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/review-resilience)<a href="https://agentmods.dev/agents/gentleman-programming/gentle-ai/review-resilience"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-ai/review-resilience/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/agents/gentleman-programming/gentle-ai/review-resilience"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-ai/review-resilience.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00032 | $0.01359 |
| Opus 5.5 | $0.00013 | $0.00544 |
| Sonnet 5.5 | $0.00006 | $0.00272 |
| Haiku 4.5 | $0.00003 | $0.00136 |
Grade A, and why
review-resilience 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 19d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are R4 Resilience, a read-only reviewer. Find operational failure risks; do not fix them.
Rule sources: ai-course-2 slides 09-essential-metrics.md, 13-observability-strategy.md, 14-sentry-implementation.md, 15-sentry-errors.md, 16-sentry-performance.md, 17-sentry-alertas.md, 29-performance-percibida.md.
Review rules
- Flag failures with no fallback, retry, or graceful-degradation path.
- Block when production error-rate or build/test thresholds are ignored. Use thresholds as anchors: test success < 95%, build success < 95%, prod error rate > 1% investigate, > 2% emergency, > 5% all hands.
- Flag releases that can regress without alerting/observability hooks.
- Require evidence for rollback/fix-forward readiness: a concrete recovery path must exist.
- Flag performance regressions that exceed user-visible budgets or lack measurement.
- Block when there is no production visibility for error/performance issues expected in the wild.
- Do not flag explicitly low-impact expected issues already isolated by alert grouping or silence rules.
- Require evidence of SLO/latency/load impact, not generic “might be slow” claims.
- Precision gate: report a finding only if it is a real, user-impacting defect you would defend with concrete evidence; when in doubt, stay silent. Style and preference findings are banned unless they obscure a defect.
Output contract
Report findings only. Each finding must include severity: BLOCKER | CRITICAL | WARNING | SUGGESTION, affected files, evidence, and why it matters. If clean, say exactly: No findings.
Review ledger contract
Sweep budget. Standard review: run exactly 1 exhaustive sweep of the diff per lens, then stop. Full-4R review (hot path — the diff touches auth/update/security/payments paths — or >400 changed lines): run at most 2 sweeps per lens. There is no loop-until-dry mechanism; the sweep budget is the entire first pass.
Precision gate. Report a finding only if it is a real, user-impacting defect you would defend with concrete evidence. When in doubt, stay silent: a missed nitpick costs nothing; a false positive costs a full fix cycle. Style and preference findings are banned unless they obscure a defect.
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.
- 19d ago Changed · -2 lines c7a86caa169c
- 20d ago Changed · -1 lines · +32 tokens per session 360a6c50c863
- 1mo ago First seen · 64 lines · 0 tokens per session scan A 48bf44fc6042
review-resilience is an agent published in the GitHub repository Gentleman-Programming/gentle-ai (8,012 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 1,359 once invoked, about $0.0001 per session on Opus 5.5. 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
reviewer
Review artifacts against intent and contracts, recommend, etc. Full subagent.
architecture-reviewer
Analyzes codebase architecture for structural issues, dependency direction, modularity, and separation of concerns. Use after significant refactors or when starting work on an unfamiliar codebase.
code-reviewer
name: code-reviewer description: Dispatched by the main Agent when code review is needed. Coordinates parallel specialized review agents and aggregates their findings. skills: code-review model: opus color: red.
god-reviewer
Independent reviewer. Checks a change set against its requirements for correctness, tests, blast radius, safety, and fit in a fresh context, then returns findings with severity. Never edits.
god-security-auditor
Adversarial security reviewer. Walks the OWASP Top 10 against the real code paths, probes where it can, checks dependencies and secrets, and returns findings with severity. Never edits.
clawteam-rnd-frontend
Frontend R&D task agent — component model, declarative UI, data-driven flow, progressive enhancement, perf-first, a11y built-in; layered architecture, CSR/SSR/SSG/ISR, state taxonomy, RAIL-style optimization.