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/candidate-screeningnpx skills add trycomp-io/comp-skills --skill candidate-screeninggit 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/candidate-screening)<a href="https://agentmods.dev/skills/trycomp-io/comp-skills/candidate-screening"><img src="https://agentmods.dev/badge/skills/trycomp-io/comp-skills/candidate-screening.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 | $0.00159 | $0.01776 |
| Opus 5 | $0.00079 | $0.00888 |
| Sonnet 5 | $0.00032 | $0.00355 |
| Haiku 4.5 | $0.00016 | $0.00178 |
Grade A, and why
candidate-screening 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 4d 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 — 147 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-guidelinese 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
Bashtool AND can run Python → use script mode (writes the rich HTML/markdown file). Existing workflow below. - Otherwise (e.g., Claude Cowork) → use inline mode: gather the same inputs conversationally, then produce the output directly in chat as markdown following the structure below. If an HTML artifact tool is available, ALSO render a self-contained HTML version (Tailwind CDN) matching the script's template.
Inline generation logic (Cowork mode)
Inputs a coletar: contexto/critérios da vaga (idealmente do job-profile-builder; se não houver scorecard, derive 4-6 critérios) e os candidatos (paste de perfis, CSV, CVs, transcrições). Mesma lógica de avaliação dos Steps 1-3 abaixo.
Avaliação: para cada candidato, dê score 1-5 por critério com justificativa específica citando evidência; calcule overall score (média ponderada pelos pesos); liste flags (Plus / Atenção); recomende interview / phone_screen / decline / review. Ranqueie por overall score desc.
Estrutura de saída (mesma do script). Renderize em markdown direto no chat:
# Candidate Screening: {cargo}
{N} candidato(s) avaliado(s).
## Ranking
| # | Candidato | Cargo atual | Score | Recomendação |
|---|---|---|---|---|
| 1 | **{nome}** | {cargo atual} | {0.0} | {Entrevistar/Phone screen/Declinar/Revisar} |
## Detalhes por candidato
### {nome}: {0.0}
*{cargo atual}*. **Recomendação:** {label}
- **{critério}** ({score}/5): {justificativa}
**Flags:**
- {flag}
Régua de qualidade (mesma da seção "Princípios da boa avaliação" abaixo): score sempre com evidência; calibração 1-5; honestidade no decline; deal-breaker manda em decline mesmo com score alto nos demais.
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.
- 4d ago First seen · 147 lines · 159 tokens per session scan A a2d4f620d9b1
candidate-screening is a skill published in the GitHub repository trycomp-io/comp-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 159 tokens to every session and 1,776 once invoked, about $0.0008 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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