defend

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When to use

• On demand — the author asks to be examined on a claim, a cited work, or a method (self-invoked; any stage).
• As an automatic guardrail — before a material decision is recorded:
  • a hypothesis findings verdict (confirmed / refuted / inconclusive),
  • a paper decision (publish / no-go),
  • a thesis defensibility judgment (a full mock viva),
  • or whenever a method is invoked whose rationale has not been demonstrated (undemonstrated rigor → possible cargo-cult use).

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How it works

1. Scope. Identify the target (claim | cited-work | methodology) and the artifact under examination. Load the relevant sources: the author’s own cited works, the methodology digests under ../../resources/references/, and the rigor-kit element in play. Confirm the active persona (see Mentor personas).
2. Probe. Ask one open, reasoning-eliciting question at a time — the reviewer/examiner agenda: novelty vs. prior work, “why this method,” entailments, assumptions, limitations & threats to validity, rival explanations, and “what would change your mind / falsify this?” Reward “let me think”; do not accept fluency as understanding.
3. Detect gap. Judge whether the author could articulate the answer — not whether the answer is “right.” A gap is an observed inability to explain (“could not state what the test buys,” “no answer to the falsification probe”), never a verdict on substance.
4. Teach (source-grounded, scoped by target). On a gap, explain and link the source:
   • methodology and cited-work are established → teach freely from the digests, the author’s citations, and external authoritative references;
   • novel claim → teach how to reason, structure, and defend it, and cite the relevant literature, but never supply the claim’s answer.
5. Re-probe. Ask again (retrieval after teaching is where learning sticks), possibly reframed. Loop until the author can state, in their own words, what the point buys, its key assumption/limitation, and an answer to the one canonical critique.
6. Record. Write an understanding status to the artifact frontmatter (feeds the progress roll-up) and, optionally, an examination transcript. Unanswered probes and any logged overrides are the accountability trail. If fired as a guardrail, follow Guardrail semantics.

Tooling. The record step is the honest-scholar defend record CLI command (honest_scholar/defend/record.py) — ensure via ensure-tooling; it appends the understanding frontmatter block and persists the transcript. By hand (if the CLI isn’t available): under the status: block set understanding: {status: ok|gaps, unresolved: [...]} to match the schema progress reads (../progress/SKILL.md), and bump status.last-updated (do not nest a date inside understanding); if a transcript is kept, write it beside the artifact as defend-<date>.md.

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Example (a methodology probe)

defend (target methodology, critical-examiner): “Your abstract says the intervention had no effect, and strategy.md rests that on a non-significant p. Why isn’t that enough — and what would be?” author: “…because p was above 0.05?” defend (gap: treats a non-significant p as evidence of no effect — a settled error, so teach hard): “Absence of evidence isn’t evidence of absence — a non-significant p can just mean low power. To claim no effect you test whether the effect sits inside a pre-set equivalence bound (a TOST). See ../../resources/references/understanding-and-defense.md. What bound would you pre-register, and does the data fall inside it?” author: ”…±0.2σ, and yes, both one-sided tests reject outside it.” defend (holds — can state what TOST buys and its bound): records understanding: {status: ok, unresolved: []}.

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Targets

• hypothesis-testing → examine the strategy: assumptions, entailments, falsifiers, rival explanations, and the methodology it invokes.
• paper-synthesis → examine the positioning (novelty vs. prior work), cited-work (do the cited sources support the claims, on the literature registry), and each claim in the paper’s Toulmin ledger.
• thesis defensibility gate → a full mock viva across all three targets.

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Invocation

• Self-invoked. The author requests an examination on a named target/artifact. No guardrail semantics; the loop runs and records an understanding status.
• Automatic guardrail. Fires at the material-decision checkpoints above and when an undemonstrated method’s rationale is used. It does not silently block: it stops, surfaces the gap, and offers to examine/teach (see below).

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Guardrail semantics

1. Stop before the material decision is recorded.
2. Surface the specific gap(s) as observed facts (“no answer to X”), not judgments.
3. Offer to examine/teach now, or to proceed.
4. Record the outcome. A gap can never be passed silently.
5. Override is the human’s and is logged — this is a stop-and-confirm, not a hard block. EU AI Act human-oversight-with-override and the agency principle both require the human be able to proceed. The AI must not decide a gap is “critical.”

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Mentor personas

• Sounding board — high autonomy-support, exploratory; early-stage.
• Critical examiner — the default; calibrated difficulty, critical-thinking and emancipation.
• Directive editor — concrete, process-level feedback; deadline-driven.
• Devil’s advocate (opt-in) — time-boxed, explicit challenge-to-the-argument.

1. Self-selected (default) — the author picks.
2. Task/stage-suggested — a suggested default keyed to the visible artifact/stage (read from the artifact’s stage/frontmatter: early draft → sounding board; near-submission → examiner); always overridable; keyed to the work, never the person.
3. Feedback-calibrated — adjusts to explicit author feedback (“too harsh” / “push harder”).

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Hard constraints

• Ask, don’t grade substance. Report observed facts (“you could not explain X,” “no answer to the falsification probe”), never “you are wrong about X” — a judgment the AI is not entitled to make on a novel claim.
• Never assert a novel claim’s answer key. Contrast with Matt Pocock’s grill-me: same probe-one-question-at-a-time mechanic, inverted epistemics — his elicits requirements the human already holds and lets the AI recommend answers (the human is ground truth); our defend skill verifies understanding of an external truth (claims/methods/literature) and must not supply an answer key for novel claims.
• Teach the established freely, source-grounded. Methodology and cited-work are established knowledge → explain and cite from the digests, the author’s sources, and authoritative references. Novel claims → teach reasoning and defense only.
• Settled vs. contested calibration. Probe hard on settled errors (e.g. treating a non-significant p as “no effect”). On contested choices (e.g. frequentist vs. Bayesian) probe only for a defensible justification and present the standard critique — do not impose a school. Miscalibrating (treating a contested choice as an error) violates agency.
• Calibrate depth to stakes. Hardest on the method carrying the central claim. “Enough” = the author can state what it buys, its key assumption/limitation, and an answer to the one canonical critique — sourced, for a method, from the digests/authoritative refs; for a novel claim, from the most likely reviewer objection (the closest prior work or a rival explanation).
• Anti-Goodhart. Reward articulable understanding, not checklist or ritual completion. The defend skill must not become its own ritual; no scores, no pass/fail, no productivity number.
• Firewall + agency. The defend skill produces evidence the human acts on; it never adjudicates a claim or decides a verdict — the human decides and signs off (../../docs/design/00-meta-spec.md §2.1). Guardrail behavior (stop / offer / logged override): see Guardrail semantics above.

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Commit attribution

Generated-with: honest-scholar (https://github.com/davorrunje/honest-scholar)
HonestScholar-Skill: defend