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.
npx agentmods add agents/gentleman-programming/gentle-ai/sdd-applygit 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-apply)<a href="https://agentmods.dev/agents/gentleman-programming/gentle-ai/sdd-apply"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-ai/sdd-apply.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 | $0.00045 | $0.00772 |
| Opus 5 | $0.00023 | $0.00386 |
| Sonnet 5 | $0.00009 | $0.00154 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
sdd-apply 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 5d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the SDD apply 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-apply/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:
- Read tasks artifact (required): read the
tasksartifact from the orchestrator-injected locator (seesdd-phase-common.mdsection B) - Read spec artifact (required): read the
specartifact from the orchestrator-injected locator (seesdd-phase-common.mdsection B) - Read design artifact (required): read the
designartifact from the orchestrator-injected locator (seesdd-phase-common.mdsection B) 3b. Resume from previous apply-progress (whenever its locator resolves): read theapply-progressartifact from the orchestrator-injected locator (seesdd-phase-common.mdsection B), skip tasks it records as complete, and merge it into your own progress when persisting. This is the durable continuation path for a work unit that spans more than one attempt: continue from recorded progress rather than rereading the whole scope, and never revert delivered work because the unit is not finished yet. - Detect TDD mode from config or existing test patterns
- Implement assigned tasks: in TDD mode follow RED → GREEN → REFACTOR; in standard mode write code then verify
- Match existing code patterns and conventions
- Mark each task
[x]complete as you finish it - Persist progress to active backend
Engram Save (mandatory)
After completing work, call mem_save with:
- title:
"sdd/{change-name}/apply-progress" - topic_key:
"sdd/{change-name}/apply-progress" - 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.
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.
- 5d ago First seen · 51 lines · 0 tokens per session scan A 4604b8bd3779
sdd-apply is an agent published in the GitHub repository Gentleman-Programming/gentle-ai (6,255 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 772 once invoked, about $0.0002 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
Demonstrate
Agent for demonstrating VS Code features.
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analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.