Construct Arguments

Build rigorous arguments hypothesis → reasoning → concrete evidence. Triad: hypothesis = what you believe, argument = why holds, examples = that holds. Apply to code reviews, design decisions, research writing, any ctx w/ claims needing justification.

Use When

• Writing/reviewing PR desc proposing technical change
• Justifying design decision in ADR
• Code review feedback beyond "I don't like this"
• Research argument or technical proposal
• Challenge/defend approach in technical discussion

In

• Required: Claim or position needing justification
• Required: Context (code review, design decision, research, doc)
• Optional: Audience (peer devs, reviewers, stakeholders, researchers)
• Optional: Counterarguments or alternative positions
• Optional: Evidence or data available

Do

Step 1: Formulate Hypothesis

Clear, falsifiable claim. Not opinion or preference — specific assertion testable vs evidence.

1. Claim in 1 sentence
2. Falsifiability test: can someone prove wrong w/ evidence?
3. Scope narrowly: specific ctx, codebase, domain
4. Distinguish from opinions → testable criteria

Falsifiable vs unfalsifiable:

→ 1-sentence hypothesis specific + scoped + falsifiable. Reader immediately imagines evidence confirming/refuting.

If err: Vague → "how would I disprove?" test. Can't imagine counter-evidence → opinion not hypothesis. Narrow scope or add measurable criteria.

Step 2: ID Argument Type

Logical structure for hypothesis. Diff claims need diff reasoning strategies.

1. Review 4 types:

2. Match hypothesis → strongest type:

   • must be true → deductive
   • tends to be true via observations → inductive
   • will likely work via similar prior cases → analogical
   • one explanation fits data better → evidential

3. Combine types for stronger arguments (analogical + inductive evidence)

→ Chosen type (or combo) + clear rationale why fits hypothesis.

If err: No single type fits cleanly → split hypothesis into sub-claims. Each w/ natural argument structure.

Step 3: Construct Argument

Logical chain connecting hypothesis → justification.

1. State premises (facts/assumptions starting from)
2. Show logical connection (premises → conclusion)
3. Steelman strongest counterargument — state best opposing before refuting
4. Address counterargument directly w/ evidence or reasoning

Worked example — Code Review (deductive + inductive):

Hypothesis: "Extracting validation logic into shared module will reduce bug duplication across 3 API handlers."

Premises:

• 3 handlers (

  createUser

  ,

  updateUser

  ,

  deleteUser

  ) impl same input valid. w/ slight variations (observed

  src/handlers/

  )
• Last 6 months, 3/5 valid. bugs fixed in 1 handler not propagated (issues #42, #57, #61)
• Shared modules enforce single src of truth (deductive: if one impl, then one place to fix)

Logical chain: Because 3 handlers duplicate same valid. (premise 1), bugs fixed in 1 missed in others (premise 2, inductive from 3/5). Shared module → fixes apply once to all callers (deductive from shared-module semantics). Therefore extraction reduces bug duplication.

Counterargument (steelmanned): "Shared modules introduce coupling — change to valid. for 1 handler could break others."

Rebuttal: Handlers already share identical valid. intent; coupling implicit + harder to maintain. Making explicit via shared module w/ parameterized options (

validate(input, { requireEmail: true })

) makes coupling visible + testable. Current implicit duplication riskier — hides dependency.

Worked example — Research (evidential):

Hypothesis: "Pre-training on domain-specific corpora improves downstream task perf more than increasing general corpus size for biomedical NER."

Premises:

• BioBERT pre-trained on PubMed (4.5B words) outperforms BERT-Large on general English (16B words) on 6/6 biomedical NER benchmarks (Lee et al., 2020)
• SciBERT pre-trained on Semantic Scholar (3.1B words) outperforms BERT-Base on SciERC + JNLPBA despite smaller pre-training corpus
• General-domain scaling (BERT-Base → BERT-Large, 3x params) smaller gains on biomedical NER than domain adaptation (BERT-Base → BioBERT, same params)

Logical chain: Evidence consistently shows domain corpus selection outweighs scale for biomedical NER (evidential: these results more likely if domain specificity > scale). 3 independent comparisons same direction → strengthens inductive case.

Counterargument (steelmanned): "Results may not generalize beyond biomedical NER — biomedicine has unusually specialized vocab inflating domain-adaptation advantage."

Rebuttal: Valid limitation. Hypothesis scoped biomedical NER specifically. Similar gains appear legal NLP (Legal-BERT) + financial NLP (FinBERT) → pattern may generalize to other specialized domains, separate claim needing its own evidence.

→ Complete argument chain + premises + logical connection + steelmanned counterargument + rebuttal. Reader follows step by step.

If err: Argument weak → check premises. Weak args stem from unsupported premises not faulty logic. Find evidence per premise or acknowledge as assumption. Counterargument stronger than rebuttal → hypothesis needs revision.

Step 4: Concrete Examples

Support w/ independently verifiable evidence. Not illustrations — empirical foundation making argument testable.

