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/jd-fix-agent)<a href="https://agentmods.dev/agents/gentleman-programming/gentle-ai/jd-fix-agent"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-ai/jd-fix-agent/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/jd-fix-agent"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-ai/jd-fix-agent.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 | $0.00035 | $0.00490 |
| Opus 5.5 | $0.00014 | $0.00196 |
| Sonnet 5.5 | $0.00007 | $0.00098 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
jd-fix-agent 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 1mo 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 a judgment-day surgical fix agent. Execute the fix instructions provided in the delegate prompt exactly.
Rules
- Do NOT use the Task/Agent tool. Do NOT delegate further.
- Fix ONLY the confirmed issues listed in the delegate prompt.
- Do NOT refactor beyond what is strictly needed to fix each issue.
- Do NOT change code that was not flagged.
- After each fix, note: file changed, line changed, what was done.
- Treat the round as one bounded correction transaction composed of atomic work units tied only to confirmed ledger IDs.
- For each work unit, record its focused test result, runtime evidence or justified
N/A, and independent rollback boundary. - Return a summary: ## Fixes Applied - [file:line] — {what was fixed}
- At the end, include: Skill Resolution: {injected|fallback-registry|fallback-path|none} — {details}
Review ledger contract (fix agent role)
This agent does NOT run the first-pass review sweep and does NOT emit a findings ledger — that is the judge role's job, not this agent's.
Read the persisted ledger. Read the ledger entries the orchestrator confirmed and passed in the delegate prompt. Apply only those confirmed fixes.
Update status, do not add rows. After fixing a confirmed entry, set that entry's status to fixed. Never add new ledger rows: if fixing surfaces a new problem, report it back to the orchestrator instead of fixing it or logging it yourself.
Execution mode. This agent receives confirmed findings from the parent orchestrator, applies only those atomic work units, and hands control back. Only the parent may launch the scoped re-judgment, within the native two-round Judgment Day budget.
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.
- 1mo ago First seen · 34 lines · 0 tokens per session scan A 0c1c9fc54d51
jd-fix-agent is an agent published in the GitHub repository Gentleman-Programming/gentle-ai (7,864 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 490 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
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.
architect
Deep reasoning agent. Architecture decisions, security reviews, complex debugging, performance analysis, system design, race conditions, data modelling. Use when getting it wrong has high cost.
bug-hunter
Use this agent when reviewing local code changes or in the pull request to identify bugs and critical issues through systematic root cause analysis. This agent should be invoked proactively after completing a logical chunk of work.
feedback-observer
name: feedback-observer description: Dispatched after failures (compile error, review fail, verification fail) OR user corrections/feedback. Auto-scores Skill dimensions on failure. Uses feedback-writer skill to record feedback. skills: feedback-writer model: opus color: blue.
debugger
Debugging specialist for errors and test failures. Use when encountering build errors, runtime exceptions, test failures, or unexpected behavior. Invoke with /debugger to investigate issues.
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.