Skill: kaising-openclaw1/long-term-memory (version 1.0.0)

--- name: ontology description: Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access. --- # Ontology A typed vocabulary + constraint system for representing knowledge as a verifiable graph. ## Core Concept Everything is an **entity** with a **type**, **properties**, and **relations** to other entities. Every mutation is validated against type constraints before committing. ``` Entity: { id, type, properties, relations, created, updated } Relation: { from_id, relation_type, to_id, properties } ``` ## When to Use | Trigger | Action | |---------|--------| | "Remember that..." | Create/update entity | | "What do I know about X?" | Query graph | | "Link X to Y" | Create relation | | "Show all tasks for project Z" | Graph traversal | | "What depends on X?" | Dependency query | | Planning multi-step work | Model as graph transformations | | Skill needs shared state | Read/write ontology objects | ## Core Types ```yaml # Agents & People Person: { name, email?, phone?, notes? } Organization: { name, type?, members[] } # Work Project: { name, status, goals[], owner? } Task: { title, status, due?, priority?, assignee?, blockers[] } Goal: { description, target_date?, metrics[] } # Time & Place Event: { title, start, end?, location?, attendees[], recurrence? } Location: { name, address?, coordinates? } # Information Document: { title, path?, url?, summary? } Message: { content, sender, recipients[], thread? } Thread: { subject, participants[], messages[] } Note: { content, tags[], refs[] } # Resources Account: { service, username, credential_ref? } Device: { name, type, identifiers[] } Credential: { service, secret_ref } # Never store secrets directly # Meta Action: { type, target, timestamp, outcome? } Policy: { scope, rule, enforcement } ``` ## Storage Default: `memory/ontology/graph.jsonl` ```jsonl {"op":"create","entity":{"id":"p_001","type":"Person","properties":{"name":"Alice"}}} {"op":"create","entity":{"id":"proj_001","type":"Project","properties":{"name":"Website Redesign","status":"active"}}} {"op":"relate","from":"proj_001","rel":"has_owner","to":"p_001"} ``` Query via scripts or direct file ops. For complex graphs, migrate to SQLite. ### Append-Only Rule When working with existing ontology data or schema, **append/merge** changes instead of overwriting files. This preserves history and avoids clobbering prior definitions. ## Workflows ### Create Entity ```bash python3 scripts/ontology.py create --type Person --props '{"name":"Alice","email":"[REDACTED_SECRET]"}' ``` ### Query ```bash python3 scripts/ontology.py query --type Task --where '{"status":"open"}' python3 scripts/ontology.py get --id task_001 python3 scripts/ontology.py related --id proj_001 --rel has_task ``` ### Link Entities ```bash python3 scripts/ontology.py relate --from proj_001 --rel has_task --to task_001 ``` ### Validate ```bash python3 scripts/ontology.py validate # Check all constraints ``` ## Constraints Define in `memory/ontology/schema.yaml`: ```yaml types: Task: required: [title, status] status_enum: [open, in_progress, blocked, done] Event: required: [title, start] validate: "end >= start if end exists" Credential: required: [service, secret_ref] forbidden_properties: [password, secret, token] # Force indirection relations: has_owner: from_types: [Project, Task] to_types: [Person] cardinality: many_to_one blocks: from_types: [Task] to_types: [Task] acyclic: true # No circular dependencies ``` ## Skill Contract Skills that use ontology should declare: ```yaml # In SKILL.md frontmatter or header ontology: reads: [Task, Project, Person] writes: [Task, Action] preconditions: - "Task.assignee must exist" postconditions: - "Created Task has status=open" ``` ## Planning as Graph Transformation Model multi-step plans as a sequence of graph operations: ``` Plan: "Schedule team meeting and create follow-up tasks" 1. CREATE Event { title: "Team Sync", attendees: [p_001, p_002] } 2. RELATE Event -> has_project -> proj_001 3. CREATE Task { title: "Prepare agenda", assignee: p_001 } 4. RELATE Task -> for_event -> event_001 5. CREATE Task { title: "Send summary", assignee: p_001, blockers: [task_001] } ``` Each step is validated before execution. Rollback on constraint violation. ## Integration Patterns ### With Causal Inference Log ontology mutations as causal actions: ```python # When creating/updating entities, also log to causal action log action = { "action": "create_entity", "domain": "ontology", "context": {"type": "Task", "project": "proj_001"}, "outcome": "created" } ``` ### Cross-Skill Communication ```python # Email skill creates commitment commitment = ontology.create("Commitment", { "source_message": msg_id, "description": "Send report by Friday", "due": "2026-01-31" }) # Task skill picks it up tasks = ontology.query("Commitment", {"status": "pending"}) for c in tasks: ontology.create("Task", { "title": c.description, "due": c.due, "source": c.id }) ``` ## Quick Start ```bash # Initialize ontology storage mkdir -p memory/ontology touch memory/ontology/graph.jsonl # Create schema (optional but recommended) python3 scripts/ontology.py schema-append --data '{ "types": { "Task": { "required": ["title", "status"] }, "Project": { "required": ["name"] }, "Person": { "required": ["name"] } } }' # Start using python3 scripts/ontology.py create --type Person --props '{"name":"Alice"}' python3 scripts/ontology.py list --type Person ``` ## References - `references/schema.md` — Full type definitions and constraint patterns - `references/queries.md` — Query language and traversal examples ## Instruction Scope Runtime instructions operate on local files (`memory/ontology/graph.jsonl` and `memory/ontology/schema.yaml`) and provide CLI usage for create/query/relate/validate; this is within scope. The skill reads/writes workspace files and will create the `memory/ontology` directory when used. Validation includes property/enum/forbidden checks, relation type/cardinality validation, acyclicity for relations marked `acyclic: true`, and Event `end >= start` checks; other higher-level constraints may still be documentation-only unless implemented in code.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)