dialectic-loop

name

Dialectic Loop

description

This skill should be used when the user wants to "test a claim against data", "validate an empirical hypothesis", "refine a model/theory with evidence", "characterize a pattern and verify it", "演繹と帰納で検証", "仮説をデータで検証して更新", "実例と反例で確かめる", "弁証法ループ", "主張を現物で裏取り", "傾向分析を検証して精緻化", or mentions iteratively updating a hypothesis by deriving predictions and testing them against a real corpus. NOTE: Use this for VALIDATING/REFINING an empirical claim or model against real data through a derive→test→update loop. Use strong-inference for debugging an UNKNOWN cause, and devils-advocate for adversarially stress-testing a DESIGN proposal.

Dialectic Loop Skill

Validate and refine an empirical claim or model by running C.S. Peirce's inquiry cycle — abduction → deduction → induction — across three roles that debate and update a hypothesis against real data.

Overview

A Dialectic Loop improves the quality of a claim about reality (a trend, a pattern, a characterization, an empirical model) by:

1. Deriving falsifiable predictions from a hypothesis (deductive role)
2. Testing those predictions against a real corpus — surfacing supporting instances and actively hunting counterexamples (inductive role)
3. Arbitrating the gap between prediction and evidence, then updating the hypothesis (arbiter role)
4. Iterating until the hypothesis stabilizes (converges)

The output is not approve/reject and not a root cause — it is a refined hypothesis (H′) with an evidence-backed confidence level and a record of what the original framing got wrong.

Key feature: In codex mode, the inductive role is assigned to Codex so the empirical test is performed by a different model than the one that authored the hypothesis. This independence is the point — it is what catches the hypothesis author's confirmation bias.

Comparison with sibling skills

Aspect	Strong Inference	Devil's Advocate	Dialectic Loop
Purpose	Find an unknown cause	Stress-test a design proposal	Validate/refine an empirical claim or model
Method	Competing hypotheses + eliminating experiments	Blue vs Red adversarial debate	Derive predictions → test on real data → update
Engine of truth	Decisive experiment	Adversarial critique	Counterexample hunting on a real corpus
Output	Root cause + evidence trail	Verdict (APPROVE/CONDITIONAL/REJECT)	Refined hypothesis H′ + confidence + "what was missed"
Loop ends when	One hypothesis survives	Max rounds / verdict	Hypothesis converges (stops changing)
Best for	Debugging	Design review	Trend analysis, profiling, theory refinement, claim-checking

Prerequisites

• The user has a claim, hypothesis, or model to validate (e.g. "X tends to do Y", "this pattern holds", "the system behaves like Z").
• A corpus / data source that can ground the test exists and is accessible (codebase, logs, dataset, documents).
• For Codex collaboration mode: Codex CLI available (codex exec / MCP mcp__codex__codex).

Design principles (lessons baked in)

These are non-negotiable; they are what make the loop work rather than perform theater:

1. Ground the inductive role in current artifacts. The inductive verifier MUST re-read data from disk (ignore cached content), and MUST process large data with scripts/grep rather than eyeballing. A prediction "tested" without touching the corpus is invalid.
2. Name roles by function, not persona. The inductive role's job is measurement + counterexample hunting, not "acting inductive." The deductive role's job is deriving falsifiable consequences, not "being logical."
3. The arbiter is mandatory. Without a third role, a partially-falsified prediction gets quietly recorded as "partial support." The arbiter exists to force the question: where did the hypothesis over- or under-reach?
4. Independence beats agreement. Assign the inductive test to a different model than the hypothesis author. A confirming result from the same author is weak; a counterexample from an independent verifier is strong.
5. Converge, don't exhaust. Stop when H′ stops changing materially, not at a fixed round count.
6. In the --abduce variant, preserve independence structurally (Codex authors and tests). Four guards make this honest: (a) Predictions are Claude's — the believer does not design the test. (b) Arbitration is Claude's, with a mandatory disk recompute — the believer does not grade the result; the arbiter re-measures from disk (full, or sampled with recorded method/tolerance). (c) Thread isolation — induction runs on a fresh thread that receives only the confirmed H, Claude's predictions, and the corpus rule, never the abduction thread's candidates/rationale (no context contamination). (d) In-sample honesty — if H is abduced from the same corpus it is tested on, that is discovery=validation fit; mark evidence_scope: exploratory_in_sample and recommend a holdout/fresh corpus for confirmatory strength.

Workflow Phases

Phase 0: Abduction (Codex) — --abduce variant only

Skipped in the default variant (the user/Claude supplies the hypothesis). When --abduce is set, Codex generates the hypothesis from the corpus, in two sub-stages on a dedicated abduction thread:

1. Candidate generation — Codex re-reads the corpus from disk and proposes multiple competing candidate hypotheses, each with the observations that suggest it, an alternative explanation, and a discriminating measure hint. (A single hypothesis is rejected — it invites premature fixation.)
2. Selection — Claude (or the user, via a prompt) picks one candidate as the confirmed hypothesis H, which feeds Phase 1+.

The abduction thread is kept separate from the later induction thread (see Design principle #6).

