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.
npx agentmods add skills/trycomp-io/comp-skills/manager-effectiveness-scorecardnpx skills add trycomp-io/comp-skills --skill manager-effectiveness-scorecardgit clone --depth 1 https://github.com/trycomp-io/comp-skillsWrote 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.
[](https://agentmods.dev/skills/trycomp-io/comp-skills/manager-effectiveness-scorecard)<a href="https://agentmods.dev/skills/trycomp-io/comp-skills/manager-effectiveness-scorecard"><img src="https://agentmods.dev/badge/skills/trycomp-io/comp-skills/manager-effectiveness-scorecard.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00224 | $0.01864 |
| Opus 5 | $0.00112 | $0.00932 |
| Sonnet 5 | $0.00045 | $0.00373 |
| Haiku 4.5 | $0.00022 | $0.00186 |
Grade A, and why
manager-effectiveness-scorecard 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 5d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dual-mode operation (Code + Cowork)
HTML through the design system (required). Whenever this skill produces HTML, load the
comp-html-guidelinesskill 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
Bashtool 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
nameemanager. 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
managere (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)
- Agrupe colaboradores por gestor.
span= número de directs. - Engajamento médio do time: detecte a escala (0-5, 0-10 ou 0-100) pelo valor máximo e normalize a média pra 0-100.
- Taxa de promoção = directs promovidos no ciclo ÷ span × 100.
- Atrito do time = saídas ÷ (directs atuais + saídas) × 100. Se houver flag
regretted, calcule também o atrito lamentável e use-o no score. - 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
- Bandas: <40 At-risk · 40-59 Developing · 60-79 Solid · 80+ Exemplar.
- Flags:
- "Gestor em risco" = atrito ≥20% E engajamento <60.
- "Sobrecarregado" = span >12 (severo se também engajamento <60).
- "Subutilizado" = span de 1 a 3.
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.
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.
- 5d ago First seen · 133 lines · 224 tokens per session scan A 34fe88c4d8d3
manager-effectiveness-scorecard is a skill published in the GitHub repository trycomp-io/comp-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 224 tokens to every session and 1,864 once invoked, about $0.0011 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.
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