CD hermes-control
通过 OpenClaw 操控 Hermes Agent 的完整指令参考。包含所有 Hermes CLI 命令、斜杠命令、工具集、配置项、网关操作、多代理协调、定时任务、技能管理等完整操作指南。当用户需要完整操控 Hermes Agent 时使用此技能。关键词：hermes、hermes agent、hermes-agent、hermes 控制、hermes 操控、hermes 自动化。
通过 OpenClaw 操控 Hermes Agent 的完整指令参考。包含所有 Hermes CLI 命令、斜杠命令、工具集、配置项、网关操作、多代理协调、定时任务、技能管理等完整操作指南。当用户需要完整操控 Hermes Agent 时使用此技能。关键词：hermes、hermes…
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
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high Dangerous commands
cmd-pipe-to-shellSKILL.md:35Downloads and executes remote code from an unrecognised host (pipe to shell)curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 5515 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 44/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 5515 tokens
- 100Steps. 50 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 17 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -5TODO / placeholder text left in the skill
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 194: enough signal without eating the budget
- +4Structure: 76 headings
- +3Step-by-step instructions: 50 items
- +4Has examples (47 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.