Phase 1: Claim Definition

When the user presents a claim:

1. State the hypothesis crisply — one sentence that could be wrong.
2. Identify the grounding corpus — exactly which files / logs / dataset will be the evidence base, and how to access them (paths, extraction rules).
3. Clarify scope — what is in and out of scope; what would count as the claim being false.

Phase 2: Deductive Derivation (Deductive role — Claude)

Take the hypothesis as given and derive 3–5 falsifiable predictions. Each prediction must specify:

• What should be observed in the corpus if the hypothesis is true.
• 支持条件 (support condition) — the measurable result that confirms it.
• 反証条件 (falsifier) — the measurable result that would refute it. A prediction with no falsifier is rejected and rewritten.
• At least one prediction should be a dedicated counter-test (designed to expose the hypothesis if it is wrong).

Record predictions P1…Pn in the state file.

Phase 3: Inductive Verification (Inductive role — Codex by default)

Hand the predictions to the inductive role with a strict mandate:

• Re-read the corpus from disk; ignore any cached content.
• For large data, process with scripts (node/python/ripgrep), not by reading raw.
• For each prediction: gather instances, produce a number (count / rate / measure), and actively search for counterexamples.
• Quote 3–5 short representative instances per prediction (verbatim, trimmed).
• Render a verdict per prediction: 【支持 / 部分支持 / 反証】 with the number and quotes.
• End with 【帰納役の総合所見】: how H should be updated, and — most importantly — 2–3 nuances or counterexamples the deductive role likely missed. Do not flatter the hypothesis.

In codex mode this is delegated via MCP (mcp__codex__codex, sandbox: "read-only") or codex exec. In claude-only mode Claude performs the empirical pass itself, but must still script over the real corpus and hunt counterexamples.

Phase 4: Arbitration (Arbiter role — Claude)

Reconcile predictions against evidence:

1. Build a scorecard: each Pi → predicted vs observed → 支持/部分支持/反証.
2. Explicitly flag every partially-falsified or falsified prediction and name where the deductive framing over- or under-reached.
3. Integrate the inductive role's "missed nuances" — these usually carry the real refinement.
4. Produce H′: the updated hypothesis, rephrased to survive the evidence.
5. Assign a confidence (low/medium/high) with the reason (how robust, how independent the evidence was).

Phase 5: Iterate or Conclude

• If H′ changed materially and max_rounds not reached → loop back to Phase 2 with H′ as the new hypothesis.
• If H′ is stable (converged) → conclude.

Stop conditions:

• Convergence: H′ ≈ H (no material change between rounds).
• max_rounds reached (default: 3).
• User requests stop.

Phase 6: Conclude and Report

Summarize: final hypothesis H_final, the prediction scorecard, the key counterexamples that forced changes, the confidence level, and a short "what the original framing missed" section (the loop's main value).

Role Distribution

Variant / Mode	Hypothesis (abduction)	Predictions (deductive)	Empirical Test (inductive)	Arbitration
default · codex	user / Claude	Claude	Codex (independent, read-only, grounded)	Claude
default · claude-only	user / Claude	Claude	Claude (must still script over real corpus)	Claude
--abduce (codex only)	Codex (from corpus)	Claude	Codex (separate thread)	Claude (+ mandatory disk recompute)

• Default mode: codex (independence is the design goal; fall back automatically if Codex unavailable).
• Optional: --rotate swaps deductive/inductive authorship between rounds to further reduce single-model bias.
• --abduce (abduction variant): Codex generates the hypothesis from the corpus (abduction), Claude derives predictions and arbitrates. This removes the default variant's weakness — that Claude grades its own hypothesis — by making author (Codex) ≠ arbiter (Claude). Requires mode = codex; incompatible with --mode claude-only and --rotate (both error). See Design principles #6 for the independence guards that keep "Codex authors and tests" honest.

State File

Loop state is persisted to tmp/dialectic-loop/<task-id>.md:

Default variant uses dialectic-loop/v1; the --abduce variant uses dialectic-loop/v2, which adds optional fields (a v1 file is read as variant: default with the new fields absent — fully backward compatible).

---
schema: dialectic-loop/v1
task_id: 20260620-090000-12345
created: 2026-06-20T09:00:00Z
claim: "masuP9 holds judgments provisionally but cuts actions decisively"
corpus: "/home/masup9/.claude/projects/**/*.jsonl (user utterances)"
mode: codex
round: 1
max_rounds: 3
status: in_progress
confidence: pending
---

dialectic-loop/v2 (abduction variant) — superset of v1 with the added fields below. Shown at creation time (Phase 0 not yet run); comments note how each field advances:

