What does this skill do, and when should you use it?
This skill defines when and how a parent orchestrator should delegate complex work to delegate_task to avoid context flooding. It provides decision gates that classify tasks (e.g., reading 4+ files, writing 2+ files, running tests) and instructs to delegate rather than execute inline. It sets hard rules: workers are ephemeral, each delegation passes a self-contained mission, and all worker output must be verified. The skill is purely instructional and has no external dependencies beyond the delegate_task tool. It is part of a larger Gentle-AI ecosystem that includes SDD orchestrators and persistent memory.
It establishes an Activation Contract that triggers on broad exploration, multi-file implementation, test/build execution, adversarial review, and multi-step debugging. It provides a decision-gate table that maps these situations to delegation actions. It outlines execution steps: identify the gate, draft a self-contained mission including goal, file paths, context, constraints, expected evidence, allowed toolsets, and required SKILL.md paths; call delegate_task; verify the worker's claimed output; then synthesize verified results into the parent reply. It includes references to tuning parameters and SDD orchestrator usage.
- A developer exploring an unfamiliar codebase needs to understand 4+ files and delegates a narrow exploration worker to keep the parent context lean.
- A developer implementing a change that spans 2+ non-trivial files delegates a single writer with the full mission to avoid context fragmentation.
- A developer needs to run tests or builds and delegates an executor instead of running inline to prevent context bloat.
- A developer wants an adversarial review of a diff or PR and delegates a fresh-context reviewer for an unbiased perspective.
- A developer faces a multi-step debugging session that would flood the parent context and delegates a debug worker, then feeds results back inline.
How do you install this skill?
- Skill may rely on external services or tools; verify availability under mainland China network conditions.
- Lacks user confirmation mechanism and sensitive data handling instructions; ensure data security when used.
- No error handling or failure feedback details; may require extra debugging when issues arise.
Place the internal/assets/skills/hermes-ephemeral-delegation/SKILL.md into your agent's skills directory, or follow the repository-wide installation (e.g., curl -fsSL .../install.sh | bash for Gentle-AI). Specific steps for this skill are not documented; refer to the repository README for the full collection installation.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/Gentleman-Programming/gentle-ai.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/internal/assets/skills/hermes-ephemeral-delegation" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Load this skill when acting as a parent orchestrator and the work matches a decision gate. Check the decision table, then follow the execution steps: draft a self-contained mission including goal, file paths, relevant context, constraints, expected evidence, allowed toolsets, and required SKILL.md paths; call delegate_task with that mission; wait for the worker summary; verify claimed output (e.g., file existence, test results); synthesize the verified results into your reply. Use parallel delegation only for independent workstreams.
What are this skill's strengths and limitations?
- Provides clear decision gates to avoid unnecessary delegation and context overflow.
- Enforces self-contained missions, reducing memory coupling between parent and workers.
- Requires verification of worker self-reports, increasing reliability.
- Supports parallel execution of independent workstreams.
- Depends on the `delegate_task` tool, which may not be available in all agents, limiting portability.
- No test suite provided to verify the skill's instructions are followed.
- References SDD orchestrator protocol, which may be specific to this ecosystem and require additional setup.
- The skill itself does not perform actual tasks; it requires integration with other skills or tools.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Hermes Ephemeral Delegation this page | 49 · Use with care | ★ 7.6k | 3d ago | MIT |
| Multi-Agent Architecture Patterns | 55 · Use with care | ★ 18k | 10d ago | MIT |
| Context Engineering | 49 · Use with care | ★ 103k | 8d ago | MIT |
| Plan Execution Orchestrator | 51 · Use with care | ★ 98k | 3d ago | Apache-2.0 |
| Make Plan | 51 · Use with care | ★ 98k | 3d ago | Apache-2.0 |
The source does not name or clearly imply alternative delegation frameworks, so no comparison is provided.
How did FollowSkills review this skill?
Evidence shows the skill defines explicit delegation rules, requires verification of subagent output, and self-contained missions, reducing over-privilege risk. However, lacks user confirmation mechanism or sensitive data handling instructions, and permission boundaries of delegation tool not fully defined, hence deduction.
Instructions are self-consistent with decision gates and execution steps, but no error handling or failure feedback details, and static assessment cannot support execution verification, so score 8.
Skill has clear trigger conditions and decision gates, but environment fit not verified, especially no mention of mainland China network reachability, potential dependency on external services, hence deduction.
Documentation structured well with metadata, activation contract, hard rules, execution steps, and references, but lacks FAQ, known limitations, and explicit maintenance path, so score 10.
Skill can complete core delegation tasks, but evidence of output format and direct usability insufficient, and static assessment limits, hence score 6.
Only source files, no independent verification or test evidence, so score 3.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →