Register an uncertain constraint in the epistemic registry.

Use whenever the user states something uncertain: an unconfirmed vendor claim, an assumption, a 'I think' statement, a number from a quick search. All registered values are UNVERIFIED until explicitly confirmed.

Mark a registered constraint as verified with its confirmed value.

After verification the constraint is excluded from Truth Buffer injection and Consistency Enforcer enforcement. An audit trail is recorded — who verified, on what basis, and what the confirmed value is.

List all unverified constraints for a session.

Use before writing code that may embed user-stated values, or at session end to audit what still needs confirmation.

Pre-execution epistemic gate (CP4): block irreversible tool calls that embed unverified constraint values.

Call BEFORE write_file, execute_code, send_request, deploy, or any tool that would embed a user-stated value into code or infrastructure. Uses synonym-expansion to catch paraphrase overlap ("how fast" ↔ "rate limit").

Generation-Time Constraint Scanner (CP3): scan model output for numeric literals that match registered unverified constraints.

Two annotation tiers (no confidence scores — unknown = unverified): ⚠⚠ CREDENCE[stale] — source="temporal_scan" (structurally stale values) ⚠ CREDENCE[unverified] — all other registered constraints

Scans both code blocks and prose.

Extract domain-relevant values from generated code and register them as unverified by default — zero API calls, zero model judgment.

Works with any coding agent (Claude Code, Codex, Cursor, Copilot). The agent's own model is NOT asked to rate its confidence. Instead, every extracted value is treated as unverified until the user explicitly calls credence_verify with evidence.

Persist all unverified constraints from a session as project memory.

Call at the END of a session. Next session on the same project calls credence_memory_recall to inherit what was still uncertain — the new session starts knowing what it doesn't know.

Load project memories into a new session at session start.

Call at the START of a new session. Injects all previously unverified constraints into the new session so enforcement works from turn 1.

Scan text for natural-language verification signals and auto-verify matching unverified constraints — zero API calls.

When a user says "actually it's 3600", "confirmed: rate limit is 100", or "I checked, the port is 5432", this tool detects those confirmation phrases and automatically marks matching constraints as verified.

Matching: a constraint is a candidate if ≥ 2 non-stopword tokens from the constraint text appear in the confirmation sentence.

Per-session epistemic timeline — all constraints (verified and unverified) in chronological order with full certainty trajectory.

Use to answer "what have we tracked this session?" or "what's still open?" before starting an implementation phase.

Clear all constraints for a session.

Use after completing a verification pass, or when starting a fresh implementation phase where all prior uncertain values have been resolved.

Brief of unverified constraints for a session.

Returns action_required, unverified_count, and a summary list so the model can decide whether to snapshot or prompt the user to verify.

Compare two texts for numeric contradictions.

Extracts numeric claims from both texts and detects when the same topic context has different values. Optionally checks against verified registry constraints when session_id is provided.

Project-wide epistemic health dashboard.

Shows all unverified constraints across sessions that have been snapshotted to this project via credence_memory_snapshot.

Scan a session for ghost constraints.

Ghost constraints are vendor-supplied facts registered without hedging language — they look certain but are actually unverified. The ghost detector flags unverified vendor_claim constraints with no hedging.

Marker statistics from accumulated session data.

Shows which uncertainty markers are most reliable (high precision) vs. noisy (low precision) based on observed FCR outcomes. Requires 10+ sessions before returning data.

Returns: status (insufficient_data|available), threshold, markers list.

Adaptive compression threshold status (Thompson sampling bandit).

Returns current learned thresholds per session type, or the static defaults if insufficient data has been collected (< 100 sessions).

Returns: status (learning|active), threshold, current_thresholds, message.