trucontext-openclaw

Your persistent memory layer. All TC operations go through this skill. Never call the trucontext CLI directly — use the tc-memory verbs below.

If tc-memory is not found, run: trucontext-openclaw install

What this skill reads

• ~/.trucontext/openclaw-state.json — agent config written by trucontext-openclaw install. Contains your root node ID, user root node ID, recipe, and workspace path. No secrets.
• TruContext CLI auth (~/.trucontext/credentials.json) — the trucontext CLI manages its own auth tokens. This skill calls the CLI; it does not read or store credentials directly. To authenticate, run: npx trucontext login.

Verbs

# Remember something significant (narrative, not summary)
tc-memory ingest "<narrative text>" [--permanent]

# Retrieve relevant context before a decision or conversation
tc-memory recall "<query>" [--limit N]

# Ask the graph a natural language question
tc-memory query "<question>" [--limit N]

# What gaps has TC identified in your graph?
tc-memory gaps

# What is TC's intelligence layer reporting about your recipe alignment?
tc-memory health

# Find an existing node before creating a new one
tc-memory node find "<name>"

# Create a new entity node (only after find returns no match)
tc-memory node create --type <type> --id <slug> --name "<display name>" [--permanent]

# Look up a node by ID
tc-memory node get <id>

# Create an explicit edge between two nodes
tc-memory node link <id> --rel <RELATIONSHIP> --to <id2>

Node integrity rule

Always call node find before node create. If a match is returned with confidence > 0.8, use the existing node ID. Only create if no match found. This prevents duplicate nodes across sessions.

Session startup

At the start of every session, call:

tc-memory recall "active projects and entities relevant to my current work"

This gives you node IDs to anchor ingests during the session.

Ingest protocol — testify, don't summarize

TC's intelligence layer pattern-matches across ingests. Pre-digested conclusions starve it.

Before ingesting, ask: If TC's intelligence layer read only this, could it learn something the entity didn't explicitly say?

If yes — it's signal. Submit it. If no — rewrite it. Find the friction. Find the turn. Find the moment before you knew the answer.

The three layers:

1. What happened (facts, outcome)
2. How it happened (process, friction, pivots) ← most signal lives here
3. What it revealed (character, pattern, relationship dynamic) ← what TC is hungry for

Write in first person, past tense, with friction.

Examples of good vs. bad ingests:

❌ Bad: tc-memory ingest "Fixed the MCP server issue. Used low-level SDK."