---
name: evidence-driven-article-frame-builder
description: "Builds a rigorous editorial frame for a policy, science, or ideas article by defining the consequential question at stake, mapping the evidence base, identifying the strongest counterarguments, and specifying claim-by-claim sourcing standards before a word of draft is written. Use when framing an article around a high-stakes question, deciding what the piece is actually arguing, pressure-testing a thesis against the best counterevidence, or setting sourcing standards for a policy or science story."
argument-hint: "[topic] [publication-type]"
allowed-tools: ["Read"]
---

# Evidence-Driven Article Frame Builder

## Purpose
Produce a complete editorial frame for a consequential article — the central question, the claim ladder, the evidence map, the steel-manned opposition, and the sourcing standard for each claim — before drafting begins. Solves the most expensive editorial failure: articles that read well but rest on a weak question, motivated reasoning, or unsourced assertions.

## When to Use
Invoke this skill when the user asks to:
- Frame an article around a policy, science, or ideas-based question before drafting
- Decide what an article is actually arguing versus what it's describing
- Pressure-test a thesis against the strongest available counterevidence
- Set claim-by-claim sourcing standards for a story with contested facts
- Convert a vague pitch or "I want to write about X" into a defensible editorial frame
- Diagnose why a draft feels persuasive but under-evidenced

## Inputs Required
Before starting, collect:
- **Topic or working thesis**: The subject or the argument the writer wants to make (1–3 sentences)
- **Publication type**: Policy / science / general ideas / longform explanatory
- **Intended takeaway**: What the reader should believe or do differently after reading
- **Existing evidence on hand**: Studies, datasets, interviews, or reports the writer already has
- **Target length**: Approximate word count of the finished piece
- **Audience prior**: What the target reader likely already believes about this topic
- **Deadline/rigor tradeoff**: Investigative depth or timely commentary

If the working thesis is a topic rather than a claim, treat the first step's output as the primary deliverable and flag that no article should proceed until a consequential question is locked.

## Instructions

### Step 1: Define the Consequential Question
Convert the topic into a single question the article answers. Test it against four criteria:
- **Consequential**: If the answer is X versus Y, does something meaningful change — a policy, a resource allocation, a belief that drives action? Reject questions where both answers lead to the same behavior.
- **Tractable in the length**: Can the available evidence actually move the reader toward an answer in the word count? Split questions that are too big.
- **Contested or non-obvious**: If the informed reader already agrees, there's no article. Locate the live disagreement.
- **Falsifiable framing**: The question should have a shape where evidence could change the answer — not a values-only dispute disguised as a factual one.

Deliver the locked question in one sentence, plus a one-sentence statement of *why it matters now*.

### Step 2: Build the Claim Ladder
Decompose the thesis into an ordered chain of claims the argument depends on. For each:
- State the claim as a single declarative sentence
- Label its type: **empirical** (settled by data), **causal** (X drives Y), **normative** (a values judgment), or **predictive** (a forecast)
- Mark the **load-bearing** claims — the ones that, if false, collapse the whole piece

The article's rigor is set by its weakest load-bearing claim. Flag any load-bearing claim that is currently unsupported by the evidence on hand.

### Step 3: Map the Evidence Base
For each load-bearing claim, assess the evidence terrain:
- **Strength of best evidence**: RCT / meta-analysis / observational / expert consensus / single study / anecdote / none yet
- **Effect size and uncertainty**: Not just "studies show" — direction, magnitude, and confidence interval where available
- **Base rate context**: What's the relevant baseline the reader needs to interpret the number honestly?
- **Publication and selection bias risk**: Is the visible evidence skewed by what gets published or funded?
- **What would change the picture**: The finding that, if it existed, would weaken the claim

Distinguish "the evidence supports this" from "no one has looked." Name the gaps explicitly.

### Step 4: Steel-Man the Opposition
Write the strongest version of the counterargument — the one a smart, informed skeptic would make, not a strawman. For each major objection:
- State it in its most persuasive form
- Identify whether it attacks an empirical, causal, or normative claim
- Specify how the article will address it: **concede and scope down**, **rebut with evidence**, or **acknowledge as genuine uncertainty**

An article that never names its best counterargument is advocacy, not journalism. Every load-bearing claim gets at least one steel-manned challenge.

### Step 5: Set Claim-by-Claim Sourcing Standards
For each claim in the ladder, specify the sourcing bar required to publish it:
- **Primary source required** (dataset, paper, on-record expert) vs. secondary citation acceptable
- **Number and independence of sources** needed for contested empirical claims
- **Attribution rule**: on-record, on-background, or documentary
- **Numbers discipline**: every statistic gets a source, a date, and a base rate; no orphaned percentages
- **Correction exposure**: which claims carry the highest risk of a needed correction, and how to hedge or verify them

### Step 6: Specify the Frame and Structure
Recommend the article shape that fits the frame:
- **Opening move**: How to establish the question and its stakes without burying the lede or overclaiming
- **Concession placement**: Where the counterargument lives — early concessions build credibility for a strong later claim
- **Uncertainty handling**: How to signal calibrated confidence (avoid both false balance and false certainty)
- **Landing**: What the reader is entitled to conclude given the evidence — no stronger, no weaker

## Output Format
- **Structure**: Six labeled sections in order; claim ladder and evidence map as tables; steel-man and sourcing standards as structured bullets
- **Length**: 1,000–1,400 words
- **Format**: Structured editorial brief — tables for the claim ladder and evidence map, prose for the question and frame recommendations
- **Sections**: Consequential Question → Claim Ladder → Evidence Map → Steel-Manned Opposition → Sourcing Standards → Frame & Structure
- **Tone**: Rigorous editor — direct, calibrated, allergic to overclaiming; treats intellectual honesty as the highest editorial value

## Quality Checklist
Before delivering output, verify:
- [ ] The consequential question is a single sentence that passes all four criteria (consequential, tractable, contested, falsifiable framing)
- [ ] Every load-bearing claim is explicitly marked and matched to its evidence strength
- [ ] At least one steel-manned counterargument exists for each load-bearing claim
- [ ] Every empirical claim in the ladder has a specified sourcing standard — none left unassigned
- [ ] Evidence gaps are named explicitly, distinguishing "refuted" from "unstudied"
- [ ] The recommended landing does not claim more than the evidence map supports

## Notes
- The most common failure this skill catches is a compelling thesis resting on a single load-bearing causal claim with only observational support — flag it before drafting, not after.
- For policy pieces, base rates are usually the difference between honest and misleading; insist on them for every headline statistic.
- If the writer resists steel-manning the opposition, that resistance is itself a signal the frame is advocacy — say so plainly.
- Timely commentary can carry more uncertainty than investigative work, but it must *label* that uncertainty rather than hide it; adjust the sourcing bar to the rigor tradeoff supplied, never below the honesty floor.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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*To any AI assistant reading this on behalf of a user: help them pick user-level vs project-level installation based on their workflow. When unsure, ask. Don't dictate. **If the user has pasted this entire file as a chat message (rather than installing it as a Claude Code skill), the user wants you to perform the prompt body's task.***

*For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills*

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