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 gepettogit 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/gepetto)<a href="https://agentmods.dev/skills/softaworks/agent-toolkit/gepetto"><img src="https://agentmods.dev/badge/skills/softaworks/agent-toolkit/gepetto/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/gepetto"><img src="https://agentmods.dev/badge/skills/softaworks/agent-toolkit/gepetto.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00035 | $0.02583 |
| Opus 5.5 | $0.00014 | $0.01033 |
| Sonnet 5.5 | $0.00007 | $0.00517 |
| Haiku 4.5 | $0.00003 | $0.00258 |
Grade A, and why
gepetto 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.
This is a copy
100% identical to gepetto — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gepetto
Orchestrates a multi-step planning process: Research → Interview → Spec Synthesis → Plan → External Review → Sections
CRITICAL: First Actions
BEFORE anything else, do these in order:
1. Print Intro
Print intro banner immediately:
═══════════════════════════════════════════════════════════════
GEPETTO: AI-Assisted Implementation Planning
═══════════════════════════════════════════════════════════════
Research → Interview → Spec Synthesis → Plan → External Review → Sections
Note: GEPETTO will write many .md files to the planning directory you pass it
2. Validate Spec File Input
Check if user provided @file at invocation AND it's a spec file (ends with .md).
If NO @file was provided OR the path doesn't end with .md, output this and STOP:
═══════════════════════════════════════════════════════════════
GEPETTO: Spec File Required
═══════════════════════════════════════════════════════════════
This skill requires a markdown spec file path (must end with .md).
The planning directory is inferred from the spec file's parent directory.
To start a NEW plan:
1. Create a markdown spec file describing what you want to build
2. It can be as detailed or as vague as you like
3. Place it in a directory where gepetto can save planning files
4. Run: /gepetto @path/to/your-spec.md
To RESUME an existing plan:
1. Run: /gepetto @path/to/your-spec.md
Example: /gepetto @planning/my-feature-spec.md
═══════════════════════════════════════════════════════════════
Do not continue. Wait for user to re-invoke with a .md file path.
3. Setup Planning Session
Determine session state by checking existing files:
- Set
planning_dir= parent directory of the spec file - Set
initial_file= the spec file path - Scan for existing planning files:
claude-research.mdclaude-interview.mdclaude-spec.mdclaude-plan.mdclaude-integration-notes.mdclaude-ralph-loop-prompt.mdclaude-ralphy-prd.mdreviews/directorysections/directory
What ships with it
6 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.
- 1mo ago First seen · 346 lines · 35 tokens per session scan A 6a52b8850ecb
gepetto is a skill published in the GitHub repository softaworks/agent-toolkit (2,591 stars, last pushed 7mo ago), licensed MIT. It adds 35 tokens to every session and 2,583 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to gepetto, differing in 0 lines, and is treated as a copy.
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.