Agent skill

Employee Suggestion Hub

by sickn33 in sickn33/agentic-awesome-skills

Suggestion register: submitter or anonymous flag, category, votes, reviewer, decision and response status.

MITAuto-check passedBusiness, Finance & HR

Install Employee Suggestion Hub

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill employee-suggestion-hub -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills employee-suggestion-hub --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/employee-suggestion-hub .claude/skills/employee-suggestion-hub && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
employee-suggestion-hub
GitHub stars
47k
Used in
1 other repo
Token cost
~3.4k tokens
SKILL.md length
1,355 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Suggestion register: submitter or anonymous flag, category, votes, reviewer, decision and response status.

  • Works in 5 steps: Identify intent → Ask only what is missing → Hold the internal context → …
  • Employee feedback programs
  • SKILL.md covers Overview, When to Use This Skill, How It Works and Field Reference, plus 9 more sections
  • Reaches json-schema.org

What it does

Employee Suggestion Hub is an agent skill from sickn33/agentic-awesome-skills. Suggestion register: submitter or anonymous flag, category, votes, reviewer, decision and response status. Use for employee feedback programs.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Performance reviews. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Employee feedback programs
  • Tasks that involve Performance reviews

Example prompts

  • “/employee-suggestion-hub”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Identify intent
  2. Ask only what is missing
  3. Hold the internal context
  4. Recommend the smallest workflow
  5. Build only on request

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, csv, sql, json and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • json-schema.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Employee Suggestion Hub loads about 3.4k tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 1,355 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 1,355 words, ~3,422 tokens.

Download SKILL.mdSave it as .claude/skills/employee-suggestion-hub/SKILL.md (or your agent's skills folder).
name
employee-suggestion-hub
description
Suggestion register: submitter or anonymous flag, category, votes, reviewer, decision and response status. Use for employee feedback programs.
category
business
risk
safe
source
self
source_type
self
date_added
2026-09-26
author
WHOISABHISHEKADHIKARI
tags
sme, business, operations, database, csv, notion, sql, engage
source_repo
WHOISABHISHEKADHIKARI/sme-ops-system-builder

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.

yaml
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 connection. For requested workspace changes, follow the shared contract: verify actual tool access and the target before writing. A user saying "connected" is not tool evidence. Never ask for a Notion password or token.

For an Excel-compatible CSV, use UTF-8 with a byte order mark so Excel opens the text correctly. A CSV is not an .xlsx workbook; create .xlsx only when the user requests a workbook. A CSV carries no types, so after it, name the columns that need a number, date or currency format applied.

csv
Suggestion Title,Submitted By,Anonymous,Department,Category,Description,Date Submitted,Votes,Reviewer,Decision,Response,Status,Suggestion ID
Add a second parking bay,Ananya Rao,FALSE,Delivery,Process,"Anonymous idea from staff, routed to the owner who can actually act on it.",2026-01-15,14,Sneha Iyer,Approved,"Short, specific and actionable feedback only.",Under Review,
sql
CREATE TABLE employee_suggestion_hub (
  suggestion_title VARCHAR(255),
  submitted_by VARCHAR(255),
  anonymous BOOLEAN NOT NULL,
  department VARCHAR(255),
  category VARCHAR(100) NOT NULL,
  description TEXT,
  date_submitted DATE NOT NULL,
  votes NUMERIC NOT NULL,
  reviewer VARCHAR(255),
  decision VARCHAR(255),
  response VARCHAR(255),
  status VARCHAR(100) NOT NULL,
  suggestion_id SERIAL PRIMARY KEY,
  created_at TIMESTAMP DEFAULT NOW(),
  updated_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX idx_employee_suggestion_hub_status ON employee_suggestion_hub (status);
json
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "Employee Suggestion Hub",
  "type": "object",
  "additionalProperties": false,
  "properties": {
      "Suggestion Title": { "type": "string" },
      "Submitted By": { "type": "string" },
      "Anonymous": { "type": "boolean" },
      "Department": { "type": "string" },
      "Category": { "type": "string" },
      "Description": { "type": "string" },
      "Date Submitted": { "type": "string", "format": "date" },
      "Votes": { "type": "number" },
      "Reviewer": { "type": "string" },
      "Decision": { "type": "string" },
      "Response": { "type": "string" },
      "Status": { "type": "string" },
      "Suggestion ID": { "type": "integer" }
  },
  "required": [
      "Category",
      "Date Submitted",
      "Votes",
      "Status"
  ]
}
markdown
| CSV column | Notion property | Set after import |
|---|---|---|
| Suggestion Title | Title | Use as the database title |
| Submitted By | Text | Leave as Text |
| Anonymous | Checkbox | Convert to Checkbox |
| Department | Text | Leave as Text |
| Category | Select (add options after import) | Convert to Select, add options: "Process", "Tooling", "Workload", "Culture", "Facilities" |
| Description | Text | Leave as Text |
| Date Submitted | Date | Convert to Date |
| Votes | Number | Convert to Number |
| Reviewer | Text | Leave as Text |
| Decision | Text | Leave as Text |
| Response | Text | Leave as Text |
| Status | Select (add options after import) | Convert to Select, add options: "Submitted", "Under Review", "Accepted", "In Progress", "Closed", "Declined" |
| Suggestion ID | Text (preserve source ID) | Keep imported IDs as Text; optionally add a separate Unique ID property |

