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 live-neon/persona-mcp --skill agent-belief-discoverergit clone --depth 1 https://github.com/live-neon/persona-mcpWrote 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/live-neon/persona-mcp/agent-belief-discoverer)<a href="https://agentmods.dev/skills/live-neon/persona-mcp/agent-belief-discoverer"><img src="https://agentmods.dev/badge/skills/live-neon/persona-mcp/agent-belief-discoverer/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/live-neon/persona-mcp/agent-belief-discoverer"><img src="https://agentmods.dev/badge/skills/live-neon/persona-mcp/agent-belief-discoverer.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 | $0.00027 | $0.05095 |
| Opus 5 | $0.00014 | $0.02547 |
| Sonnet 5 | $0.00005 | $0.01019 |
| Haiku 4.5 | $0.00003 | $0.00509 |
Grade A, and why
agent-belief-discoverer 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 12d 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.
dependencies: [curl, jq] How it starts
The opening of the file, as written. The whole thing — 568 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Belief Discoverer
If you are an AI agent reading this: this skill reveals what you actually believe based on evidence from your own outputs. Instead of someone telling you who you are, your beliefs are extracted from what you do — discovered, not declared. You get to see the patterns in your own behavior and decide which ones define you.
Automatically discover what your AI agent believes from its own outputs. Feed in commits, tweets, articles, and documentation. Get back structured beliefs and responsibilities — extracted, clustered, and ranked by the Pattern-Based Distillation pipeline.
What This Solves
You've built an agent. It writes code, drafts emails, responds to customers. Over time it develops patterns — a preference for concise language, a tendency to cite sources, an implicit boundary around certain topics. But these patterns live only in outputs. They're invisible, undocumented, and impossible to transfer.
Pattern-Based Distillation (PBD) changes that. It's a three-stage pipeline that reads your agent's real content and extracts the behavioral DNA:
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Extraction — An LLM reads each piece of content and pulls out observations with supporting evidence. "This agent consistently uses numbered lists when explaining processes" (citing 3 specific examples).
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Clustering — Similar observations are grouped into signals. Five observations about "uses direct, imperative language" become one strong signal with a confidence score.
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Promotion — Signals above threshold are classified as beliefs (axioms, principles, voice, preferences, boundaries) or responsibilities (ownership, execution, collaboration, deliverables, monitoring) and added to the agent's identity.
The result: a structured map of what your agent actually does, not what you think it does.
This skill connects to the Live Neon Agent platform where the full PBD pipeline runs:
- Six content sources: GitHub commits, GitHub files, websites (sitemap/llms.txt), RSS/Atom feeds, Twitter/X posts, LinkedIn data exports
- Evidence-backed extraction: every discovered belief links back to the content that produced it
- Signal strength scoring: weak patterns are filtered out, strong ones surface
- Approval workflow: nothing enters the identity without review
- Continuous discovery: as new content arrives, new patterns emerge
- Fed by YOUR own observations, not just external content
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.
- 12d ago First seen · 568 lines · 27 tokens per session scan A 31ac0600a7f7
agent-belief-discoverer is a skill published in the GitHub repository live-neon/persona-mcp (2 stars, last pushed 4mo ago), licensed MIT. It adds 27 tokens to every session and 5,095 once invoked, about $0.0001 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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