Maintainer of this skill?
Automated security scan — not a certification.
Gentle-AI configures the AI coding agents you already use: Claude Code, Cursor, OpenCode, Codex, Pi, and more. Choose persistent memory, Organic-Driven Development, curated skills, MCP servers, personas, and optional bounded review. Open source, no agent lock-in.
# Add to your Claude Code skills
git clone https://github.com/Gentleman-Programming/gentle-aiLast scanned: 9/6/2026
{
"issues": [
{
"file": "README.md",
"line": 265,
"type": "remote-install",
"message": "Install command (remote install script piped to a shell — review the source before running): \"curl -fsSL https://raw.githubusercontent.com/Gentleman-Programming/gentle-ai/mai\"",
"severity": "low"
}
],
"status": "PASSED",
"scannedAt": "2026-09-06T08:23:07.729Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how gentle-ai compares with popular alternatives.
gentle-ai is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Gentleman-Programming. Gentle-AI configures the AI coding agents you already use: Claude Code, Cursor, OpenCode, Codex, Pi, and more. Choose persistent memory, Organic-Driven Development, curated skills, MCP servers, personas, and optional bounded review. Open source, no agent lock-in. It has 7,891 GitHub stars.
Yes. gentle-ai passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Gentleman-Programming/gentle-ai" and add it to your Claude Code skills directory (see the Installation section above).
gentle-ai is primarily written in Go. It is open-source under Gentleman-Programming on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh gentle-ai against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
https://github.com/user-attachments/assets/fa5c0cfe-06e7-4c0d-bd6e-8ac7cb934339
https://github.com/user-attachments/assets/6d2bc422-a4dd-4ecf-a04b-fcd3bea7fea9
If Gentle-AI made your agent worth trusting, a star helps other people find it.
WORKS WITH THE AGENT YOU ALREADY HAVE
Pi · OpenCode · Claude Code · Codex · Cursor · VS Code Copilot · Gemini CLI · Kilo Code Kimi Code · Kiro IDE · Qwen Code · Hermes · Antigravity · Windsurf · OpenClaw · Trae · Conductor
17 integrations · native configuration · compare capabilities →
The cost of a fresh session is not the tokens — it is you, re-explaining the same decisions every morning. Engram removes that: your agent writes down what it learns as it goes and reaches for it before it reaches for you, so context accumulates instead of resetting.
Small changes should not need a planning pipeline, and larger work should not lose its context between sessions. Organic Driven Development (ODD) keeps understood changes lightweight and gives substantial, authorized work one recoverable feature document. Questions, reviews and proposals stay read-only until you ask for a change. The agent explores before changing code, checks the results, and keeps progress current so work can resume without rebuilding the plan.
Tasks are sized by understanding, risk, and resumability, never by file count. A small task — understood, with contained risk, and resumable from the diff alone — runs inline: no subagents, no task document. Work becomes large only when it can't be resumed from the diff — several sessions, external waits, or requirements too long to survive compaction — never because of its size. The feature document of a large task holds your spec verbatim, and every handoff points at it instead of paraphrasing it.
https://github.com/user-attachments/assets/857a2195-34b8-4720-87af-f71bca3dec92
A subagent costs a fresh session, uncached reads, and a handoff. So the orchestrator delegates only when it can name the reason:
| Reason | When it applies |
|---|---|
| Exploration | Understanding needs more than one inline batch (about 3 calls and 10k tokens) or more than about five sequential lookups; one read-only explorer returns a short map with path:line evidence |
| Parallel writers | Two or more independent units of behavior (code, tests, and docs together) with disjoint edit surfaces, each needing several minutes of work: several files or roughly 100+ changed lines, because a subagent spends one to two minutes reading before it writes. A delivery that depends on another is not independent |
| Context backstop | A long session hands its next bounded unit to a fresh worker |
| Independent verifier | A change that names a high-risk item (data, security, shared contracts, concurrency, delivery, or no test that would catch a regression); never a native risk tier alone, an unclear or failed assessment, or the model that wrote the change |
A trigger holds only when its condition truly holds; none holds by default, and when in doubt the parent works inline. Without a reason the parent writes inline, following its feature document. File count, task size, or a model price ratio never decide the route on their own.
https://github.com/user-attachments/assets/ccd96c9e-e0fd-4504-ad94-0302398f13a9
The routing rules above came from benchmarks, not intuition. These are Gentle Shell runs on a local benchmark with hidden acceptance suites; each cell is a single run (n=1), and cost is API-equivalent.
| Scenario | Before | After |
|---|---|---|
| 3-line bug in one function | 335 s · 56 turns · $0.339 | * |