SKILLEMALL.ai
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AC continuous-user-research

Run longitudinal, in-context diary studies for product teams and convert weekly participant entries into concise User Signals + Recommendations with evidence, confidence, and experiment-ready actions. Use for onboarding drop-off diagnosis, feature habit/frequency understanding, motivation and emotion analysis, multi-touchpoint journey mapping, and channel/device behavior comparisons. Supports event-based, interval-based, signal-based, and mixed timing designs with pilot validation, compliance monitoring, and privacy-redacted reporting.

modbender/skill-library-mcp Agent Skills author: modbender MIT 12 files body ≈ 8 818 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Run longitudinal, in-context diary studies for product teams and convert weekly participant entries into concise User Signals + Recommendations with evidence…

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorNotionAirtableSlackData and analyticsPersonal productivitySecuritytype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
52/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8818 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 52/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 25 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 40Execution cost. Instruction body is 8818 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 223 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 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 541: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 223 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.