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 rdd-defect-workflowgit 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/rdd-defect-workflow)<a href="https://agentmods.dev/skills/gentleman-programming/gentle-ai/rdd-defect-workflow"><img src="https://agentmods.dev/badge/skills/gentleman-programming/gentle-ai/rdd-defect-workflow.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.00747 |
| Opus 5 | $0.00020 | $0.00374 |
| Sonnet 5 | $0.00008 | $0.00149 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
rdd-defect-workflow 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gentle-ai-rdd-defect-workflow — 86% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activation Contract
Load when the frontmatter trigger terms apply to a defect workflow.
This skill guides public collaboration. It does not grant issue approval, label, review, exception, or merge authority.
Hard Rules
- Check the user-owned RDD kill switch first. When disabled, do not start receipt reviews or fabricate approval; follow ordinary policy and report
disabled/unmanaged. - Require an approved issue (
status:approved) and clean currentmainreproduction before implementation. Audit existing PRs for supersession or conflict; stop or narrow stale claims. - Group by causal authority invariant. Use one issue and one PR or explicit chain per independent invariant and rollback boundary. Split independent causes; never merge a superseded or conflicting authority line.
- Inventory every operator flow claimed by the issue or PR, including entry, mode, environment, expectation, and negative controls. Require one truthful black-box bench journey per CLI or lifecycle flow, or actual runtime E2E proof when the core bench cannot represent it. Synthetic proxy coverage never proves another runtime.
- Use CodeGraph-first impact mapping, a dedicated worktree, and behavior-first tests. Run source-mutating normalization before candidate freeze.
- Forecast authored changes before edits. The hard limit is 400 additions plus deletions; above it, STOP for a chain or explicit maintainer-approved exception.
- Only when RDD is enabled, bind the review candidate identity, lineage, correction, and recovery records exactly. Keep bounded review defects in one correction transaction; ordinary repository policy decides delivery.
- Require independent read-only candidate validation before publication. Validation cannot edit source or authority; findings require a new candidate.
- Keep communication humane and evidence-based. Repository labels and workflow metadata are maintainer-owned, never evidence of contributor blame.
Decision Gates
| Condition | Action |
|---|---|
| RDD disabled | Ordinary policy; disabled/unmanaged; no receipt or approval claim. |
| Issue gate or reproduction fails | Wait, stop, or narrow with evidence. |
| Invariant or rollback is independent | Separate issue and authoritative PR line. |
| Core bench fits / does not fit | Bench journey / actual runtime E2E; never proxy. |
| Forecast exceeds 400 lines | Chain or approved exception before edits. |
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.
- 8d ago First seen · 55 lines · 41 tokens per session scan A 7dcabfdf0aee
rdd-defect-workflow is a skill published in the GitHub repository Gentleman-Programming/gentle-ai (6,328 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 747 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.
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discover
Primary router for discovery, debugging, investigation, quality, and exploration workflows. Analyzes user intent and dispatches to debug, bughunt, quick-review, deep-review, coverage, testing-strategy, learn, tour, impact, assist-review. The recommended entry point for any 'find out', 'check', 'review', or…
repo-consistency-sweep
Proactive defect-class detection that handles the lower-value half of code review (per Bacchelli and Bird 2013) so human reviewers stay focused on design, intent, and knowledge transfer. Catches convention drift, ordering bugs, type-safety gaps, security and multi-tenant invariants (CWE-grounded), and operability…
post-mortem
Diagnose instruction defects and optionally submit Rosetta GitHub issue.
memorix-troubleshooting
Use when Memorix MCP, setup, project binding, HTTP control plane, hooks, skills, or agent integration is missing, stale, or failing.
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.