OpenClaw Knowledge Runtime

Build a standalone layered knowledge runtime with typed links across knowledge entries, entities, memories, and reusable assets. Use when designing or implem...

Security Scan

The skill's declared purpose, instructions, and included code are internally consistent and proportional: it defines a local, standalone knowledge runtime using local file paths and adapter surfaces and requests no credentials or installs.

Purpose & Capability

Name/description match the actual contents: documentation and a tiny index.js implement a standalone knowledge/runtime design. Required artifacts (storage files, adapters, record types) align with the stated goal; nothing asks for unrelated cloud credentials or external services.

Instruction Scope

SKILL.md confines activity to building queries, reading layered stores, one‑hop link expansion, ranking, and writing back only stable findings. It references default local storage paths under memory/knowledge/*. This is appropriate for a local runtime, but the instructions imply the agent will read and (occasionally) write files in those locations when integrated — verify host adapters constrain what gets read/written and avoid dumping raw logs or sensitive data into the store.

Install Mechanism

No install spec. The skill is instruction-only plus a small index.js and docs; nothing downloads or executes external installers or archives.

Credentials

No required environment variables, credentials, or config paths are declared. The runtime uses only local file paths and adapter interfaces as described — proportional to the stated functionality.

Persistence & Privilege

always is false and the skill does not request persistent platform privileges. It describes write-back behavior but leaves actual persistence to the host through adapters; no self-enabling or cross-skill configuration changes are present.

Assessment

This skill appears coherent and local-only, but before installing: (1) confirm where the host will place and permission the memory/knowledge/* files so sensitive information isn't written to an insecure location; (2) review any host adapter code that integrates this runtime (query_builder, retrieval_selector, write_back) to ensure it filters sensitive inputs and enforces write-back rules; (3) when deploying in production, enforce retention/rotation and access controls on the knowledge store and audit what the skill writes to avoid accidentally persisting raw logs or secrets.

What This Skill Does

Use this skill to design or implement a standalone knowledge runtime that can:

1. Read layered memory and knowledge sources.
2. Retrieve the most relevant knowledge for the current role, objective, and signals.
3. Link knowledge to entities, genes, tasks, and prior events.
4. Write stable findings back after successful runs.

Why Install It

This skill is useful when an agent already has memories, logs, tasks, and reusable assets, but they are still scattered across unrelated files or stores.

Use it to:

• turn scattered memory into a layered runtime
• add typed links between knowledge, entities, events, and reusable assets
• return a compact retrieval bundle for prompts, ranking, and observability
• keep write-back strict so the store stays durable instead of noisy

Quick Start

Follow this default sequence:

1. Define the two-axis memory model with layers and scopes.
2. Store knowledge_entry, knowledge_link, and entity records.
3. Build a query from role, objective, direction, and recent signals.
4. Rank candidates, expand one hop through typed links, and trim results.
5. Expose a small output bundle to prompts, task ranking, and dashboards.
6. Write back only stable findings after successful runs.

Memory Model

Use two axes.

• Layers: working, episodic, semantic, procedural, policy
• Scopes: session, shared, published

Default placement rules:

• gene, capsule, skill, and reusable playbooks belong to procedural.
• Event logs, task outcomes, and run histories belong to episodic.
• Stable conclusions and research briefs belong to semantic.
• User constraints and system rules belong to policy.

Core Records

The runtime should center on three record types:

• knowledge_entry: the main unit of stored knowledge
• knowledge_link: a typed relationship between records
• entity: the canonical form of a repo, module, topic, paper, person, org, or asset

Storage

Default files:

• memory/knowledge/knowledge_store.jsonl
• memory/knowledge/knowledge_links.jsonl
• memory/knowledge/knowledge_index.json
• memory/knowledge/entity_index.json

Retrieval Flow

When retrieval is needed:

1. Build the current query from role, objective, direction, query bundle, and signals.
2. Retrieve candidate knowledge from layered sources.
3. Expand one hop through typed links.
4. Return a compact bundle with:
   • knowledge_hits
   • knowledge_bias_tags
   • linked_entities
   • linked_genes
   • memory_layers
   • knowledge_context_preview

Typed Links

Recommended relations:

• mentions_entity
• supports_gene
• derived_from_event
• abstracts_task
• contradicts
• supersedes
• same_topic_as
• evidence_for
• used_by_cycle

Write-Back Rule

Only write back stable, high-signal findings.

• Good: validated findings, repeated problem patterns, reusable research summaries
• Bad: raw logs, speculative notes, temporary scratch content

Adapter Surfaces

Keep the runtime decoupled from any one agent loop. Plug it into host systems through generic adapters:

• query_builder: turns role, objective, and signals into a retrieval query
• retrieval_selector: ranks hits and prepares the runtime output bundle
• task_ranker: adds knowledge relevance into task or action scoring
• prompt_context: injects a compact knowledge block into prompts
• write_back: records durable findings after successful runs
• observability: exposes hit counts, linked entities, and layer coverage to reports or dashboards

Additional Resources

Use these files:

• README.md: overview, use cases, and integration checklist
• examples.md: example retrieval, ranking, and write-back flows
• reference.md: record schemas, output shape, and adapter details