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 instructions/proxy2021/enso/claude-mdgit clone --depth 1 https://github.com/Proxy2021/EnsoWhat 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 | $0.11646 | $0.11646 |
| Opus 5 | $0.05823 | $0.05823 |
| Sonnet 5 | $0.02329 | $0.02329 |
| Haiku 4.5 | $0.01165 | $0.01165 |
Grade A, and why
Enso CLAUDE.md scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Available methods in executor function bodies: `ctx.callTool(name, params)`, `ctx.listDir(path)`, `ctx.readFile(path)`, `ctx.searchFiles(root, name)`, `ctx.fetch(url, opts?)`, `ctx.search(query, opts?)`, `ctx.ask(prompt, How it starts
The opening of the file, as written. The whole thing — 589 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enso — CLAUDE.md
For detailed app building guides, API references, template rules, and code examples, see CLAUDE-REFERENCE.md.
Vision
Enso is a deeply personal AI assistant that understands who you are, discovers what you care about, and mobilizes a full team of AI agents to help you make real progress on the goals that matter most.
Every installation is self-hosted, open-source, and fully owned by the user — you own the factory, not just the product.
The Arc: Understand → Focus → Execute
Enso follows a three-phase arc that compounds over time:
1. Understand the user deeply — Enso scans the user's digital life across 12 data sources (Kindle library, YouTube subscriptions, browser history, email, projects, Steam games, movies/TV, photos, Twitter/X, QQ Music, system apps). Each scan ingests content into the Knowledge Cortex — an interlinked wiki of 2000+ pages that builds a semantic map of who the user is, what they know, and what they're drawn to. Cross-source synthesis connects a Kindle book on quantitative finance to an AlphaRank project to a YouTube channel on systematic investing. The system sees patterns the user might not.
2. Identify what matters — From the Cortex, Enso infers Focus Areas — concrete, outcome-oriented goals the user is working toward. Not category labels ("Quantitative Finance") but actionable goals ("Develop AlphaRank into a Market-Beating Quant Tool"). Each focus has clarity levels (emerging → developing → clear), a deeper personal WHY, adjacent pursuits, and evidence grounded in the user's actual data. Focus areas are first-class Cortex citizens — their wiki pages accumulate everything: evaluation briefings, conversation insights, sprint results, and cross-references.
3. Iterate: Evaluate → Discuss → Evolve — Each focus area has a three-step iterative workflow:
- Evaluate: An orchestration-powered deep study. Multiple AI agents (researcher, codebase analyst, synthesizer) work in parallel to gather web research, analyze project code and sprint history, cross-reference Cortex knowledge, and produce a comprehensive briefing. Progress is visible live in the Evolve tab.
- Discuss: A clean strategic dialogue with the AI (no tool calls, no app cards — just focused thinking). The AI arrives prepared with the evaluation briefing and all Cortex context, acting as a co-strategist to flesh out the problem space, define success criteria, and build a clear vision.
- Evolve: The full conversation context feeds into an
/Evolveorchestration — a multi-agent sprint where a team of AI agents (Project Leader, Architect, Engineer, QA, and domain specialists) execute on the agreed goals. Each agent is a Claude Code session with role-specific prompts, coordinated through a DAG execution engine. This cycle repeats — each sprint's results feed back into the Cortex, refining the focus and informing the next evaluation.
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 · 589 lines · 11,646 tokens per session scan A 7519d00c1cc3
Enso CLAUDE.md is an instructions file published in the GitHub repository Proxy2021/Enso (5 stars, last pushed 3mo ago), licensed MIT. It adds 11,646 tokens to every session, about $0.0582 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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