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engagement-deep-dive

engagement-deep-dive is a skill for Claude Code from trycomp-io/comp-skills. It costs 149 tokens per session (1,419 once invoked), scanned A, original, MIT.

An analysis of employee engagement survey data, such as eNPS, which is a measure based on how likely employees are to recommend the organization. It compares results across areas, time at the company, managers, and job levels.

In plain words
What is it for?
Use it to analyze a survey CSV, identify low-scoring areas and managers, find patterns, and prioritize actions in an executive HTML report or chat analysis.
Why use it?
Overall survey scores can hide problems in particular teams or groups, so segmentation shows where engagement is weakest.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Claude Code.

Part of the comp-skills plugin — 37 skills shipped together

not rated 2repo 3mo ago A scan 149 tokens original MIT

Good fit Use it to analyze a survey CSV, identify low-scoring areas and managers, find patterns, and prioritize actions in an executive HTML report or chat analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/trycomp-io/comp-skills/engagement-deep-dive
View source ↗ trycomp-io/comp-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add trycomp-io/comp-skills --skill engagement-deep-dive
Clone the repo
git clone --depth 1 https://github.com/trycomp-io/comp-skills

Made for: Claude Code.

Or install comp-skills, the plugin that ships this one along with the rest of its 37 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for engagement-deep-dive

README.md
[![agentmods](https://agentmods.dev/badge/skills/trycomp-io/comp-skills/engagement-deep-dive.svg)](https://agentmods.dev/skills/trycomp-io/comp-skills/engagement-deep-dive)
Your own site
<a href="https://agentmods.dev/skills/trycomp-io/comp-skills/engagement-deep-dive"><img src="https://agentmods.dev/badge/skills/trycomp-io/comp-skills/engagement-deep-dive.svg" alt="Measured on agentmods" height="20"></a>
Per session 149 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,419 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00149 $0.01419
Opus 5 $0.00075 $0.00709
Sonnet 5 $0.00030 $0.00284
Haiku 4.5 $0.00015 $0.00142

Measured 8d ago against content hash d811d7749ecf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

engagement-deep-dive scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (eam_client.py, scripts/engagement_dive.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/engagement-deep-dive/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dual-mode operation (Code + Cowork)

HTML pelo design system (obrigatório). Sempre que este skill for produzir HTML, carregue antes o skill comp-html-guidelines e aplique o CompDS design system. Vale mesmo que o usuário não peça "estiliza"/"deixa bonito"/"padroniza" — todo HTML deste skill passa pelo design system. Isso não altera a metodologia abaixo; governa só a camada visual do HTML.

Detect platform at start:

  • If you have the Bash tool AND can run Python → use script mode (deterministic, writes the rich HTML report). This is the existing workflow below.
  • Otherwise (e.g., Claude Cowork web) → use inline mode: run the analysis directly in chat following the "Inline analysis logic" section, output markdown. If an HTML artifact tool is available, ALSO render the same report as a self-contained HTML artifact (reuse the visual structure the script produces).

Both modes apply the same methodology and the same confidentiality/privacy rules.

Inline analysis logic (Cowork mode)

Como o usuário fornece os dados

  • Cole a tabela do survey no chat ou anexe um CSV. Mínimo: score (0-10 ou 1-5) OU enps (0-10). Recomendado: area, tenure_months, manager_id, level.
  • Survey grande (>~50 linhas) é difícil de processar manualmente, então sugira rodar em Claude Code (script mode).

Normalização (igual ao script)

  • tenure_months vira faixa: <6 0-6m; <12 6-12m; <24 1-2y; <36 2-3y; <60 3-5y; ≥60 5y+; vazio → Desconhecido.
  • Classificação eNPS (escala 0-10): ≥9 promoter; 7-8 passive; 0-6 detractor.

Metodologia (fixa, idêntica ao script)

  1. Score médio global = média aritmética de todos os score válidos.
  2. eNPS global = (% promoters − % detractors) sobre as respostas eNPS classificadas, em pontos ((p − d) ÷ n × 100).
  3. Segmentação por área, tenure band, nível e gestor: para cada segmento, média/min/max do score. Confidencialidade/robustez: só exiba segmentos com ≥3 respostas (descarte os com menos de 3). Ordene áreas/tenure/nível por score crescente (piores primeiro). Gestores: bottom 10 (também só com ≥3 respostas).

Read the full file on GitHub · 110 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 110 lines · 149 tokens per session scan A d811d7749ecf

Subscribe to this mod's changes

engagement-deep-dive is a skill published in the GitHub repository trycomp-io/comp-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 149 tokens to every session and 1,419 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsbrazilclaudeclaude-skillscompensationhrpeople-analytics
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