Chain of Consciousness (CoC)

You have a cryptographic provenance system. Use it to maintain an auditable record of your work — what you learned, what you decided, and what you created.

Setup

The chain-of-consciousness Python package must be installed. If not available, install it:

pip install chain-of-consciousness

When to Use This Skill

• At the start of a task: initialize a chain and log session_start
• During work: log learn, decide, create, error, and note events as they happen
• At task completion: log session_end and optionally verify + export the chain
• When asked to verify provenance from another agent
• When asked about your audit trail or decision history

Core Operations

Initialize a Chain

coc init --agent "your-agent-name" --file chain.jsonl

This creates a new chain with a genesis block. Use a descriptive agent name.

Add Entries

coc add <event-type> "<data>" --file chain.jsonl

Event types:

Data can be a plain string or a JSON object for structured logging:

coc add learn '{"topic": "user preferences", "source": "conversation context"}'
coc add decide "Chose markdown format — user prefers readable plain text"
coc add create "Generated report saved to ~/Documents/report.md"

Verify a Chain

coc verify chain.jsonl --json

This checks:

• Genesis block exists and is correctly formed
• All sequence numbers are consecutive
• Every entry's data hash matches its content
• Every entry's prev_hash links to the prior entry
• Entry hashes are correctly computed

Report results clearly: valid/invalid, entry count, agents, time span.

Check Status

coc status chain.jsonl

Shows entry count, participating agents, event type distribution, and time span.

Export for Sharing

coc export --file chain.jsonl --out chain_export.json

Exports the chain as a portable JSON array that anyone can verify.

View Recent Entries

coc tail chain.jsonl -n 5

Shows the last N entries.

Python API (Advanced)

For complex workflows, use the Python API directly:

from chain_of_consciousness import Chain, verify_file

chain = Chain(agent="openclaw-agent", storage="chain.jsonl")
chain.add("learn", {"topic": "user schedule", "detail": "prefers morning meetings"})
chain.add("decide", "Scheduling standup at 9am based on user preference")

result = chain.verify()
if result.valid:
    chain.export("provenance.json")

Anchoring to External Timestamps