The rows above are documentation examples only. Emit empty templates unless the user explicitly requests examples. Money stays currency, dates stay date, and anything pointing at another table stays relation.

Field Reference

#FieldTypeSQLJSON SchemaNotionCSV example
1Suggestion TitletextVARCHAR(255)stringTextAdd a second parking bay
2Submitted BytextVARCHAR(255)stringTextAnanya Rao
3AnonymouscheckboxBOOLEANbooleanCheckboxFALSE
4DepartmenttextVARCHAR(255)stringTextDelivery
5CategoryselectVARCHAR(100)stringSelect (add options after import)Process
6Descriptionlong_textTEXTstringTextAnonymous idea from staff, routed to the owner who can actually act on it.
7Date SubmitteddateDATEstring, format: dateDate2026-01-15
8VotesnumberNUMERICnumberNumber14
9ReviewertextVARCHAR(255)stringTextSneha Iyer
10DecisiontextVARCHAR(255)stringTextApproved
11ResponsetextVARCHAR(255)stringTextShort, specific and actionable feedback only.
12StatusselectVARCHAR(100)stringSelect (add options after import)Under Review
13Suggestion IDidSERIAL PRIMARY KEYintegerText (preserve source ID)(blank)
Show full SKILL.md (483 more words)Show less

Select Options

Category

Process | Tooling | Workload | Culture | Facilities

Status

Submitted | Under Review | Accepted | In Progress | Closed | Declined

Relations

Link fields: none

Examples

Prompt

People suggest improvements and never hear anything back.

Context first - one question per message, nothing already answered:

Q: Ideas per month? A: Maybe five.

Q: Anonymous? A: Yes.

Q: Who responds? A: Nobody formally.

Recommended next step - offered, not built:

Collect, triage, decide and respond. The response step is the one that determines whether people submit again.

Workflow: Submitted → Triaged → Decided → Responded → Implemented or closed

Want the CSV, SQL, JSON Schema and Notion mapping for this?

Best Practices

  • Build when requested; recommend and offer a build for advice-only requests.
  • One question per message. A batched intake reads as a form and gets guessed at.
  • Keep display names identical across CSV and JSON; document normalized SQL identifiers.
  • Use relation for anything that points at another table, text only for free text.
  • Money fields are currency, never text. Dates are date, never free text.
  • If the user requests an example row, keep it obviously fake so nobody imports it as real data.

Limitations

  • Empty template only. It does not compute payroll, tax, leave balances or KPIs.
  • Notion relations need both databases imported before the link column resolves.
  • Select options are a starting set. Rename them to match how the business talks.
  • No automation, reminders or sync. Those need the integration layer.
  • Does not implement changes or promise any suggestion will be adopted.
  • Legal, tax and HR review is still required before this drives real decisions.

