employee-suggestion-hub
Lets your agent record employee suggestions. It organizes ideas, votes, and decisions to improve the work environment.
What it does
This system helps collect and manage employee suggestions. It allows people to submit ideas, which can be anonymous or identified, and tracks the review and decision process.
When to use it
- ✓When you want to set up an employee suggestion box.
- ✓To build a portal for staff feedback.
- ✓To create a system for tracking ideas submitted by employees.
Example of what to ask your AI
“I want to set up a system for collecting employee suggestions.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI asks for details about how suggestions will be submitted and processed. Then, it suggests a simple workflow and can generate files like spreadsheets or databases to organize this information.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill employee-suggestion-hubOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/employee-suggestion-hub/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Employee Suggestion Hub
What it is: Feedback loop.
Overview
Works out the smallest useful Employee Suggestion Hub setup for the business in front of it, then builds it only when asked. The default output is a short recommendation, not a spreadsheet. Artifacts - CSV, SQL DDL, JSON Schema, Notion mapping - are produced on request, from one field list so they cannot drift apart.
Layer: Layer 6: Engage. Fits: Scale stage. Table code: n/a.
When to Use This Skill
- suggestion box
- employee feedback portal
- idea submission tracker
- staff suggestions
Also use it when the user says "feedback loop", or describes the same process happening in a spreadsheet, a document or someone inboxes.
Do not use it for: payroll calculation, tax filing, or legal advice. This skill produces empty templates only - it never holds or processes real employee or customer data.
How It Works
Follow the shared execution contract. The module-specific rules below define only domain fields, decisions, calculations, and safety constraints.
Step 1 - Identify intent
Read the request and pick the intent before asking anything.
- "set up" or "build" or "create" -> the user wants artifacts; go to Step 2.
- "our process is ..." or "it is in a sheet" -> the user wants to move an existing process; capture it, then Step 2.
- "is this right" or "review" or "audit" -> the user wants a check, not a build; answer from what they share.
- "how do I ..." -> advice question; answer directly and offer the build only if it helps.
Ask only if this is the highest-value missing fact; otherwise proceed without an opener:
Q: How many suggestions do you get in a month?
Step 2 - Ask only what is missing
Skip anything the user already answered, in any earlier message. Ask the rest one at a time, and stop as soon as the remaining answers would not change the output.
- Submissions - How many people? / How many ideas a month? / Anonymous allowed?
- Process - Who triages? / Who responds? / SLA days?
- Follow-up - Are decisions shared? / Implemented ideas credited? / Closed or open?
- Current process - How do people share ideas now? / Form, chat or meeting? / What happens to them?
- Outcome - What do you need? / A submission form, a review queue or reporting?
Never invent an answer. If the user does not know, record it as unknown and carry on.
Step 3 - Hold the internal context
Hold the answers in this shape. It stays internal - it is not shown to the user unless they ask, and it never carries a value the user did not give.
module: employee-suggestion-hub
intent: null # setup | advice | review | fix | build | convert | export
scale: null # Starter | Growth | Scale, only if the answer changes it
areas:
"Submissions": null
"Process": null
"Follow-up": null
"Current process": null
"Outcome": null
requested_outputs: [] # csv | sql | json | notion | xlsx - requested formats only
confirmed_facts: [] # only what the user actually said
open_questions: [] # the unanswered ones, in the order worth asking
Step 4 - Recommend the smallest workflow
If an artifact was requested, build it after resolving essential missing facts. Otherwise give a short recommendation and offer the relevant artifact.
Recommended approach: Collect, triage, decide and respond. The response step is the one that determines whether people submit again.
Why this one: Suggestion schemes die from silence, not from a lack of ideas. Recording the response is what keeps submissions coming.
Workflow: Submitted → Triaged → Decided → Responded → Implemented or closed
Step 5 - Build only on request
Once the user asks for it, derive the fields from the confirmed context and emit the requested artifacts. For machine-readable text, keep prose outside the data; for files, provide a usable link. Report material validation failures or limitations separately.
A selected Notion output is rendered by notion-manual-import, so route the
Notion step there. When the user selects Notion, hand that step to
@notion-manual-import: it holds the CSV, the property
mapping, the import steps and the verification checklist, and it renders the Field
Reference below instead of defining a table of its own. Do not restate the mapping
here and do not improvise the import steps. Manual CSV and mapping outputs need no
…
FAQ
Can I use this to collect customer feedback?
No, this system is specifically for employee suggestions.
Will the AI store the suggestion data?
No, the AI only creates templates (like spreadsheets or database code) for you to use. It does not store or process actual employee data.
What if I don't know the answer to a question?
The AI will record it as 'unknown' and continue. It doesn't invent answers and focuses on creating the structure you need.
Details
Original description by the author
Suggestion register: submitter or anonymous flag, category, votes, reviewer, decision and response status. Use for employee feedback programs.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2918
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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