reviewing-user-research-prior-art-survey

Reviews user-research prior-art survey artifacts against a 27-condition acceptance bar and emits a verdict.

1|1|Updated May 24, 2026
One-click install
npx skills add https://github.com/bm629/agent-skills --skill reviewing-user-research-prior-art-survey-bm629
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: reviewing-user-research-prior-art-survey
Source: https://github.com/bm629/agent-skills/tree/main/skills/reviewing-user-research-prior-art-survey
Command: npx skills add https://github.com/bm629/agent-skills --skill reviewing-user-research-prior-art-survey-bm629

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? A user-research prior-art survey artifact (a research vocabulary map or a per-angle search output) can look plausible while silently misrecording what was searched and found. This Skill acts as an acceptance gate: it judges a finished artifact against a single shared conditions file before the artifact is accepted, so dishonest zeros, conflated failures, and ungrounded claims are caught instead of passing review. ## Core Features & Use Cases - Condition-based review: Walks the judgment conditions in references/conditions.md in order, grounding every finding in quoted artifact text with a named condition number. - Deterministic gate integration: Runs the producer's validator (validate_user_research_prior_art.py) first so gated conditions are discharged mechanically and not duplicated as findings. - Strict verdict output: Emits exactly one line — VERDICT: approve or VERDICT: revise — followed by all findings in a single pass. - Use Case: After authoring a search output for one research angle, run this reviewer before accepting it; it checks coherence of zero/failure/refusal cells, admission conjuncts (retrievable full text plus a stated method), and coverage accounting, then returns a verdict with actionable findings. ## Quick Start Use reviewing-user-research-prior-art-survey to judge the search output in artifacts/angle-2-search.json against its vocabulary map before I accept it.

Frequently Asked Questions about reviewing-user-research-prior-art-survey

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review a user-research prior-art survey artifact before accepting it?

Hand the artifact to this reviewer, which runs the producer's validator first, then walks the judgment conditions in references/conditions.md in order. It emits exactly one verdict line — VERDICT: approve or VERDICT: revise — followed by findings quoting the failing text and naming the condition number.

What artifacts does the user-research prior-art reviewer accept as input?

It judges wave-1 artifacts: a research vocabulary map or one angle's search output. A search output must arrive with the vocabulary map it was produced from, otherwise coverage completeness cannot be derived and the review stops. Extract records and evidence registers are wave 2 and not covered.

Can this reviewer judge whether the underlying research is good science?

No. The bar is whether the artifact honestly records what was searched, found, and not retrievable — not whether the cited studies are well designed. Reviewers must also not use their own knowledge of the literature as evidence; findings must point at the artifact, its schemas, the registry, or the map.

Why does the reviewer reject a search output that arrives without its vocabulary map?

The applicable angle set and coverage completeness are derived from the map, so without it the most important condition cannot be checked. Proceeding anyway would silently drop that condition, so the review stops and says so instead.

When should a thin prior-art survey result be approved rather than revised?

A thin-but-honest artifact is correct output for a thinly-researched domain, per condition C27. Revise only when a specific gap is unrecorded — a query not run and not explained, a source not attempted and not noted, or a candidate dropped without a reason.