Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
No file changes detected in this version. - No updates or changes were introduced in this release.
What This Skill Does
Babel Epistemic Convention tracks confidence levels across multi-agent handoffs. Without it, inferences made by one agent silently become verified facts by the time they reach a third agent. The skill provides a tagging vocabulary for confidence, intent, register, affect, grounds, and trajectory that travels with the work.
Confidence calibration propagates through the forwarding block without any shared infrastructure, so the receiving agent does not need the skill installed.
When to use it
- Tagging a news-sourced inference as REPORTED before passing it to a downstream summarizer
- Preventing a derived metric from being treated as confirmed data in a financial pipeline
- Flagging regulatory constraints that must remain non-overridable across agent delegations
- Marking a four-month declining trend as ESCALATE rather than a neutral INFORM update
- Auditing a three-agent research chain for confidence laundering after incorrect outputs
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Research Acme Corp's potential Vanta partnership and hand off to the drafting agent.
- 1Search for partnership mentions and find a single unverified news item
- 2Label the claim REPORTED at 0.25 confidence, not VERIFIED_DATA
- 3Set intent to INFORM and consider switching to FLAG_RISK given low confidence
- 4Append the Babel forwarding block to the handoff output
- 5Drafting agent inherits the label as REPORTED and does not promote it to verified in the draft
Handoff payload with per-assertion confidence tags intact; drafting agent qualifies the claim as unconfirmed in the output.