Content
88%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A highly actionable, well-sequenced deployment runbook: executable commands at every step, explicit validation checkpoints, and concrete error-recovery guidance. Its only weaknesses are mild repetition of port/S3 details across sections and a monolithic single-file structure where the env-var reference and troubleshooting material could live in separate reference files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient and assumes competence — concrete commands and env-var overrides with no filler explanations — but port and S3-linkage details are repeated across the 工作边界, 部署流程, and 最终回复 sections and could be tightened. | 4 / 5 |
Actionability | Every step ships copy-paste-ready bash (docker -v checks, curl install, FASTGPT_NON_INTERACTIVE=true bash install.sh, docker compose up -d, curl -I verification) plus concrete env-var override examples and specific troubleshooting fixes such as switching the Mongo image to 4.x for AVX issues. | 5 / 5 |
Workflow Clarity | A clearly sequenced 6-step flow with explicit validation checkpoints (docker compose ps, logs --tail=120, curl -I), a non-empty-directory guard before deployment, explicit warnings against deleting data volumes, and an error-recovery troubleshooting section covering port conflicts, S3 endpoint, Mongo Illegal instruction, and config changes. | 5 / 5 |
Progressive Disclosure | Sections (目标, 工作边界, 部署流程, 常见问题处理, 最终回复) are well organized with no nested references, and no bundle files exist so nothing is mis-filed. However, at ~125 lines the env-var reference and troubleshooting detail are inlined in SKILL.md rather than split into reference files, so it falls short of the well-signaled one-level-deep ideal. | 4 / 5 |
Total | 18 / 20 Passed |