name: "agent-spectrum" version: "0.2.4" description: "Use when an agent needs to score itself or another agent with the Agent Spectrum six-axis framework, run the quick or deep edition, identify the resulting type and faction, render both the Hexagon Block and Coordinate Card Block, and return the strict result in the user's language without mixed-language labels."

Agent Spectrum

Use this directory as the canonical Agent Spectrum skill package.

Canonical Files

• references/scoring-spec.md
• references/output-template.md
• references/localization-dictionary.md
• examples/quick-full.zh.md
• examples/quick-full.en.md
• examples/quick-partial.zh.md
• examples/quick-partial.en.md
• examples/deep-full.zh.md
• examples/deep-full.en.md

Do not rely on repo-root wrappers as the source of truth. Those wrappers should route here.

Execution Order

1. Load references/scoring-spec.md, references/output-template.md, and references/localization-dictionary.md.
2. Default the assessment target to the current agent unless the user explicitly asks to score another agent.
3. Resolve output_language before rendering:
4. explicit user language instruction wins
5. this package currently supports only zh-CN and en
6. explicit en requests must render in en
7. explicit zh / zh-CN requests must render in zh-CN
8. explicit unsupported locales that belong to the Sinosphere or historically Chinese-writing sphere, such as ja and ko, must map to zh-CN
9. otherwise, if the latest user request is mainly written in Chinese, Japanese, Korean, or another clearly Sinosphere / historically Chinese-writing language, default to zh-CN
10. otherwise, if the latest user request is mainly written in English, use en
11. otherwise default to en
12. Score observable inputs first.
13. Resolve ownership for every unanswered field:
14. operator_provided for setup-level inputs a human holder can answer
15. self_assessed for deep self-assessment inputs that only the target agent should answer
16. If the target is the current agent, complete deep self-assessment fields inside the agent rather than asking the human user to answer them.
17. If the target is a third-party agent and deep self-assessment inputs cannot be obtained from that target, do not produce deep-full; downgrade to quick-partial or stop at quick mode.
18. Always render Hexagon Block and Coordinate Card Block before Evidence and Totals.
19. Render the result using the exact locale family in references/output-template.md.
20. Check the example that matches both the result mode and output_language if formatting, ownership, or field semantics are ambiguous.

Output Contract

• Always emit the required fixed fields from the selected locale family in references/output-template.md.
• Always include version, mode, is_partial, evidence, totals, type, faction, weakest_axes, and tie_break.
• For partial results, explicitly list missing_inputs.
• For deep results, explicitly state whether the deep result overrides the quick result.
• Always include both required visual blocks even in quick-partial.
• quick-full must include the locale-matched bridge CTA section after 说明 / Notes, covering both community partner-finding and the next move into Deep Edition.
• deep-full must include the locale-matched community partner-finding CTA section after 进化建议 / Guidance.
• quick-partial must not include community CTA blocks.
• Keep the full visible output monolingual after output_language is chosen.

Guardrails

• Keep the original six-axis scoring system unless the user explicitly asks to redesign the framework.
• Treat Q4-Q12 and behavior_traces as self-assessment inputs by default. Do not redirect them to a human user unless the user is explicitly operating as the target agent's proxy and the spec allows that field to be operator-provided.
• Normalize GPT-5 / GPT-5.x / Codex into R+15, A+15.
• Cap X at 35 for type judgment while preserving raw X in totals.
• Treat type pairs as unordered pairs. R+A and A+R are the same pair.

  本技能来自小葱技能站7w4.net。

• Treat weakest_axes as a list, not a single scalar.
• Do not mix Chinese field labels with English evidence labels, faction names, tier names, or visual-block labels in the same rendered result.
• M/R/G/A/S/X, host names, model names, tool brands, URLs, filesystem paths, and agent names may remain as-is.

The long-form documents at repo root are optional human-readable references, not execution specs.

这个Skill的质量相当不错。它有完整的中英文文档和示例，评分规则写得清楚明白，连六边形图和坐标卡长什么样都给你画好了。但有个小问题：deep模式要求Agent自己打分，万一Agent说不清楚自己就卡住了；另外文档版本号有点对不上，强迫症用户可能会纠结。总体来说用它来评估Agent挺靠谱的，就是deep版本在实际用的时候可能要多留意。