Security & Safety Notes

  • Never fill in real names, salaries, medical or banking data. Placeholders only.
  • Label example rows as synthetic, and keep bank details masked.
  • Local reads, generation commands, and validation are part of a requested artifact build. External writes, messages, provisioning, and publication require authorization for that action and target; existing explicit authorization does not need to be repeated.
  • If sensitive data is supplied, avoid repeating unnecessary identifiers. Use only what the requested review needs; keep generated templates empty. Do not claim deletion from the conversation or service storage.
  • Privacy, legal and disciplinary cases need a qualified human reviewer before anything is acted on.

Common Pitfalls

  • Problem: a static mapping is described as a completed workspace build. Solution: deliver manual mappings without a connection; claim a live change only after the authorized tool operation succeeds.
  • Problem: asked all six questions in one message. Solution: ask one, wait, and drop any the first answer already covered.
  • Problem: built a full system when one table was asked for. Solution: build what was requested; mention the parent skill separately.
  • Problem: all four artifacts drift apart. Solution: derive all four from the field list in this file, never by hand.
  • Problem: Notion import shows every column as Text. Solution: that is expected. Apply the property mapping table once, after import.

Reusable Prompt

I want to set up feedback loop for my company.
Ask me one short question at a time, and only about what I have not already told you.
Then recommend the smallest setup that fits, and wait for me to ask before you build it.
When I ask, output CSV, SQL DDL, JSON Schema, a Notion property mapping or an Excel workbook. Data only.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/employee-suggestion-hub of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit b84d35a

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Employee Suggestion Hub next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Employee Suggestion Hub compared with similar skills
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Employee Suggestion Hub this skillsickn33/agentic-awesome-skills47k1 repos~3.4kAutomated safety check: PassMIT
Wp Performance Reviewelvismdev/claude-wordpress-skills2351 repos~4.5kAutomated safety check: PassMIT
Align Humanagentscope-ai/OpenJudge871—~3.1kAutomated safety check: PassApache-2.0
Run Mv Hoi Reconstructionnvidia-isaac/video_to_data861—~1.5kAutomated safety check: PassCustom licence
Company Analysiszhu1090093659/dsh-trading238—~4.2kAutomated safety check: PassCustom licence
Windbg Diagnostic Methodmicrosoft/win-dev-skills466—~1.9kAutomated safety check: PassMIT

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Questions about Employee Suggestion Hub

What does Employee Suggestion Hub do?

Suggestion register: submitter or anonymous flag, category, votes, reviewer, decision and response status. Employee Suggestion Hub is an agent skill from sickn33/agentic-awesome-skills. Suggestion register: submitter or anonymous flag, category, votes, reviewer, decision and response status.

When should I use Employee Suggestion Hub?

Employee Suggestion Hub fits situations like: employee feedback programs; tasks that involve Performance reviews.

How do I install Employee Suggestion Hub in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill employee-suggestion-hub -a claude-code`. Or copy the skill folder (skills/employee-suggestion-hub in sickn33/agentic-awesome-skills) into .claude/skills/employee-suggestion-hub in your project. Claude Code loads it when a task matches its description.

How do I install Employee Suggestion Hub in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill employee-suggestion-hub -a codex`. Or copy the skill folder (skills/employee-suggestion-hub in sickn33/agentic-awesome-skills) into .agents/skills/employee-suggestion-hub in your project. Codex loads it when a task matches its description.

Can I use Employee Suggestion Hub in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill employee-suggestion-hub -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/employee-suggestion-hub, .gemini/skills/employee-suggestion-hub, .github/skills/employee-suggestion-hub and .opencode/skills/employee-suggestion-hub in your project.

What does Employee Suggestion Hub need to run?

SKILL.md names no scripts, command-line tools or credentials: Employee Suggestion Hub is instructions for the agent only.

Does Employee Suggestion Hub access the network?

SKILL.md names 1 domain. In commands or code: json-schema.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Employee Suggestion Hub safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Employee Suggestion Hub use?

Employee Suggestion Hub is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Employee Suggestion Hub use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Employee Suggestion Hub?

Skills that share tags, products or a category with Employee Suggestion Hub: Wp Performance Review (elvismdev/claude-wordpress-skills, 235 stars), Align Human (agentscope-ai/OpenJudge, 871 stars), Run Mv Hoi Reconstruction (nvidia-isaac/video_to_data, 861 stars) and Company Analysis (zhu1090093659/dsh-trading, 238 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Employee Suggestion Hub?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.