Agent skill

Dei Dashboard

by sickn33 in sickn33/agentic-awesome-skills

Diversity, equity and inclusion dashboard: metric by department and period, value against target, group size and minimum-threshold flag.

MITAuto-check passed

Install Dei Dashboard

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill dei-dashboard -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills dei-dashboard --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/dei-dashboard .claude/skills/dei-dashboard && 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
dei-dashboard
GitHub stars
47k
Used in
1 other repo
Token cost
~3.2k tokens
SKILL.md length
1,334 words
Files
1
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Diversity, equity and inclusion dashboard: metric by department and period, value against target, group size and minimum-threshold flag.

  • Works in 5 steps: Identify intent → Ask only what is missing → Hold the internal context → …
  • 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

Dei Dashboard is an agent skill from sickn33/agentic-awesome-skills. Diversity, equity and inclusion dashboard: metric by department and period, value against target, group size and minimum-threshold flag. Use for DEI reporting.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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.

Example prompts

  • “/dei-dashboard”

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 680176d. 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

Dei Dashboard loads about 3.2k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 1,334 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

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 680176d, republished under its MIT licence (© sickn33). 1,334 words, ~3,205 tokens.

Download SKILL.mdSave it as .claude/skills/dei-dashboard/SKILL.md (or your agent's skills folder).
name
dei-dashboard
description
Diversity, equity and inclusion dashboard: metric by department and period, value against target, group size and minimum-threshold flag. Use for DEI reporting.
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

DEI Dashboard

What it is: Diversity & inclusion.

Overview

Works out the smallest useful DEI Dashboard 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

  • dei dashboard
  • diversity reporting
  • workforce diversity metrics
  • inclusion tracker

Also use it when the user says "diversity & inclusion", 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: Which stages of hiring do you want to look at?

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.

  • Scope - Hiring, progression or both? / Which stages? / By department or overall?
  • Data - What data exists today? / Voluntary self-ID? / Anonymised?
  • Baselines - Compare against what? / External benchmark? / Internal target?
  • Current process - Anything tracked now? / HRIS or a sheet? / Is it anonymised?
  • Outcome - What do you need? / A metric set or a dashboard view?

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: dei-dashboard
intent: null            # setup | advice | review | fix | build | convert | export
scale: null             # Starter | Growth | Scale, only if the answer changes it
areas:
  "Scope": null
  "Data": null
  "Baselines": 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: Use voluntary, anonymised data and aggregate to groups large enough to protect people. Skip any breakdown that would identify someone.

Why this one: Diversity data is easy to collect and easy to misuse. Anonymity and minimum group size are conditions of doing it at all, not nice-to-haves.

Workflow: Voluntary data → Anonymised aggregation → Stage metrics → Review → Action

