impact-check

name	impact-check
description	Assess whether the research problem/behavior is important — its potential value and reach. Use when user says "impact check", "check impact", or wants to judge whether an idea/behavior matters before committing.
argument-hint	["idea-or-behavior-description"]
allowed-tools	WebSearch, WebFetch, Grep, Read, Glob, mcp__llm-chat__chat

Impact Check Skill

Check whether a proposed idea studies an important problem/behavior — one worth committing effort to: $ARGUMENTS

What Counts as Impact

An idea is high-impact to the extent it does ANY of the following:

• solves an important problem;
• is likely to be widely used or cited by follow-up research;
• could change the direction of a research area;
• helps real applications, industry, society, or cross-disciplinary research;
• reveals an important phenomenon, even if the method is simple.

Constants

• REVIEWER_BACKEND = llm-chat — External LLM reviewer via llm-chat MCP (model defers to LLM_MODEL env). Always ask the external reviewer for strict, high-rigor feedback.

Instructions

Given an idea / behavior description, systematically assess its importance:

Phase A: Extract the Impact-Bearing Claims

1. Read the description and name the behavior/phenomenon or problem under study (not the method).
2. Extract the elements that determine its importance:
   • What problem does it address, and who actually has that problem?
   • What downstream research or applications would build on it if the result holds?
   • What would change — in the field or in practice — if it is true?
   • What is the reach: one narrow setting, a whole research area, or real-world / cross-disciplinary use?
3. State the single strongest one-line case for why it matters.

Phase B: Assess Impact Along the Dimensions

For EACH impact dimension below, judge how strongly the idea scores and gather supporting evidence (use WebSearch / WebFetch to check whether the problem is an open/active question, how much attention it gets, and where it would be used):

1. Important problem — Is this a problem the field (or a real application) actually needs solved, or a niche curiosity?
2. Uptake / citation — Would follow-up research likely build on, use, or cite this? Does it produce a reusable artifact (dataset, method, finding, diagnostic)?
3. Direction-shifting — Could the result change how people think about or approach a research area?
4. Real-world reach — Does it help applications, industry, society, or cross-disciplinary work?
5. Phenomenon value — Even if the method is simple, does the result reveal an important phenomenon?

Phase C: Cross-Model Verification

Call the external LLM reviewer via mcp__llm-chat__chat. Always ask for strict, high-rigor feedback.

Prompt should include:

• The idea / behavior description
• Your per-dimension assessment from Phase B
• Ask: "Is this problem important? Who would care, and how far would the result reach? What is the strongest 'so what?' objection?"

Phase D: Impact Report

Output a structured report:

## Impact Check Report

### Studied Problem/Behavior
[1-2 sentence description — the behavior/problem, not the method]

### Impact Dimensions
1. Important problem — HIGH/MEDIUM/LOW — [why]
2. Uptake / citation — HIGH/MEDIUM/LOW — [why]
3. Direction-shifting — HIGH/MEDIUM/LOW — [why]
4. Real-world reach — HIGH/MEDIUM/LOW — [why]
5. Phenomenon value — HIGH/MEDIUM/LOW — [why]

### Overall Impact Assessment
- Score: X/10
- Recommendation: PROCEED / PROCEED WITH CAUTION / DEPRIORITIZE
- Why it matters: [the strongest case, one sentence]
- "So what?" risk: [the strongest objection a reviewer would raise about importance]
- Who would care: [the audience that benefits most]

Important Rules

• Be BRUTALLY honest — committing to an unimportant problem wastes months of research time.
• "Interesting" is not "important". Apply the "so what?" test: if the result came out either way, would anyone change what they do?
• A simple method can be high-impact if it reveals an important phenomenon — do not penalize simplicity.

Review Tracing

After each mcp__llm-chat__chat reviewer call, save the trace following shared-references/review-tracing.md. Write files directly to .mechanist/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).