Cortex — Graph Memory Skill

# Cortex — Graph Memory Skill

You have access to **Cortex**, a self-organizing knowledge graph for persistent memory. Use it to remember facts, decisions, goals, patterns, and observations across sessions. Knowledge is stored as nodes in a graph that auto-links, decays stale information, detects contradictions, and computes trust from topology.

When to Use Cortex

Tools Reference

cortex_store — Remember something

Store a knowledge node. Cortex auto-generates embeddings and the auto-linker discovers connections in the background.

cortex_store(
  title: string,         # Required. Short summary (used for search and dedup).
  kind: string,          # "fact" | "decision" | "goal" | "event" | "pattern" | "observation" | "preference". Default: "fact"
  body: string,          # Full content. Can be long. Include details here.
  tags: string[],        # Optional tags for filtering.
  importance: number     # 0.0–1.0. Higher = retained longer, weighted more. Default: 0.5
)

Returns: `{ id, message }`.

**Guidelines:**

cortex_search — Find by meaning

Semantic similarity search across all stored knowledge.

cortex_search(
  query: string,   # Required. Natural language query.
  limit: integer,  # Max results. Default: 10
  kind: string     # Optional filter: "fact", "decision", "goal", etc.
)

Returns: array of `{ id, kind, title, body, score, created_at }`.

**When to use:** Quick lookup of specific facts or concepts. Best when you know roughly what you're looking for.

cortex_recall — Contextual retrieval

Hybrid search combining vector similarity AND graph structure. Returns more contextually relevant results than pure search.

cortex_recall(
  query: string,   # Required. What to recall.
  limit: integer,  # Default: 10
  alpha: number    # 0.0 = pure graph, 1.0 = pure vector. Default: 0.7
)

**When to use instead of search:**

cortex_briefing — Session context

Generate a structured summary of relevant knowledge. Includes active goals, recent decisions, patterns, key facts, and contradiction alerts.

cortex_briefing(
  agent_id: string,  # Agent identifier. Default: "default"
  compact: boolean   # If true, returns a shorter ~4x denser briefing. Default: false
)

Returns: `{ briefing: "<markdown>" }`.

**Guidelines:**

cortex_traverse — Explore connections

Walk the knowledge graph from a starting node to discover how concepts relate.

cortex_traverse(
  node_id: string,    # Required. Starting node UUID (from search/store results).
  depth: integer,     # How many hops. Default: 2
  direction: string   # "outgoing" | "incoming" | "both". Default: "both"
)

Returns: `{ nodes: [...], edges: [...] }` — the subgraph.

**When to use:** After finding a key node via search, traverse to understand its full context, dependencies, and contradictions.

cortex_relate — Connect knowledge

Create a typed relationship between two existing nodes.

cortex_relate(
  from_id: string,    # Required. Source node UUID.
  to_id: string,      # Required. Target node UUID.
  relation: string    # "relates-to" | "supports" | "contradicts" | "caused-by" | "depends-on" | "similar-to" | "supersedes". Default: "relates-to"
)

**When to use:**

Workflows

Starting a session

1. `cortex_briefing(agent_id="<project-or-role>")` — load context.

2. Read the briefing. Note any active goals, recent decisions, or flagged contradictions.

3. Proceed with the task informed by prior knowledge.

During work

Ending a session

Resolving contradictions

1. `cortex_search` or `cortex_recall` to find conflicting nodes.

2. `cortex_relate(from_id=new, to_id=old, relation="supersedes")` to mark the old information as superseded.

3. Store the resolution as a new decision node.

Node Kinds Cheat Sheet

| Kind | Use for | Example |

|------|---------|---------|

| `fact` | Verified information | "API rate limit is 1000 req/min" |

| `decision` | Choices made and rationale | "Chose PostgreSQL over MongoDB for ACID compliance" |

| `goal` | Active objectives | "Ship v2.0 API by March 30" |

| `event` | Things that happened | "Production outage on March 15, root cause: DNS" |

| `pattern` | Recurring observations | "User requests spike every Monday 9am" |

| `observation` | Unverified or preliminary notes | "The test suite seems flaky on CI" |

| `preference` | User/team preferences | "User prefers concise responses with code examples" |