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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.

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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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Turn an incoming set of findings — from an AI reviewer, a referee report, a code review, a linter, or a second model — into verified fixes, without letting a confident misread damage correct work. Every finding is a CANDIDATE until checked against the actual source. Use whenever you receive review comments, audit findings, or a critique you did not write yourself, especially when the reviewer is a model or when the volume is too large to check by feel.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

Before and after changing anything shared — a function's return value, a signature, a schema, a label set, a config default, a constant, a file format — find every consumer and actually run them. Catches the change that looks purely additive but silently breaks a contract in a file you never opened. Use when editing shared code, adding a field/column/return element, renaming, changing units or defaults, or touching a pipeline that produces reported numbers.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

Snapshot the computational environment for a replication package — detects the analysis stack (R / Stata / Python) and emits the right lockfiles (renv.lock + sessionInfo.txt, requirements.txt / environment.yml / uv.lock, Stata version + ado package list), records seeds and RNG kind, optionally writes a pinning Dockerfile, and produces a paste-ready "Computational requirements" block. Use when user says "capture the environment", "snapshot my dependencies", "pin the versions", "make a renv.lock / requirements.txt", "make this byte-reproducible", or before releasing a replication package to openICPSR / the AEA Data Editor.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

challenge

無料

Stress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

Save a structured state snapshot before stopping or handing off. Captures the active plan, recent decisions, file pointers (with line numbers), open questions, and the next 1–3 actions into a checkpoint file under `quality_reports/checkpoints/`. Optionally proposes `[LEARN]` entries to add to MEMORY.md. Use when user says "checkpoint", "save state", "snapshot before I stop", "where am I", "wrap up the session for handoff", or before a long break / model switch / collaborator handoff. Companion to (NOT replacement for) the narrative session-log workflow.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

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