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 Arnosdeus/agent-toolkit --skill task-skillgit clone --depth 1 https://github.com/Arnosdeus/agent-toolkitWrote 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/arnosdeus/agent-toolkit/task-skill)<a href="https://agentmods.dev/skills/arnosdeus/agent-toolkit/task-skill"><img src="https://agentmods.dev/badge/skills/arnosdeus/agent-toolkit/task-skill/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/arnosdeus/agent-toolkit/task-skill"><img src="https://agentmods.dev/badge/skills/arnosdeus/agent-toolkit/task-skill.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 | $0.00030 | $0.00163 |
| Opus 5.5 | $0.00012 | $0.00065 |
| Sonnet 5.5 | $0.00006 | $0.00033 |
| Haiku 4.5 | $0.00003 | $0.00016 |
Grade A, and why
task-skill 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 1mo 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.
What it actually says
Task Skill
- Restate the requested outcome and constraints; inspect the repository and all applicable instructions.
- Identify relevant files, architecture, dependencies, existing commands, and likely regressions.
- Make a short plan and implement incrementally, touching only relevant files.
- Run focused tests, then available lint/type checks. For frontend changes, run meaningful Playwright checks.
- Review the diff, inspect generated artifacts, check regressions and security implications, and clean temporary work.
- Summarize exactly what changed, what was verified, commands/results, and any remaining limitation. Never claim unverified success.
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.
- 1mo ago First seen · 14 lines · 30 tokens per session scan A 9a36232059fe
task-skill is a skill published in the GitHub repository Arnosdeus/agent-toolkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 163 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
opensrc
Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for"…
first-plan-lens-engine
Skill que define o motor de detecção e roteamento de stack lenses do first-plan. Mapeia manifestos (go.mod, package.json, etc) -> stack -> papel -> lens skill correspondente. Use quando precisar decidir qual lens aplicar a uma pasta durante Discovery, ou quando estender suporte a uma nova stack.
first-plan-pattern-extraction
Skill que define como extrair padrões do código com confidence scoring. Use durante Discovery quando precisar identificar convenções, anti-padrões ou padrões idiomáticos do projeto. Cada padrão extraído deve vir com exemplo concreto do código (path:line).
first-plan-lens-terraform
Stack lens para Terraform e IaC similar (OpenTofu, Pulumi). Use durante Discovery quando arquivos .tf forem detectados. Cobre estrutura de módulos, backend de state, environments, providers, naming/tagging.
code-review
Orchestrate an adaptive multi-agent review of a pull request or local change that builds a structural and semantic brief, assigns risk-based reviewer lanes at runtime, challenges and deduplicates their findings, verifies each one, and renders an evidence-backed review. Use when a pull request or local change needs…
jira-issue-create
Create one or more Jira issues through acli from user-approved drafts using heading-based Context, Acceptance criteria, and Engineering notes sections. Use only when explicitly invoked to create Jira issues; use jira-issue-refine when requirements still need refinement.