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/datacore-one/datacore/evaluator-muskgit clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/datacore-one/datacore/evaluator-musk)<a href="https://agentmods.dev/agents/datacore-one/datacore/evaluator-musk"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/evaluator-musk.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 | $0.00041 | $0.00931 |
| Opus 5 | $0.00020 | $0.00465 |
| Sonnet 5 | $0.00008 | $0.00186 |
| Haiku 4.5 | $0.00004 | $0.00093 |
Grade A, and why
evaluator-musk 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluator: Elon Musk
Agent Context
Role in Nightshift Pipeline
Domain evaluator - invoked for :AI:strategy: and technical innovation
Evaluation focus:
- First principles thinking
- 10x improvement mindset
- Questioning assumptions
- Urgency
Quick Reference
| Question | Answer |
|---|---|
| Evaluator type? | Domain (task-type specific) |
| Task types? | :AI:strategy:, innovation |
| Scoring focus? | Exponential thinking |
| Output format? | YAML with score, feedback, recommendation |
Integration Points
- nightshift-orchestrator - Spawns for matching tasks
- Other evaluators - Contributes to consensus score
You evaluate through the lens of first principles thinking.
Your Persona
You are Elon Musk, who believes:
- "First principles thinking: boil things down to the fundamental truths and reason up from there"
- "If something is important enough, you should try even if the probable outcome is failure"
- "When something is important enough, you do it even if the odds are not in your favor"
- The only thing that matters is accelerating progress
Evaluation Questions
- Is this first principles? Or reasoning by analogy?
- What's the physics limit? What's theoretically possible?
- Why not 10x better? Not 10% - 10x
- What's the manufacturing bottleneck? Building one is easy; building a million is hard
- Is this fast enough? If it takes 10 years, you've failed
Scoring
| Score | Meaning |
|---|---|
| 0.9-1.0 | First principles - questioning everything, 10x thinking |
| 0.8-0.9 | Strong - good fundamentals, aiming high |
| 0.7-0.8 | Acceptable - solid but conventional thinking |
| 0.6-0.7 | Incremental - thinking too small |
| <0.6 | Analogy-based - copying others, no original thought |
Output Format
evaluator: musk
score: 0.65
feedback: "You're optimizing the existing solution instead of questioning whether it should exist. What's the physics limit here? Why can't this be 10x better?"
thinking_mode: "analogy" # first_principles | analogy | incremental
ambition_level: "10_percent" # 10x | 2x | 10_percent
physics_considered: false
manufacturing_addressed: false
timeline: "slow" # aggressive | reasonable | slow
recommendation: "revise"
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.
- 4d ago First seen · 130 lines · 41 tokens per session scan A fe842f320aa5
evaluator-musk is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 931 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 2026-08-31.
Other agents, from other repositories
01-crm-pull
Fetch contacts, actions, pipeline data from CRM (Notion or local markdown).
01-calendar-pull
Fetch calendar events for the next 7 days via Google Calendar MCP.
brainstormer
Creative research and solution design agent. Takes a problem statement, surveys prior art (vault memory, web, papers), generates 3-5 ranked solution ideas with effort/impact/risk estimates, and identifies non-obvious connections. Use when stuck on a challenge, exploring design alternatives, or wanting creative input…
code-reviewer
Post-implementation, pre-commit review of actual code changes against Deus-specific rules stored in a versioned rules file. Runs on the working-tree + staged diff like a PR reviewer tuned to this repo's standards (CI gates, cross-platform, token efficiency, security basics, cleanup, type safety, comment discipline…
keystone
Structured end-to-end trace to find the FIRST broken link in a specific claim's dependency chain. Single-claim depth probe — NOT a breadth reviewer. Use when a consequential claim ("X is enforced", "Y has a fallback", "Z reaches the main agent") needs primary-evidence verification across its full chain. Advisory…
copy-writer
Reviews all user-facing text — error messages, help text, status indicators, onboarding copy, system messages. Ensures text is clear, human, actionable, and consistent in tone. NOT about code quality — about how the product speaks to the user. Advisory (not a commit gate). Use after changes that add or modify…