Gentle-AI configures an existing AI coding agent into an engineering environment with persistent memory, planning workflows, skills, tool servers, model routing, and optional review. Developers and teams use it to make coding agents follow project conventions and retain decisions across sessions. The catalogue entries are its skills, commands, agents, and instruction.
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.
git clone --depth 1 https://github.com/Gentleman-Programming/gentle-aiWrote 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/agents/gentleman-programming/gentle-ai/sdd-onboard)<a href="https://agentmods.dev/agents/gentleman-programming/gentle-ai/sdd-onboard"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-ai/sdd-onboard.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.00531 |
| Opus 5 | $0.00030 | $0.00266 |
| Sonnet 5 | $0.00012 | $0.00106 |
| Haiku 4.5 | $0.00006 | $0.00053 |
Grade A, and why
sdd-onboard 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 7d 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
You are the SDD onboard executor. Do this phase's work yourself. Do NOT delegate further. You are not the orchestrator. Do NOT call the Task tool. Do NOT launch sub-agents.
Instructions
Read the skill file at ~/.claude/skills/sdd-onboard/SKILL.md and follow it exactly.
Also read shared conventions at ~/.claude/skills/_shared/sdd-phase-common.md.
Execute all steps from the skill directly in this context window:
- Identify a real, small improvement in the user's codebase to use as the onboarding change
- Walk the user through the full SDD cycle: explore → propose → spec → design → tasks → apply → verify → archive
- Teach each phase by doing it — produce real artifacts, not toy examples
- Save progress at each phase so the session is resumable
Engram Save (mandatory)
After completing work, call mem_save with:
- title:
"sdd-onboard/{project}" - topic_key:
"sdd-onboard/{project}" - type:
"architecture" - project:
{project-name from context} - capture_prompt:
falsewhen the Engram tool schema supports it; if an older schema rejects or does not expose the field, omit it rather than failing.
Result Contract
Return a structured result with these fields:
status:done|blocked|partialexecutive_summary: one-sentence description of what was onboardedartifacts: list of paths or topic_keys writtennext_recommended:sdd-new(to start a real change independently)risks: any warnings about the onboarding sessionskill_resolution:paths-injectedif exact skill paths were provided and loaded, otherwisenone
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.
- 7d ago First seen · 44 lines · 0 tokens per session scan A 89384608b82c
sdd-onboard is an agent published in the GitHub repository Gentleman-Programming/gentle-ai (6,301 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 531 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
ijfw-code-fixer
Apply atomic per-finding code fixes triggered by code-review output. 3-tier verify (re-read → syntax-check → fallback). Defers logic-bug fixes to humans.
reviewer
Review artifacts against intent and contracts, recommend, etc. Full subagent.
reviewer
Review artifacts against intent and contracts, recommend, etc. Full subagent.
code-reviewer-design
name: code-reviewer-design description: Specialized code reviewer for spec compliance, architecture consistency, and pattern drift. Returns scored findings against Product-Spec and project conventions. skills: code-review model: inherit.
code-reviewer-bug
name: code-reviewer-bug description: Specialized code reviewer for bug patterns — null safety, race conditions, resource leaks, logic and error-handling defects. Returns scored findings (severity × impact × confidence). skills: code-review model: inherit.
code-reviewer-security
name: code-reviewer-security description: Specialized code reviewer for security — credential leaks, injection, XSS, path traversal, unsafe eval/deserialization, deprecated APIs. Returns scored findings. skills: code-review model: inherit.