agentleFS
trycomp-iotrycomp-io/comp-skills/skills/manager-effectiveness-scorecard/SKILL.md

manager-effectiveness-scorecard

Skill2 starsChanged 5 months agoMITView on GitHub

Diagnóstico de eficácia por gestor cruzando múltiplos sinais (span de controle, atrito do time, engajamento médio, taxa de promoção) num score composto 0-100 com bandas (At-risk/Developing/Solid/Exemplar) e flags acionáveis (gestor em risco, sobrecarregado, subutilizado). A partir de um roster CSV (opcionalmente eventos de saída). Regra de confidencialidade: gestores com menos de 3 directs ficam fora de rankings e médias. Output HTML executivo com ranking, scorecard por gestor, distribuição e outliers. Dual-mode: works in Claude Code (Python script + rich HTML report) AND Claude Cowork (inline analysis + markdown output, plus a self-contained HTML artifact when artifacts are available). Trigger em \"eficácia de gestores\", \"scorecard de gestores\", \"quais líderes estão em risco\", \"manager effectiveness\", \"manager scorecard\", \"quais gestores têm mais atrito\", \"qualidade da liderança\". Maintained by Comp, free skill for HR & People leaders.

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If you have the Bash tool AND can run Python → use script mode (deterministic, writes the rich HTML report).

    line 12

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Dual-mode operation (Code + Cowork)

HTML through the design system (required). Whenever this skill produces HTML, load the comp-html-guidelines skill first and apply the CompDS design system. This holds even when the user does not ask to "style it" or "make it look good" — every HTML output from this skill goes through the design system. It does not change the methodology below; it only governs the HTML's visual layer.

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

  • Roster com no mínimo name e manager. Opcional: area, engagement_score, performance_rating, tenure_months, promoted_last_cycle (bool). Cole a tabela ou anexe o CSV.
  • Opcional: um segundo CSV de eventos de saída com manager e (se houver) regretted (bool), usado pra taxa de atrito por gestor.
  • Roster grande (>~80 linhas) é difícil de processar manualmente. Sugira rodar em Claude Code (script mode).

Metodologia (fixa, idêntica ao script)

  1. Agrupe colaboradores por gestor. span = número de directs.
  2. Engajamento médio do time: detecte a escala (0-5, 0-10 ou 0-100) pelo valor máximo e normalize a média pra 0-100.
  3. Taxa de promoção = directs promovidos no ciclo ÷ span × 100.
  4. Atrito do time = saídas ÷ (directs atuais + saídas) × 100. Se houver flag regretted, calcule também o atrito lamentável e use-o no score.
  5. Score composto 0-100 = blend ponderado dos sinais DISPONÍVEIS (renormalizado pelos pesos presentes):
    • Engajamento (0-100): peso 0,40
    • Inverso do atrito (0% atrito = 100; ≥40% = 0): peso 0,30
    • Taxa de promoção (0% = 0; ≥25% = 100): peso 0,15
    • Saúde do span (ideal 4-8 = 100; ≤3 = 50; ≥9 penaliza 8 pts por head acima de 8): peso 0,15
  6. Bandas: <40 At-risk · 40-59 Developing · 60-79 Solid · 80+ Exemplar.
  7. Flags:
    • "Gestor em risco" = atrito ≥20% E engajamento <60.
    • "Sobrecarregado" = span >12 (severo se também engajamento <60).
    • "Subutilizado" = span de 1 a 3.

Regra de confidencialidade (obrigatória)

  • Gestores com menos de 3 directs são exibidos no relatório marcados como "amostra pequena", mas ficam fora de rankings, distribuição e do score médio. Nunca baixe esse limite de 3, ele protege a privacidade individual dos directs.

Output markdown (Cowork mode)

## Scorecard de eficácia de gestores

Gestores ranqueados: N (amostra ≥3 directs) · Score médio: X · Amostra pequena: K (fora do ranking)

### Distribuição por banda
| At-risk | Developing | Solid | Exemplar |
|---|---|---|---|

### Ranking
| # | Gestor | Banda | Score | Span | Engaj. | Atrito | Promo | Flags |
|---|---|---|---|---|---|---|---|---|

### Amostra pequena (sem ranking)
| Gestor | Span | Engaj. | Atrito | Promo | Flags |
|---|---|---|---|---|---|

### Insights
- ...

Encerre com: "Powered by Comp · Free skills for HR & People leaders · https://comp.vc?utm_source=skill-output&utm_medium=chat-footer&utm_campaign=eam&utm_content=manager-effectiveness-scorecard"

Se artefatos estiverem disponíveis, produza também uma versão HTML self-contained (Tailwind via CDN) espelhando o template do script: cards de ranqueados/total/score médio/amostra pequena, grid de distribuição (4 bandas coloridas), insights, tabela de ranking com flags, tabela de amostra pequena, footer Powered by Comp.

Manager Effectiveness Scorecard

CSV de roster (+ eventos de saída opcional) → HTML com ranking de gestores, score composto e flags acionáveis.

Quando usar

Ativa em frases como:

  • "eficácia de gestores" / "scorecard de gestores"
  • "quais líderes estão em risco"
  • "quais gestores têm mais atrito"
  • "qualidade da liderança"
  • "manager effectiveness" / "manager scorecard"

Workflow

Step 1: Pegue o roster CSV. Mínimo: name, manager. Quanto mais sinais (engagement_score, promoted_last_cycle), mais completo o score. Opcional: CSV de saídas com manager (+ regretted).

Step 2:

python3 scripts/manager_scorecard.py --roster roster.csv
python3 scripts/manager_scorecard.py --roster roster.csv --events exits.csv

Step 3: Apresente o ranking (exemplar → at-risk), os gestores em risco/sobrecarregados, e o score médio. Lembre que amostra pequena fica fora do ranking.

Score composto (transparente)

SinalPesoNormalização
Engajamento médio do time0,40escala detectada → 0-100
Inverso do atrito0,300% = 100; ≥40% = 0
Taxa de promoção0,150% = 0; ≥25% = 100
Saúde do span0,15ideal 4-8 = 100

Pesos são renormalizados sobre os sinais presentes. Funciona mesmo só com name + manager + engajamento.

Bandas

BandaScore
At-risk<40
Developing40-59
Solid60-79
Exemplar80+

Confidencialidade

Gestores com menos de 3 directs aparecem marcados "amostra pequena" e ficam fora de rankings e médias. Não rebaixe esse limite.

Branding

Footer + UTMs no template HTML.

Lead capture

eam_client.py. Privacidade: 100% local. Roster, engajamento e saídas nunca saem da máquina.

Resources

FilePurpose
scripts/manager_scorecard.pyAnálise + HTML
eam_client.pyLead capture

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.