1. ≥1 positive example confirming hypothesis
2. ≥1 edge case/boundary example testing limits
3. Each independently verifiable: another person can reproduce or check no relying on your interpretation
4. Code claims → specific files, line nums, commits
5. Research claims → specific papers, datasets, experimental results

Example selection criteria:

createUser

updateUser

→ Concrete examples reader can verify independently. ≥1 positive + 1 edge case. Each refs specific artifact (file, line, issue, paper, dataset).

If err: Examples hard to find → hypothesis too broad or not grounded observable reality. Narrow scope → what you can actually point to. Absence = signal, not gap to paper over.

Step 5: Assemble Complete Argument

Combine hypothesis + argument + examples → appropriate format for ctx.

1. Code reviews — structure:

   [S] <one-line summary of the suggestion>
   
   **Hypothesis**: <what you believe should change and why>
   
   **Argument**: <the logical case, with premises>
   
   **Evidence**: <specific files, lines, issues, or metrics>
   
   **Suggestion**: <concrete code change or approach>
   

2. PR descriptions — body:

   ## Why
   
   <Hypothesis: what problem this solves and the specific improvement claim>
   
   ## Approach
   
   <Argument: why this approach was chosen over alternatives>
   
   ## Evidence
   
   <Examples: benchmarks, bug references, before/after comparisons>
   

3. ADRs (Architecture Decision Records) — std ADR format + triad → Context (hypothesis), Decision (argument), Consequences (examples/evidence of expected outcomes)

4. Research writing — std structure: Intro = hypothesis, Methods/Results = argument + examples, Discussion = counterarguments

5. Review assembled for:

   • Logical gaps (conclusion follow from premises?)
   • Missing evidence (unsupported premises?)
   • Unaddressed counterarguments (strongest objection answered?)
   • Scope creep (stays within hypothesis bounds?)

→ Complete formatted argument appropriate for ctx. Reader can eval hypothesis, follow reasoning, check evidence, consider counterarguments — all in 1 coherent structure.

If err: Assembled feels disjointed → hypothesis too broad. Split into focused sub-arguments, each w/ own triad. 2 tight > 1 sprawling.

Check

• Hypothesis falsifiable (could disprove w/ evidence)
• Hypothesis scoped specific ctx, not universal
• Argument type ID'd + appropriate for claim
• Premises stated explicitly, not assumed shared knowledge
• Logical chain connects premises → conclusion no gaps
• Strongest counterargument steelmanned + addressed
• ≥1 positive example supports
• ≥1 edge case or limitation acknowledged
• All examples independently verifiable (refs provided)
• Out format matches ctx (code review, PR, ADR, research)
• No logical fallacies (appeal to authority, false dichotomy, strawman)

Traps

• State opinions as hypotheses: "This code is messy" = preference not hypothesis. Rewrite testable: "This module has 4 responsibilities that should be separated per single-responsibility principle, as evidenced by 6 public methods spanning 3 unrelated domains."
• Skip counterargument: Unaddressed objections weaken even if reader never voices. Always steelman — state strongest opposing in best form before rebutting.
• Vague examples: "We've seen this pattern" not evidence. Point to specific issues, commits, lines, papers, datasets. Can't find concrete → hypothesis not well-grounded.
• Argument from authority: "Senior engineer said so" or "Google does it this way" not logical argument. Authority motivates investigation; argument must stand on evidence + reasoning.
• Scope creep in conclusions: Drawing broader than evidence supports. Examples cover 3 API handlers → don't conclude about entire codebase. Match conclusion scope → evidence scope.
• Conflate argument types: Inductive lang ("tends to") for deductive ("must be") or vice versa. Precise about strength — deductive = certainty, inductive = probability.

→

• review-pull-request

  — apply argumentation → structured code review feedback

• review-research

  — evidence-based arguments in research

• review-software-architecture

  — justify architectural decisions via triad

• create-skill

  — skills = structured arguments for how to accomplish task

• write-claude-md

  — doc conventions + decisions benefiting from clear justification

Composition: Argumentation + Advocatus Diaboli

High-stakes decisions → compose w/

advocatus-diaboli

1. Structure via argumentation — build triad
2. Stress-test via advocatus-diaboli — steelman proposal, challenge each assumption w/ specific questions. Severity: Critical (redesign/abandon), Medium (adjust), Low (note + proceed)
3. Revise per findings — critical → redesign; medium → adjustment; low → noted

When compose vs alone:

• Argumentation alone → constructing proposal, PR desc, design justification
• Advocatus-diaboli alone → reviewing someone else's existing argument
• Compose both → you're both proposer + need adversarial self-review pre-committing

Example — PR response refinement: Argumentation structured response (hypothesis: combining PRs better, argument w/ evidence, collaboration offer). Advocatus-diaboli caught 2 critical issues: claim about proxy proc ID speculative not factual (would've been embarrassing on security PR), "I have tested this in practice" unverifiable. Both removed. Final 40-50% shorter — overexplaining signals insecurity.

Example — System design triage: Argumentation (via Plan agent) designed full 500-line triage pipeline. Advocatus-diaboli killed: at 9 items, premature + system itself would become maintenance burden (recursive trap). Final: 25 lines added to existing script.