---
schema: dialectic-loop/v2
variant: codex-abduction          # default | codex-abduction
roles: "abduction=Codex, deduction=Claude, induction=Codex, arbitration=Claude"
phase: abduction                  # abduction → deductive → inductive → arbitration → report
original_claim: ""                # user-supplied claim if any (else empty — Codex abduces)
confirmed_hypothesis: ""          # set in Phase 0b (selection)
abduction_status: pending         # pending → done (set AFTER candidates persisted)
hypothesis_status: pending        # pending → confirmed (after Phase 0b)
abduction_thread_id: ""           # set in Phase 0a (empty/bash-exec in Bash mode)
induction_thread_id: ""           # set in Phase 3 — MUST differ from abduction_thread_id
confidence: pending               # → low | medium | high (Phase 4)
evidence_scope: pending           # → confirmatory | exploratory_in_sample (Phase 4)
claim: ""                         # v2: mirrors confirmed_hypothesis once Phase 0b selects it (empty before); original_claim holds the user seed
# ...plus the remaining v1 fields (task_id, created, corpus, mode, round, max_rounds, status)
---

# ...candidates are persisted under a `## Abduction` / `### Candidates` section in the body.

# Dialectic Loop: <claim>

## Round 1

### Deductive — Predictions
- P1: ... | 支持条件: ... | 反証条件: ...
- P2: ... (counter-test) | ...

### Inductive — Evidence (Codex)
- P1: 【支持】rate=72% — "quote", "quote"
- P2: 【部分支持】27.7% — "quote"
- 総合所見: ...（演繹役が見落とした反例: ...）

### Arbitration
- Scorecard: P1 支持 / P2 部分支持（演繹役が過大評価）/ ...
- H′: ...
- Confidence: medium — independent extraction, N=423

## Round 2
...

Safety Guards

• Inductive verification runs read-only (sandbox: "read-only"); it observes, it does not mutate.
• The inductive role must re-read from disk and script over large corpora (no raw-dump reading, no cached content).
• Confirm before any file modification (this skill is analytical; writes are limited to the state file and the final report).
• Set per-delegation timeout: min(wait_timeout + 60, 600) * 1000 ms for codex exec.
• Abduction variant only: the arbiter's disk recompute is mandatory (not optional); the induction thread must be distinct from the abduction thread; and on Codex failure, never silently fall back to claude-only (would break author≠verifier — see command Error Handling).

Output Format

Progress Display

Dialectic Loop — Round 2 / 3
Claim: masuP9 holds judgments provisionally but cuts actions decisively

Predictions:
  [✓] P1 hedged judgments dominant      → 支持   (72%)
  [~] P2 judgment coupled to verify     → 部分支持 (28%)  ← deductive over-reach
  [✓] P3 no principle-only commands     → 支持   (0 found)
  [✓] P4 decision deferral present      → 支持   (8)

H′: provisional in belief, decisive in action; verifies selectively, not reflexively
Confidence: medium  |  Convergence: pending (H changed this round)

Completion Report

Dialectic Loop Complete
=======================
Claim (H):     <original>
Refined (H′):  <updated hypothesis that survived the evidence>
Confidence:    medium (independent verifier, N=423, 1 prediction partially falsified)

Scorecard:
  P1 支持 (72%) · P2 部分支持 (28%) · P3 支持 (0) · P4 支持 (8)

What the original framing missed:
  1. ...
  2. ...

Loop log: tmp/dialectic-loop/<task-id>.md

Invoking the Skill

Use the /dialectic-loop command:

# Basic — validate a claim against a corpus
/dialectic-loop "User X holds judgments provisionally but cuts actions decisively" --corpus "~/.claude/projects/**/*.jsonl"

# Mode + rounds
/dialectic-loop --mode claude-only --max-rounds 2 "This API degrades under concurrency"

# Rotate authorship to reduce bias
/dialectic-loop --rotate "Codebase favors composition over inheritance"

# Abduction variant — let Codex generate the hypothesis from the corpus (claim optional)
/dialectic-loop --abduce --corpus "scripts/**/*.sh"

# Japanese
/dialectic-loop 「この傾向分析の仮説をログで検証して精緻化して」

Compact Recovery

If compacted mid-loop:

1. Run TaskList to see progress.
2. Read the state file: tmp/dialectic-loop/<task-id>.md.
3. Resume from the current phase based on round and which sections are filled.

State	Resume at
variant: codex-abduction, abduction_status ≠ done	Phase 0a (regenerate candidates, new abduction thread)
candidates present, hypothesis_status ≠ confirmed	Phase 0b (selection)
H confirmed, no predictions	Phase 2 (Deductive)
predictions present, no evidence	Phase 3 (Inductive)
evidence present, no arbitration	Phase 4 (Arbitration)
arbitration present, H′ changed, rounds remain	Phase 2 (next round)
H′ converged or max_rounds reached	Phase 6 (Report)

Thread recovery (abduction variant): if abduction_thread_id is lost but candidates are recorded, do not regenerate it; if induction_thread_id is lost mid-loop, start a fresh induction thread from the current round's H + predictions + corpus rule and recompute that round (never reuse the abduction thread).

References

Detailed templates in references/:

• abduction-template.md — the prompt handed to Codex in the --abduce variant's Phase 0 to generate competing candidate hypotheses from the corpus.
• prediction-template.md — structure for the deductive role's falsifiable predictions.
• induction-verification-template.md — the prompt handed to the inductive role (Codex), including the counterexample-hunting mandate.