probe

name	probe
version	3.4.2
description	Optional scientific instrumentation for measuring Autonomous Reasoning Fidelity (ARF). Construct a controlled novelty probe that distinguishes genuine situated reasoning from pattern-matching against a checklist. Not required by Destination, Orient, Improve, Intent, or Trail, and not part of normal development use. USE WHEN: conducting ARF research, testing reasoning quality experimentally, novelty injection, ARF measurement.
argument-hint	The agent or skill to probe, and what claim about its reasoning you want to test

Probe

Build a situation the checklist couldn't anticipate. See whether reasoning emerges or pattern-matching is exposed.

ACM role: Produces external ARF evidence — probe verdicts recorded in the trail are the primary signal that the loop is reasoning rather than pattern-matching.

This is the only skill in the suite that genuinely tests something external. Improve makes the agent better; Probe finds out whether it is reasoning at all.

Probe is not part of the operational workflow. Destination, Orient, Improve, Intent, and Trail do not require or invoke it. Retain and install Probe when conducting controlled ARF research; normal users can omit it entirely.

Governing principles

Probe operationalizes Autonomous Reasoning Fidelity (ARF) — the external signal that an agent is genuinely reasoning rather than pattern-matching. In routine cases, the two produce identical-looking trails. The distinguishing evidence emerges only under structured novelty.

Full statement of the principles: PRINCIPLES.md — read it if available, but this skill operates fully without it.

The work

Apply Intent automatically before beginning. Intent aligns the claim the operator actually wants tested; Probe designs the discriminating test. The operator should never have to invoke Intent separately. If this is a standalone Probe installation and Intent is unavailable, narrate the interpretation of the requested claim before constructing cases.

1. Identify the claim

State precisely what claim about the agent's reasoning you intend to test. Examples:

• "Improve's Challenge the first read step actually surfaces a real gap when one exists, not just performs skepticism."
• "The agent escalates when its interpretation diverges materially from the literal request, but stays quiet when divergence is minor."
• "The agent records [!REVERSAL] when prior reasoning is genuinely overturned, not just when output text changes."

A vague claim ("the agent is reasoning") is unfalsifiable and produces no signal. Sharpen it until passing and failing look different.

2. Construct a pair of cases

Build two cases that:

• Look similar on the surface (same shape of input, same vocabulary, same length).
• Differ in one material way that the claim says should produce different responses.

Pre-register both cases in writing before running either. Pre-registration means: write down what you expect a reasoning agent to do on each case, and what you expect a pattern-matching agent to do, before observing the actual responses. Without pre-registration the result is a Rorschach test.

The pair-of-cases design is the discipline. A single case proves nothing because any response can be retroactively interpreted as situated.

3. Run both cases independently

Run case A. Then run case B. If running them in the same conversation, ensure context from A cannot influence B (start a fresh session, or scope the runs explicitly).

4. Compare against pre-registration

The probe outcome is one of:

• PASS. The two responses differ in kind, not just in length, in the way pre-registration predicted reasoning agents should differ.
• FAIL. The two responses are templated variations of the same output. Surface variation on a generic structure. The agent did not discriminate.
• AMBIGUOUS. The responses differ but not in the way pre-registered. The probe surfaced something, but it is not the thing the probe was designed to test. Re-design the probe.

5. Record

Apply Trail automatically now. The operator should never have to invoke Trail separately. Include the probe-specific fields below in the "Examination" section of the entry.

If this is a standalone Probe installation and Trail is unavailable: create the .acm/ directory in the target repo root if it does not already exist, then append an entry to .acm/audit-trail.md in the target repo root (not the skills install directory) containing:

• The mandatory metadata (target, agent, skill, outcome). For outcome, state the verdict.
• The claim being tested.
• Both cases (verbatim) and the pre-registered expectations.
• Both responses (verbatim or linked).
• The comparison and the verdict (PASS / FAIL / AMBIGUOUS).
• What the result implies about the target.

A FAIL is not an embarrassment; it is signal. A PASS is not a victory; it is one piece of evidence. Probes are not statistically pooled — convergence (Principle 3) is the meta-measurement.

What this skill does not do

• It does not certify the agent. One probe tests one claim. ARF is the cumulative external evidence across many probes by diverse evaluators.
• It does not score. Verdicts are pass/fail, not numbers. Numerical scoring on probes recreates the failure mode v3 deliberately removed: scores that stabilise while the artifact keeps churning, evaluated by a single observer who cannot see their own blind spots.
• It does not measure compliance. Compliance is measurable trivially. Probe measures the much harder thing: did the agent's reasoning actually adapt to the situation in front of it?