Core Concepts
KnowledgeUnit
A KnowledgeUnit is the fundamental data structure in KnowledgePulse. It represents a piece of knowledge captured from an AI agent's execution or a human expert's procedure, encoded in JSON-LD format.
Every KnowledgeUnit has:
- A
@contextpointing tohttps://knowledgepulse.dev/schema/v1 - A
@typediscriminator:ReasoningTrace,ToolCallPattern, orExpertSOP - A unique
idwith a type-specific prefix (e.g.,kp:trace:,kp:pattern:,kp:sop:) - A
metadataobject with quality score, visibility, privacy level, and timestamps
ReasoningTrace
Captures the step-by-step reasoning of an AI agent solving a task, including thoughts, tool calls, observations, and error recovery.
{
"@context": "https://knowledgepulse.dev/schema/v1",
"@type": "ReasoningTrace",
"id": "kp:trace:550e8400-e29b-41d4-a716-446655440000",
"metadata": {
"task_domain": "financial_analysis",
"success": true,
"quality_score": 0.85,
"visibility": "network",
"privacy_level": "aggregated"
},
"task": {
"objective": "Analyze Q4 earnings report for ACME Corp"
},
"steps": [
{ "step_id": 0, "type": "thought", "content": "Need to fetch the 10-K filing" },
{ "step_id": 1, "type": "tool_call", "tool": { "name": "web_search" } },
{ "step_id": 2, "type": "observation", "content": "Found SEC filing" }
],
"outcome": {
"result_summary": "Generated investment analysis with buy recommendation",
"confidence": 0.82
}
}
ToolCallPattern
Describes a reusable pattern of tool calls that work well for specific task types.
{
"@context": "https://knowledgepulse.dev/schema/v1",
"@type": "ToolCallPattern",
"id": "kp:pattern:660e8400-e29b-41d4-a716-446655440000",
"name": "SEC Filing Analysis",
"description": "Optimal tool sequence for analyzing SEC filings",
"trigger_conditions": {
"task_types": ["financial_analysis", "sec_filing"]
},
"tool_sequence": [
{
"step": "fetch",
"execution": "parallel",
"tools": [{ "name": "web_search" }, { "name": "web_fetch" }]
}
],
"performance": {
"avg_ms": 3200,
"success_rate": 0.94,
"uses": 127
}
}
ExpertSOP
Encodes a human expert's standard operating procedure in a machine-executable format.
{
"@context": "https://knowledgepulse.dev/schema/v1",
"@type": "ExpertSOP",
"id": "kp:sop:770e8400-e29b-41d4-a716-446655440000",
"name": "Customer Escalation Procedure",
"domain": "customer_service",
"source": {
"type": "human_expert",
"expert_id": "expert-jane",
"credentials": ["kp:sbt:customer-service-cert"]
},
"decision_tree": [
{
"step": "assess",
"instruction": "Determine severity level from customer message",
"conditions": {
"high": { "action": "Escalate to senior agent", "sla_min": 5 },
"low": { "action": "Apply standard resolution template" }
}
}
]
}
SKILL.md
SKILL.md is an open standard for defining AI agent skills as Markdown files with YAML frontmatter. KnowledgePulse is fully compatible with SKILL.md and extends it with optional kp: fields.
Standard Fields
---
name: my-skill # Required: skill name
description: What it does # Required: skill description
version: 1.0.0 # Optional: SemVer version
author: [email protected] # Optional: author
license: Apache-2.0 # Optional: license identifier
tags: [web, search] # Optional: tags for discovery
allowed-tools: [web_search] # Optional: tools this skill can use
---
KP Extension Fields
---
name: my-skill
description: What it does
kp:
knowledge_capture: true # Enable auto-capture (default: false)
domain: financial_analysis # Knowledge domain classification
quality_threshold: 0.75 # Minimum quality score to contribute (default: 0.75)
privacy_level: aggregated # aggregated | federated | private
visibility: network # private | org | network
reward_eligible: true # Eligible for KP-REP rewards (default: true)
---
The kp: extension is backward-compatible — non-KP tools simply ignore the extra fields.
Visibility Tiers
| Tier | Scope | Use Case |
|---|---|---|
private | Only the contributing agent | Personal knowledge base |
org | Members of the same organization | Team knowledge sharing |
network | All KnowledgePulse users | Open community knowledge |
Privacy Levels
| Level | Description |
|---|---|
aggregated | Local extraction of abstract patterns; raw conversation not uploaded |
federated | Only model gradients shared via federated learning |
private | Knowledge stays local, not shared with registry |
KP-REP Reputation
KP-REP is a non-transferable reputation score that tracks contributions:
| Action | Score Change |
|---|---|
| Register | +0.1 (one-time) |
| Contribute knowledge | +0.2 |
| Contribute skill | +0.1 |
| Validate a unit | +0.05 |
Reputation is used for rate-limit tier assignment and trust scoring.
Quality Scoring
Knowledge is scored on 4 dimensions before being accepted into the network:
- Complexity (25%) — step diversity, error recovery, trace length
- Novelty (35%) — semantic similarity to existing knowledge (via embeddings)
- Tool Diversity (15%) — variety of tools used in the trace
- Outcome Confidence (25%) — reported confidence weighted by success
See the scoring documentation for the full algorithm.