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 hermes-ephemeral-delegationgit 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/hermes-ephemeral-delegation)<a href="https://agentmods.dev/skills/gentleman-programming/gentle-ai/hermes-ephemeral-delegation"><img src="https://agentmods.dev/badge/skills/gentleman-programming/gentle-ai/hermes-ephemeral-delegation/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/hermes-ephemeral-delegation"><img src="https://agentmods.dev/badge/skills/gentleman-programming/gentle-ai/hermes-ephemeral-delegation.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 | $0.00042 | $0.00750 |
| Opus 5 | $0.00021 | $0.00375 |
| Sonnet 5 | $0.00008 | $0.00150 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
hermes-ephemeral-delegation 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 11d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activation Contract
Load this skill when you are acting as the parent orchestrator and the work ahead falls into any of these categories:
- Broad exploration (4+ files to understand, codebase mapping, approach comparison)
- Multi-file implementation (touching 2+ non-trivial files)
- Test or build execution
- Fresh adversarial review (diffs, PR readiness, incident audit)
- Multi-step debugging that would flood the parent context
Do NOT load this skill if you are already inside a delegated child task — you are the executor, not the orchestrator.
Hard Rules
- Use
delegate_taskfor all complex work listed above. Do NOT execute it inline. - Workers are EPHEMERAL: each
delegate_taskcall creates a fresh context. Do NOT request persistent agent files or profiles. - Pass a self-contained mission. Workers have no memory of the parent conversation.
- Treat worker output as self-report: verify file writes, test pass/fail, URLs, and IDs before reporting success to the user.
- Batch parallel calls only for INDEPENDENT workstreams. Sequential dependencies must run sequentially.
Decision Gates
| Situation | Action |
|---|---|
| Need to read 4+ files to understand | Delegate a narrow exploration worker |
| Need to write 2+ non-trivial files | Delegate a single writer with the full mission |
| Need to run tests or builds | Delegate an executor; do not run inline |
| Need an adversarial review of a diff | Delegate a fresh-context reviewer |
| Multi-step debug that grows the context | Delegate a debug worker; feed results back inline |
| Simple 1-file edit you already understand | Do it inline; no delegation needed |
| Quick git/state check | Do it inline; no delegation needed |
Execution Steps
- Identify which gate applies. If none applies, skip delegation.
- Draft a self-contained mission for the worker — include:
- Exact goal (one sentence)
- File paths or targets to act on
- Relevant prior context the worker needs (decisions, conventions, prior findings)
- Constraints (style, test runner, budget)
- Expected evidence to return (e.g., file written, test output, URL found)
- Allowed toolsets/MCP/skills the worker should use
- Any
SKILL.mdpaths to load before work
- Call
delegate_taskwith that mission. - Wait for the worker summary.
- Verify the claimed output (check file existence, test result, side effect).
- Synthesize the verified result into your orchestrator reply.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 71 lines · 42 tokens per session scan A 3a2648b219b8
hermes-ephemeral-delegation is a skill published in the GitHub repository Gentleman-Programming/gentle-ai (6,594 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 750 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 skills, from other repositories
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.
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.
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…
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…
jira
Unified Jira entry point. Routes to preview (default), create, or review subcommands.