pedrohcgs

credible-claims

Research-brief + claim-record discipline for delegated or AI-assisted research work. Use when starting any substantive research task or long autonomous run (write the brief first), and when reporting results that will support a claim in a paper or decision (produce the claim record). Keeps faster execution from being confused with credible evidence.

pedrohcgs 1,544 2,983 Updated 2w ago
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npx skillscat add pedrohcgs/claude-code-my-workflow/credible-claims

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SKILL.md

Credible claims: the brief before, the record after

Cheaper generation increases demand for scarce validation. The fix is two lightweight artifacts: a research brief that constrains what the system may do, and a claim record that constrains what may be reported. Applies to agents, workflows, sims, proof patches, data construction — anything delegated.

Three standing rules

  1. Delegate only after inputs, boundaries, and completion criteria are clear. No open-ended "make it better" runs.
  2. Require evidence, not confident conclusions. Every delegated task returns inspectable evidence: locations, diffs, diagnostics, counts, failing cases, logs — never just "done/looks fine."
  3. Escalate anything that changes the economic object, the identifying assumptions, the inferential procedure, or the reporting language. Those decisions return to the researcher (the user), always. A standing user ruling counts as a returned decision; record it.

The research brief (before execution)

One short block, written before launching the work:

  • Question / target: what exactly is being estimated, proved, built.
  • Completion: what counts as done; what results would not answer the question.
  • Prohibited substitutions: what the system may not silently change (estimand, sample, assumptions, statement of a theorem, benchmark spec).
  • Known failure modes: what tends to go wrong here; the checks matched to each.
  • Required evidence: what must come back (numbers, locations, diffs, diagnostics).
  • Escalation triggers: which findings/decisions must return to the user before proceeding.
  • Blocked-route rule: a route that depends on unavailable data, an unsupported assumption, or an unproved result is marked blocked — a scientific outcome, not an instruction to search until a favorable answer appears.

The claim record (during/after execution)

For each claim the work will support:

  • Support: which data/analysis/proof supports it (with locations).
  • Changes after seeing results: anything modified after outcomes were visible, and why (diagnostic-triggered fix vs favorable switch — keep these distinguishable).
  • Unresolved: checks that remain open, and how they constrain the language.
  • Decision: who decided what could be reported (user ruling vs assistant default).

Proportionality: a routine task needs one short paragraph; heavier records only when branching is extensive, outputs will be reused, errors are consequential, or correction is costly.

Reporting language

The final decision is never "all checks green" — it is whether the evidence supports the proposed language. The options are: repair; narrower language; additional review; an exploratory/descriptive label; or decline to report. Never upgrade language beyond the evidence (associational ≠ causal; pointwise ≠ uniform; illustrated ≠ validated; imposed ≠ derived).

Keep credibility questions separate

Reproducibility, implementation correctness, statistical performance, measurement validity, and identification/scope are different questions; evidence on one cannot answer another. Reproducible code may implement the wrong estimator; favorable simulations cannot establish an assumption; a correct estimate may answer the wrong question.

Learn forward

Every diagnosed failure becomes a durable artifact: a reusable test, a documented warning, or a memory entry stating the failure, why it happened, and the check that now prevents it. Preserve failed approaches and the reason they failed — a blocked route re-attempted without a new mechanism is waste.

Cross-references