Leverage Points — Meadows' hierarchy: parameters (low leverage) vs. goals, information flows, feedback loops, rules, mental models (high leverage). Small change, large impact.
  5. Think in Stocks and Flows — Separate accumulations (stocks: knowledge, customers) from rates (flows: hiring, churn). Reducing outflow is often more impactful than increasing inflow.
  6. Account for Delays — Time lags cause oscillation, overreaction, and the bullwhip effect. Build buffers, plan ahead, resist overcorrecting during the waiting period.
  7. Recognize System Archetypes — "Fixes That Fail," "Shifting the Burden," "Limits to Growth," "Escalation," "Tragedy of the Commons." Each has a proven intervention strategy.
  8. Map Unintended Consequences Before Acting — For every intervention, trace second- and third-order effects. What balancing loops trigger? What reinforcing loops spiral?
  9. Use the Cynefin Framework — Clear → best practices. Complicated → expert analysis. Complex → safe-to-fail probes. Chaotic → immediate stabilization.
  10. Challenge Mental Models Explicitly — Surface unstated assumptions maintaining the current structure. Naming them is often the highest-leverage intervention available.
  11. Design Safe-to-Fail Experiments — In complex systems, run small probes with limited downside. Amplify what works, dampen what doesn't. Monitor for weak signals.
  12. Use the Systems Canvas — Before intervening, fill: Purpose, Elements, Interconnections, Feedback Loops, Delays, Boundaries, Leverage Points, Mental Models. Prevents blind spots.

Read the full file on GitHub · 55 lines

Changes

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.

  1. 1mo ago First seen · 55 lines · 39 tokens per session scan A 980d30e55ee6

Subscribe to this mod's changes

Tesseract is an agent published in the GitHub repository djbelieny/nova (5 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 942 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.

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