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credible-claims — Claude Code skill: brief first, claim record after

A skill for delegated and AI-assisted research: write the brief before the run, the claim record after. Keeps fast execution from passing as credible evidence.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add pedrohcgs/claude-code-my-workflow --skill credible-claims -a claude-code
Open on GitHub

What credible-claims does

The skill adds two artifacts. A research brief goes down before the work starts and pins the question, completion criteria, prohibited substitutions, known failure modes, required evidence, and escalation triggers. The claim record follows during and after: for each claim it lists the supporting data with locations, anything changed once results were visible, checks still open, and who decided what could be reported.

Three standing rules carry the discipline. Delegate only after inputs, boundaries, and completion criteria are clear. Demand inspectable evidence rather than confident conclusions — locations, diffs, diagnostics, counts, failing cases, logs. Send back to the user anything that touches the economic object, the identifying assumptions, the inferential procedure, or the reporting language. A route that depends on unavailable data or an unproved result gets marked blocked, which is a scientific outcome rather than a prompt to keep searching.

The final call is not whether every check passed. It is whether the evidence supports the wording you plan to use: repair, narrower language, more review, an exploratory label, or no report. The skill works with Read, Grep, Glob, and Write, so it reads and writes files but runs no code. Proportionality matters — a routine task needs one short paragraph, a heavy record only when branching is wide, outputs get reused, or a correction would be costly.

What it helps with

  • write a brief before a long autonomous run
  • pin down which data backs each claim
  • separate a diagnostic fix from a favorable switch
  • mark a route blocked instead of hunting for a convenient answer
  • stop report wording from outrunning the evidence

Example prompts for the agent

  • Write the research brief before launching this task
  • Build the claim record from this run
  • Check whether the reporting language goes beyond the evidence

How to install credible-claims

1Skills CLI (Vercel)

npx skills add pedrohcgs/claude-code-my-workflow --skill credible-claims -a claude-code

2Manually: put the files in place

~/.claude/skills/credible-claims/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/credible-claims/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about credible-claims

How do I install credible-claims in Claude?

Run npx skills add pedrohcgs/claude-code-my-workflow --skill credible-claims -a claude-code. The skill lands at ~/.claude/skills/credible-claims/SKILL.md.

What does credible-claims do?

It adds a research brief written before execution and a claim record produced when reporting results. The brief constrains what the system may do; the record constrains what may be reported.

Which tools can the skill use?

Read, Grep, Glob, and Write. It reads and writes files, and does not run code or perform the analysis itself.

Source text

SKILL.md · MIT
---
name: credible-claims
description: 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.
allowed-tools: ["Read", "Grep", "Glob", "Write"]
metadata:
  protocol: bounded-delegation
---


# 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

- [`verification-ladder.md`](../../references/verification-ladder.md) — rung 5 (the ledger)
- [`.claude/rules/orchestrator-protocol.md`](../../rules/orchestrator-protocol.md) — RUN_CONFIG is the brief for a fan-out

© pedrohcgs · MIT · pedrohcgs/claude-code-my-workflow

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