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AF user-research-synthesis

Analyze and synthesize user research findings into structured, actionable insights. Use when given user research data, interview transcripts, survey results, or user feedback that needs to be analyzed and summarised. Produces a themed synthesis with prevalence data, supporting quotes, pain points analysis, feature request prioritisation, and recommended next steps. For interview transcripts specifically use user-interview-synthesis instead.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 4 files body ≈ 2 462 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Analyze and synthesize user research findings into structured, actionable insights.

As a process F 41/100 · Will not run — References files that are not bundled: ../professional-brain/SKILL.md

AnalyzerWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
F
41/100
Will not run
References files that are not bundled: ../professional-brain/SKILL.md
Tools and files w 18
0
Result and completion w 14
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: user-research-synthesis (mohitagw15856/pm-claude-skills)

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../professional-brain/SKILL.md

Process rating: all ten parameters 41/100

Will not run. References files that are not bundled: ../professional-brain/SKILL.md
  • 0Tools and files. 1 referenced file(s) missing: ../professional-brain/SKILL.md
  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 70 steps, 2 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2462 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 444: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 70 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (1 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.