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
Metric,Department,Period,Measure Type,Value,Target,Group Size,Minimum Group Size Met,Data Source,Notes,Metric ID
Net revenue,Delivery,2026-03,Count,139240,95,12,TRUE,Manual,"Baseline captured in February; headcount denominators still exclude the contract workforce.",
sql
CREATE TABLE dei_dashboard (
  metric VARCHAR(255),
  department VARCHAR(255),
  period VARCHAR(255),
  measure_type VARCHAR(100) NOT NULL,
  value NUMERIC NOT NULL,
  target NUMERIC NOT NULL,
  group_size NUMERIC NOT NULL,
  minimum_group_size_met BOOLEAN NOT NULL,
  data_source VARCHAR(255),
  notes TEXT,
  metric_id SERIAL PRIMARY KEY,
  created_at TIMESTAMP DEFAULT NOW(),
  updated_at TIMESTAMP DEFAULT NOW()
);
json
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "DEI Dashboard",
  "type": "object",
  "additionalProperties": false,
  "properties": {
      "Metric": { "type": "string" },
      "Department": { "type": "string" },
      "Period": { "type": "string" },
      "Measure Type": { "type": "string" },
      "Value": { "type": "number" },
      "Target": { "type": "number" },
      "Group Size": { "type": "number" },
      "Minimum Group Size Met": { "type": "boolean" },
      "Data Source": { "type": "string" },
      "Notes": { "type": "string" },
      "Metric ID": { "type": "integer" }
  },
  "required": [
      "Measure Type",
      "Value",
      "Target",
      "Group Size"
  ]
}
markdown
| CSV column | Notion property | Set after import |
|---|---|---|
| Metric | Title | Use as the database title |
| Department | Text | Leave as Text |
| Period | Text | Leave as Text |
| Measure Type | Select (add options after import) | Convert to Select, add options: "Count", "Percentage", "Ratio", "Average" |
| Value | Number | Convert to Number |
| Target | Number | Convert to Number |
| Group Size | Number | Convert to Number |
| Minimum Group Size Met | Checkbox | Convert to Checkbox |
| Data Source | Text | Leave as Text |
| Notes | Text | Leave as Text |
| Metric 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
1MetrictextVARCHAR(255)stringTextNet revenue
2DepartmenttextVARCHAR(255)stringTextDelivery
3PeriodtextVARCHAR(255)stringText2026-03
4Measure TypeselectVARCHAR(100)stringSelect (add options after import)Count
5ValuenumberNUMERICnumberNumber139240
6TargetnumberNUMERICnumberNumber95
7Group SizenumberNUMERICnumberNumber12
8Minimum Group Size MetcheckboxBOOLEANbooleanCheckboxTRUE
9Data SourcetextVARCHAR(255)stringTextManual
10Noteslong_textTEXTstringTextBaseline captured in February; headcount denominators still exclude the contract workforce.
11Metric IDidSERIAL PRIMARY KEYintegerText (preserve source ID)(blank)
Show full SKILL.md (492 more words)Show less

Select Options

Measure Type

Count | Percentage | Ratio | Average

Relations

Link fields: none

Examples

Prompt

We want to see where our hiring process drops people out.

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

Q: Which stages? A: Apply to offer.

Q: Voluntary self-ID? A: Yes, at joining.

Q: Anonymised? A: Not yet.

Recommended next step - offered, not built:

Use voluntary, anonymised data and aggregate to groups large enough to protect people. Skip any breakdown that would identify someone.

Workflow: Voluntary data → Anonymised aggregation → Stage metrics → Review → Action

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 collect personal data, make hiring decisions or set targets on your behalf.
  • 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 diversity & inclusion 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/dei-dashboard of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 680176d

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

Dei Dashboard 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.

Dei Dashboard compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dei Dashboard this skillsickn33/agentic-awesome-skills47k1 repos~3.2kAutomated safety check: PassMIT
Product Metrics Dashboard Designphuryn/pm-skills27k—~1.3kAutomated safety check: PassMIT
Metrics Dashboardborghei/Claude-Skills886—~1.8kAutomated safety check: PassMIT
DashboardInsForge/InsForge13k—~2.3kAutomated safety check: PassApache-2.0
Observe Metricsruvnet/ruflo74k—~592Automated safety check: NotesMIT
North Star Metricphuryn/pm-skills27k—~1kAutomated safety check: PassMIT

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Questions about Dei Dashboard

What does Dei Dashboard do?

Diversity, equity and inclusion dashboard: metric by department and period, value against target, group size and minimum-threshold flag. Dei Dashboard is an agent skill from sickn33/agentic-awesome-skills. Diversity, equity and inclusion dashboard: metric by department and period, value against target, group size and minimum-threshold flag.

How do I install Dei Dashboard in Claude Code?

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

How do I install Dei Dashboard in Codex?

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

Can I use Dei Dashboard 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 dei-dashboard -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dei-dashboard, .gemini/skills/dei-dashboard, .github/skills/dei-dashboard and .opencode/skills/dei-dashboard in your project.

What does Dei Dashboard need to run?

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

Does Dei Dashboard 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 Dei Dashboard 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 Dei Dashboard use?

Dei Dashboard 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 Dei Dashboard use?

About 3.2k tokens (SKILL.md is roughly 13k 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 Dei Dashboard?

Skills that share tags, products or a category with Dei Dashboard: Product Metrics Dashboard Design (phuryn/pm-skills, 27k stars), Metrics Dashboard (borghei/Claude-Skills, 886 stars), Dashboard (InsForge/InsForge, 13k stars) and Observe Metrics (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dei Dashboard?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 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.