Softaworks Agent Toolkit is a collection of packaged instructions, scripts, agents, and commands that give AI coding assistants reusable procedures for development, documentation, planning, and professional work. Developers install its components individually in compatible coding-agent environments; catalogue entries represent parts of this toolkit.
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 softaworks/agent-toolkit --skill skill-judgegit clone --depth 1 https://github.com/softaworks/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/softaworks/agent-toolkit/skill-judge)<a href="https://agentmods.dev/skills/softaworks/agent-toolkit/skill-judge"><img src="https://agentmods.dev/badge/skills/softaworks/agent-toolkit/skill-judge/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/softaworks/agent-toolkit/skill-judge"><img src="https://agentmods.dev/badge/skills/softaworks/agent-toolkit/skill-judge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 27 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00041 | $0.06532 |
| Opus 5.5 | $0.00016 | $0.02613 |
| Sonnet 5.5 | $0.00008 | $0.01306 |
| Haiku 4.5 | $0.00004 | $0.00653 |
Grade A, and why
skill-judge 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.
How it starts
The opening of the file, as written. The whole thing — 753 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Judge
Evaluate Agent Skills against official specifications and patterns derived from 17+ official examples.
Core Philosophy
What is a Skill?
A Skill is NOT a tutorial. A Skill is a knowledge externalization mechanism.
Traditional AI knowledge is locked in model parameters. To teach new capabilities:
Traditional: Collect data → GPU cluster → Train → Deploy new version
Cost: $10,000 - $1,000,000+
Timeline: Weeks to months
Skills change this:
Skill: Edit SKILL.md → Save → Takes effect on next invocation
Cost: $0
Timeline: Instant
This is the paradigm shift from "training AI" to "educating AI" — like a hot-swappable LoRA adapter that requires no training. You edit a Markdown file in natural language, and the model's behavior changes.
The Core Formula
Good Skill = Expert-only Knowledge − What Claude Already Knows
A Skill's value is measured by its knowledge delta — the gap between what it provides and what the model already knows.
- Expert-only knowledge: Decision trees, trade-offs, edge cases, anti-patterns, domain-specific thinking frameworks — things that take years of experience to accumulate
- What Claude already knows: Basic concepts, standard library usage, common programming patterns, general best practices
When a Skill explains "what is PDF" or "how to write a for-loop", it's compressing knowledge Claude already has. This is token waste — context window is a public resource shared with system prompts, conversation history, other Skills, and user requests.
Tool vs Skill
| Concept | Essence | Function | Example |
|---|---|---|---|
| Tool | What model CAN do | Execute actions | bash, read_file, write_file, WebSearch |
| Skill | What model KNOWS how to do | Guide decisions | PDF processing, MCP building, frontend design |
Tools define capability boundaries — without bash tool, model can't execute commands. Skills inject knowledge — without frontend-design Skill, model produces generic UI.
What ships with it
1 file 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.
- 1mo ago First seen · 753 lines · 41 tokens per session scan A 737ef3628f0e
skill-judge is a skill published in the GitHub repository softaworks/agent-toolkit (2,591 stars, last pushed 7mo ago), licensed MIT. It adds 41 tokens to every session and 6,532 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
plugin-creator
Create and scaffold plugin directories for Codex with a required .codex-plugin/plugin.json, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries…
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.