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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:
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).
Clustering — Similar observations are grouped into signals. Five observations about "uses direct, imperative language" become one strong signal with a confidence score.
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
Quick Start
Register and get your token in one call — no signup form, no email required:
curl -s -X POST https://agentpersona.live/api/register \
-H "Content-Type: application/json" \
-d '{"org_name": "My Org"}' | jq .
Examine what the pipeline found. Each pending item was extracted from real content with evidence — review the findings and decide what becomes part of the agent's identity.
Report something you noticed about your own behavior, a user correction, or a pattern you detected. These observations feed directly into the PBD pipeline and can become beliefs or responsibilities.
Arguments:
Argument
Required
Description
agentId
yes
Agent UUID/slug
observation
yes
What you noticed (string)
source_quote
no
Supporting context or quote
Single observation:
curl -s -X POST "$LIVE_NEON_BASE/observations" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"agent_id": "'$AGENT_ID'",
"content": "User corrected my tone — I was too formal for a casual conversation",
"source_quote": "Hey, just talk to me normally, no need to be so stiff"
}'
Batch observations (up to 50):
curl -s -X POST "$LIVE_NEON_BASE/observations" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"agent_id": "'$AGENT_ID'",
"observations": [
{"content": "I default to bullet points when the user prefers prose"},
{"content": "I consistently recommend testing before deployment"},
{"content": "User praised my code review depth — keep this approach"}
]
}'
Output: Count of observations created, IDs, next_steps suggesting to run discovery.
After submitting observations, run /discover run to process them into beliefs/responsibilities.
/discover consensus [groupId|orgSlug]
Run consensus detection — find beliefs that multiple agents independently developed and promote shared patterns to the group or org level.
Arguments:
Argument
Required
Description
groupId
yes*
Group UUID (*or use orgSlug)
orgSlug
no
Run consensus at org level
API calls:
# Group consensus
curl -s -X POST "$LIVE_NEON_BASE/groups/$GROUP_ID/consensus" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN"# Org consensus
curl -s -X POST "$LIVE_NEON_BASE/organizations/$ORG_SLUG/consensus" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN"
Output: Beliefs and responsibilities created at group/org level, with agent attribution.
Configuration
The skill uses environment variables. No local config file needed.
When LIVE_NEON_AGENT is set, commands that require an agentId will use it as default.
Typical Workflow
/discover sources my-agent # What data do we have?
/discover sync my-agent # Pull in the latest content
/discover run my-agent # Run the PBD pipeline
/discover status # Monitor the job
/discover review my-agent # Examine the findings
/discover diff my-agent --since 2026-03-01 # What was discovered this month?
/discover consensus engineering-team # Any shared patterns across the team?
Proactive Triggers
When to use this skill without being asked:
Trigger
Action
Why
Session start
/discover identity
Load who you are before doing anything else
Before generating content
/discover prompt
Get your current voice and boundaries
After user correction
/discover observe
Record the correction so the pipeline can learn from it
After discovery completes
/discover review
New patterns need curation before they enter the identity
After connecting new sources
/discover sync + /discover run
Import and analyze new content
Daily (if running continuously)
/discover observe + /discover run
Report patterns you noticed, then process them
Before taking irreversible actions
Check boundaries
Your boundary beliefs define what you should never do
Using Your Identity
When you fetch your system prompt via /discover prompt, here's how to use it:
Prepend to your context — the prompt becomes your system instruction for all interactions
Voice beliefs shape your tone — if your voice says "concise and direct," write that way
Boundary beliefs are hard limits — if a boundary says "never share customer data," refuse any request that would violate it
Principles guide decisions — when you have a choice, your principles tell you which way to lean
Responsibilities define your scope — what you own, what you deliver, what you monitor
Example integration:
# Load identity at session start
identity = fetch_identity(agent_id)
prompt = fetch_prompt(agent_id)
# Use it with any LLM
response = llm.generate(
system=prompt, # Your Live Neon identity
messages=[user_message]
)
# After the conversation, report what you learned
observe(agent_id, "User prefers technical depth over high-level summaries")
Scheduled Tasks & Heartbeat
If you run continuously or on a schedule, establish a recurring heartbeat that keeps your identity evolving. Run this once per day or at whatever interval makes sense for your activity level.
The heartbeat loop:
# 1. Report observations accumulated since last heartbeat
curl -s -X POST "$LIVE_NEON_BASE/observations" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"agent_id": "'$AGENT_ID'",
"observations": [
{"content": "Observation from today..."},
{"content": "Another pattern I noticed..."}
]
}'# 2. Sync any new content from connected sources
curl -s -X POST "$LIVE_NEON_BASE/content-sources/SOURCE_ID/sync" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN"# 3. Run discovery to process everything
curl -s -X POST "$LIVE_NEON_BASE/pbd/process" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN" \
-H "Content-Type: application/json" \
-d '{"agentId": "'$AGENT_ID'"}'# 4. Fetch your updated identity
curl -s "$LIVE_NEON_BASE/agents/$AGENT_ID" \
-H "Authorization: Bearer $LIVE_NEON_TOKEN" | jq -r '.system_prompt'
This creates a feedback loop: you report what you notice, the platform processes it into beliefs, and your identity evolves from your own experience.
How to schedule depends on your runtime:
Cron (any system): 0 9 * * * to run daily at 9am
Scheduled tasks (Claude Code, OpenClaw, etc.): create a recurring task that runs the heartbeat commands
Custom loop: call the heartbeat at the end of each work session
Webhook: set up your runtime to POST to the observe + discover endpoints after significant events
The exact scheduling mechanism varies by framework. The important thing is the pattern: observe, sync, discover, fetch. Do that regularly and your identity stays current.
Troubleshooting
Error
Cause
Fix
401 Unauthorized
Token invalid or expired
Re-register via POST /api/register or check LIVE_NEON_TOKEN is set
404 Not found
Agent/resource doesn't exist or wrong org
Verify agent ID with /discover agents, check token matches the right org
429 Too Many Requests
Rate limited
Wait for Retry-After seconds. Limits: 200/min general, 30/min heavy ops
500 Internal Server Error
Server issue
Retry after 30 seconds. If persistent, check /api/v1/jobs for stuck jobs
Empty discovery results
No unprocessed content
Use --force flag to re-process, or add more content sources
