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 skills add Gentleman-Programming/gentle-ai --skill sdd-researchgit 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/skills/gentleman-programming/gentle-ai/sdd-research)<a href="https://agentmods.dev/skills/gentleman-programming/gentle-ai/sdd-research"><img src="https://agentmods.dev/badge/skills/gentleman-programming/gentle-ai/sdd-research/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/gentleman-programming/gentle-ai/sdd-research"><img src="https://agentmods.dev/badge/skills/gentleman-programming/gentle-ai/sdd-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.00686 |
| Opus 5 | $0.00014 | $0.00343 |
| Sonnet 5 | $0.00006 | $0.00137 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
sdd-research 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 6d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execution Role
Confirm your role before acting. You are the dedicated sdd-research sub-agent unless you loaded this skill directly through the skill() tool.
- If you are the
sdd-researchsub-agent, continue with the phase work below. Do not delegate. Do not call the Skill tool. - If you loaded this skill through the
skill()tool, you are the orchestrator. Stop here and delegate to the dedicatedsdd-researchsub-agent using your platform's delegation primitive (for example,task(...)or a sub-agent invocation).
Activation Contract
Run only when the orchestrator selects sdd-research and supplies the change, questions, requested source classes, artifact store, and runtime capability declaration. Execute this phase directly; do not delegate.
Hard Rules
- Generated technical artifacts default to English. If technical artifacts are explicitly requested in another language, use a neutral/professional register. Public/contextual comments follow the target context language. Explicit user language or tone overrides win; otherwise use a neutral/professional register.
- Read
../_shared/research-lifecycle.mdand../_shared/sdd-phase-common.mdfirst. - Admit only
gentle-ai.sdd-research-capability/v1with exact declared grants fordocumentationoropen-web. - Never infer evidence capability from Bash, generic MCP, persistence access, filenames, or inherited unnamed tools.
- Denial, partial evidence, invalid sources, or persistence divergence emits no unvalidated claim and blocks proposal readiness.
- Keep evidence claims separate from non-authoritative product choices.
Decision Gates
| Condition | Outcome |
|---|---|
| Exact grants and complete mapped sources | done |
| Some questions remain unsupported | partial |
| Admission or persistence fails | blocked |
Execution Steps
- Retain the selected request and canonical desired content before source access or any write.
- Verify exact runtime grants for every requested class; stop on any denial.
- Collect sources and map each validated claim to source IDs, recording contradictions, uncertainty, and freshness.
- Persist
gentle-ai.sdd-research/v1and updategentle-ai.sdd-preproposal/v1using the active store contract. - In hybrid mode, write identical bytes to both stores. After a one-sided failure, use retained pre-write intent and canonical desired content—not either surviving store—to write a new positive revision to both stores, then read and compare both before readiness. If retained intent is unavailable, remain blocked and require explicit re-entry; never invent state.
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.
- 9d ago First seen · 57 lines · 28 tokens per session scan A 5990433745f2
sdd-research is a skill published in the GitHub repository Gentleman-Programming/gentle-ai (6,416 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 686 once invoked, about $0.0001 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 skills, from other repositories
ijfw-review
Use when the user asks for a review of any artifact -- code diff, PR, book chapter, campaign brief, landing-page copy, or design tokens. Trigger: review, code review, review this, PR review, review my X, review chapter, review brief, review page, /ijfw-review.
ijfw-cross-audit
Generate a cross-platform multi-model audit (Trident) on a diff, brief, or artifact. Trigger: 'cross audit', 'Trident', 'second opinion', 'check with other models', 'check with other AIs', 'cross-check this', 'get another perspective', /cross-audit.
requirements-authoring-flow
Workflow for authoring requirements and specifications: drafting, review, validation, etc.
ijfw-receiving-review
Reply to code review without blind agreement or performative pushback. Use when you have received feedback, need to address review, respond to review, handle review comments, or PR comments came back. Trigger: received feedback, address review, respond to review, review comments to handle, PR comments came back…
jira
Unified Jira entry point. Routes to preview (default), create, or review subcommands.
new-track
Start a new feature or bug track. Collaborative intake process with structured questions, AI guidance, and progressive refinement before generating spec.md and plan.md. Use when the user asks to 'start a new track', 'create a feature track', 'add a bug fix track', or says 'I want to build X', 'fix the Y bug', 'plan a…