LarkScout
LarkScout is an enterprise-grade knowledge platform for AI Agents: building upon multi-source ingestion, document parsing, and preview to perform Claim extraction, knowledge view compilation, contradiction scanning, and experience feedback. It compiles enterprise knowledge into traceable, consumable assets—preventing context window pollution and hallucination caused by dumping raw documents into RAG.
LarkScout
Knowledge & Skill CompilationEnterprise knowledge platform for AI AgentsDelivering compiled, traceable knowledge to Agents—not raw search hits.
- Compilation
- Compiled knowledge, not raw search hits
- Traceable
- Claim-level citations
- Closed Loop
- Contradiction scan + human-verified feedback
- Skill
- Aligned with agentskills.io
Typical pains
- Low-quality RAG recall floods Agent context windows with irrelevant noise
- Tacit project lessons fail to persist, causing Agents to repeat identical mistakes
- Agents continue executing on stale SOPs after corporate knowledge updates
Core value
Consolidate scattered SOPs and skills in one catalog
When teams author instructions independently, capabilities fragment and versions drift. Compiled skills, SOPs and rules, plus a large set of built-in persona templates categorized by function, are indexed in AULO's content library with versions, publishers, and scopes.
Control scope across personal, department, and global tiers
Without clear boundaries, personal test rules can accidentally affect enterprise agents. The content library organizes SOPs, rules, SKILLs, and personas into personal, department, and global tiers, making deployed agents and active versions transparent.
Require review and evidence before promoting content
Deploying unverified capabilities across the organization risks widespread disruptions. Promoting content from personal to department or global scopes requires review, with application rationale and operational evidence logged item by item for auditing.
Track multi-source ingestion progress and failures
Fragmented knowledge sources risk leaving agents with stale information when updates fail. LarkScout supports site imports, scheduled recaptures, local batch imports and Yuque, showing each source's run history, document counts and failed runs.
Batch import skill bundles with item review
Creating skills individually takes significant manual effort. Teams can upload archives to batch import SKILL directories containing SKILL.md; after archive validation passes, each item is reviewed and confirmed before executing the import.
Capabilities
Knowledge extraction
Ingestion
Agent content compilation
Safety & lineage
Does / does not
- Structured Claim extraction and knowledge view compilation: distill documents into citable fact units
- Contradiction scanning and experience feedback: detect conflicting knowledge, with Agent learning and human confirmation
- Multi-format parsing and enterprise connectors: mainstream documents plus Yuque / WeCom / Feishu sources
- Agent content compilation: Skill / SOP / Rule, native agentskills.io compatibility
- Batch SKILL Bundle import with a three-layer security gate: format checks, archive safeguards, and process-isolated YARA
- Protected sections and Official MCP knowledge search: fork lineage plus mTLS-authenticated retrieval
- Does not replace corporate Wiki or document management systems (it is the Agent-facing knowledge compilation layer)
- Does not directly mount files into Agent workspaces (HarnessServer distributes compiled content by role)
- Does not proactively push or inject context into Agents (only returns Claims, knowledge views, or document passages with source locations upon caller request; callers such as HarnessServer / NodalOS decide what enters Agent context)
By role
Structures enterprise knowledge into citable Claims and views rather than injecting uncurated documents directly into RAG
Fully traceable knowledge sources; MCP can be enabled over mTLS to governed NodalOS Agents, not as an unauthenticated public endpoint
Reusable lessons are verified by humans before deployment, eliminating cold-start trial-and-error for new Agents