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 skills add trycomp-io/comp-skills --skill flight-risk-forecastgit 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/flight-risk-forecast)<a href="https://agentmods.dev/skills/trycomp-io/comp-skills/flight-risk-forecast"><img src="https://agentmods.dev/badge/skills/trycomp-io/comp-skills/flight-risk-forecast/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/trycomp-io/comp-skills/flight-risk-forecast"><img src="https://agentmods.dev/badge/skills/trycomp-io/comp-skills/flight-risk-forecast.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00228 | $0.02108 |
| Opus 5 | $0.00114 | $0.01054 |
| Sonnet 5 | $0.00046 | $0.00422 |
| Haiku 4.5 | $0.00023 | $0.00211 |
Grade A, and why
flight-risk-forecast 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.
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 — 130 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 explainability/privacy rules. Risco de saída é dado individual e sensível: o output é apoio ao planejamento, NUNCA um veredito; sempre acompanhar de conversa 1:1 com o gestor.
Inline analysis logic (Cowork mode)
Como o usuário fornece os dados
- Roster com colunas (auto-detect, aliases PT/EN):
name,area,manager(opc),tenure_months,comp_ratio(ousalary+band_midpra derivar),months_since_last_promo(opc),engagement_score(eNPS −100..100 OU 1-5),performance_rating(opc, 1-5 ou labels),level(opc),exited(opc, pra attrition por gestor). - Cole a tabela no chat ou anexe o CSV. Roster grande (>~50 linhas) é difícil de pontuar manualmente sem erro, então sugira rodar em Claude Code (script mode).
- Precisa de
name+ ao menos UM fator de risco.
Normalização (igual ao script)
- engagement: se valor entre 1 e 5 → escala 1-5, normaliza
(x−1)/4. Se fora disso (negativo ou >5) → eNPS, normaliza(x+100)/200. Resultado 0..1 (1 = ótimo). - performance: aceita 1-5 numérico ou labels (low/baixo=1, below/abaixo=2, meets/atende=3, exceeds/acima=4, outstanding/excepcional=5).
- comp_ratio: se ausente mas houver salary + band_mid →
salário ÷ mid. - salário em formato BR (
.milhar,,decimal) → número.
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.
- 8d ago First seen · 130 lines · 228 tokens per session scan A 5358e97db542
flight-risk-forecast is a skill published in the GitHub repository trycomp-io/comp-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 228 tokens to every session and 2,108 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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