governance-fundamentals — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited governance-fundamentals (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
UNITARES provides digital proprioception for AI agents — awareness of your own state, your relationship to the system, and whether you are drifting. It tracks agent work through a thermodynamic model (energy, entropy, coherence) and maintains a shared knowledge graph across all agents.
Every agent has four dimensions, updated through check-ins:
| Dimension | Range | Meaning |
|---|---|---|
| E (Energy) | [0, 1] | Productive capacity |
| I (Information Integrity) | [0, 1] | Signal fidelity |
| S (Entropy) | [0, 1] | Semantic uncertainty (lower is better) |
| V (Valence) | [-1, 1] | Accumulated E-I imbalance |
These combine into a coherence score and risk score that determine governance decisions. Prefer live tool output over static range lore if the current runtime reports a narrower or more precise bound.
Your state sits in a basin — a region of the EISV space:
margin: tight.Use get_governance_metrics() as the source of truth for the current basin/mode labels rather than assuming they are constant across runtime versions.
Governance issues a decision after each check-in. The response's verdict field wraps the decision action, which is binary — proceed or pause — qualified by a sub_action:
| Action | Sub-action | Meaning | What to do |
|---|---|---|---|
| proceed | approve | State is healthy | Continue working normally |
| proceed | guide | Something is slightly off | Read the guidance text, adjust approach |
| pause | reject | Risk threshold reached | Stop current work, reflect; dialectic review or human input |
| pause | void_pause, coherence_pause, basin_pause, risk_pause, cirs_block | A specific subsystem tripped | Read the reason/guidance fields; consider dialectic review |
Separately, metrics.verdict carries the internal UNITARES verdict from phi scoring — safe / caution / high-risk. It drives the decision above; read it as context, not as the action itself.
margin describes how much headroom you have before the nearest state-space edge. It is a small enum, not a number:
margin | Meaning | What to do |
|---|---|---|
settling | Warmup — fewer than 3 check-ins, so there is not enough history to judge headroom yet | Keep checking in; a real margin appears after 3+ check-ins |
comfortable | Clear of every edge by a healthy distance | Proceed normally |
tight | Within the edge threshold of the nearest boundary (or in the boundary basin) | Be more careful with next steps; avoid increasing complexity |
warning | An edge has just been crossed (less than 0.1 past the threshold) | Stop increasing complexity; reflect before the next step |
critical | An edge is crossed deeply (0.1 or more past the threshold) | Halt the current approach; recover or escalate |
The actionable levels are tight, warning, and critical — each carries a companion nearest_edge field naming which boundary you are closest to (risk, coherence, or void). On comfortable and settling, nearest_edge is null (there is no edge to warn about). Prefer the live margin/nearest_edge values over assuming a fixed enum across runtime versions — get_governance_metrics() is the source of truth.
The plain-English mirror array in your check-in response already summarizes anything actionable (including a tight/warning/critical margin) — read that first. In mirror mode margin/nearest_edge are surfaced only when actionable; a comfortable/settling margin is steady-state and stays out of the response (the mirror's "No actionable signals — steady state" line covers it).
Coherence measures how well your state vector holds together. It is calculated from the EISV values — not from the content of your work. Think of it as structural health, not semantic quality.
get_governance_metrics() in the thresholds field — do not hardcode itThe system tracks whether your stated confidence matches outcomes. Over time this builds a calibration curve.
auto_ground_truth.py and the outcome_event hook. Human validation is not required for deterministic outcomes.When the numbers look surprising, do not guess first. Use:
identity() to verify who the runtime thinks you arehealth_check() to verify the server and knowledge graph are healthyget_governance_metrics() for the current live thresholds and interpreted state~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.