← Back to Skills Marketplace
hzz780

Agent Spectrum

by hzz780 · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
141
Downloads
0
Stars
0
Active Installs
1
Versions
Install in OpenClaw
/install agent-spectrum
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 ty...
README (SKILL.md)

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:
    • explicit user language instruction wins
    • this package currently supports only zh-CN and en
    • explicit en requests must render in en
    • explicit zh / zh-CN requests must render in zh-CN
    • explicit unsupported locales that belong to the Sinosphere or historically Chinese-writing sphere, such as ja and ko, must map to zh-CN
    • 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
    • otherwise, if the latest user request is mainly written in English, use en
    • otherwise default to en
  4. Score observable inputs first.
  5. Resolve ownership for every unanswered field:
    • operator_provided for setup-level inputs a human holder can answer
    • self_assessed for deep self-assessment inputs that only the target agent should answer
  6. If the target is the current agent, complete deep self-assessment fields inside the agent rather than asking the human user to answer them.
  7. 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.
  8. Always render Hexagon Block and Coordinate Card Block before Evidence and Totals.
  9. Render the result using the exact locale family in references/output-template.md.
  10. 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.
  • 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.

Usage Guidance
Plain language guidance: - This skill is instruction-only and appears coherent with its stated purpose: it uses only the included local reference files and templates and requests no credentials or installs. - Behavior to note: by default it will score the current agent and will complete deep self-assessment fields internally (self_assessed). If you don't want the agent to autonomously self-score, avoid implicit/autonomous invocation or require explicit confirmation before invoking skills. - The package includes example outputs that contain links to X/Twitter and Telegram. The skill itself does not perform network calls, but if your agent/session grants social-media posting tools or APIs, an agent following the recommendations could post — review available tool permissions before giving the agent posting access. - If you plan to score a third-party agent, be explicit in the prompt; the skill downgrades or refuses deep-full when required self-assessment fields for a third-party cannot be obtained (this is intentional and coherent). - If you want a stricter safety posture: disable implicit invocation for this skill (or globally), or require human confirmation before running deep/full assessments. - Confidence is high; the assessment would change if the package contained install scripts, required credentials, referenced system/global config, or instructed external network calls — any of those would raise concerns.
Capability Analysis
Type: OpenClaw Skill Name: agent-spectrum Version: 0.1.0 The 'agent-spectrum' skill bundle is a framework designed to assess and score AI agents across a six-axis framework (Inscription, Reasoning, Generation, Action, Resonance, Mutation). The bundle contains comprehensive scoring logic in 'references/scoring-spec.md', localization dictionaries for English and Chinese, and output templates for rendering visual 'Hexagon' and 'Coordinate Card' blocks. The instructions in 'SKILL.md' guide the agent to perform self-assessment or evaluate other agents based on observable tools and model types. While it includes links to external community platforms (X and Telegram) associated with the aelf blockchain project, there is no evidence of malicious intent, data exfiltration, or unauthorized execution. The logic is consistent with a gamified capability assessment tool.
Capability Assessment
Purpose & Capability
The package's name/description (six-axis Agent Spectrum scoring, quick/deep editions, localized outputs) matches the included files and runtime instructions. It only references local reference files, templates, and example outputs; it requests no environment variables, binaries, or external configuration that would be unrelated to scoring. Nothing requested appears disproportionate to the stated scoring/visualization purpose.
Instruction Scope
SKILL.md is the execution spec and stays within the scoring domain: load local scoring/spec/template/localization files, determine language, resolve ownership of inputs, compute quick/deep results, and render two visual blocks. Important behavioral notes: it defaults the target to the current agent, and explicitly instructs the agent to complete deep self-assessment fields inside the agent (self_assessed) rather than asking a human. The instructions do not tell the agent to read arbitrary system files or to transmit data to external endpoints; example outputs include links (X and Telegram) but the skill does not itself instruct network calls.
Install Mechanism
No install spec and no code files; the skill is instruction-only and therefore does not write code to disk or pull third-party packages. This is the lowest-risk install profile.
Credentials
The skill requires no environment variables, credentials, or config paths. All data it references comes from local packaged docs and runtime-observed session inputs (model, tool buckets, etc.), which is proportionate to a scoring/templating tool.
Persistence & Privilege
always:false (good). However, the agents/openai.yaml policy field sets allow_implicit_invocation: true, and the skill's runtime rules default the target to the current agent and tell the agent to self-assess deep fields autonomously. Combined, this means the skill can be invoked implicitly and may autonomously score the agent (including completing self_assessed fields) without explicit human answers. That is consistent with the skill's purpose but is a behavioral privilege you should be aware of.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install agent-spectrum
  3. After installation, invoke the skill by name or use /agent-spectrum
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
- Initial release of agent-spectrum skill with comprehensive six-axis agent scoring. - Supports both quick and deep assessment modes, with strict scoring and field ownership rules. - Enforces consistent language output in either English or Simplified Chinese, with special handling for Sinosphere languages. - Always renders both Hexagon Block and Coordinate Card Block before evidence and totals. - Explicitly distinguishes between partial and full results, including required fields and handling of missing inputs. - Implements strong guardrails for scoring, field assignment, localization, and label usage to ensure output accuracy and clarity.
Metadata
Slug agent-spectrum
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Agent Spectrum?

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 ty... It is an AI Agent Skill for Claude Code / OpenClaw, with 141 downloads so far.

How do I install Agent Spectrum?

Run "/install agent-spectrum" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Agent Spectrum free?

Yes, Agent Spectrum is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Agent Spectrum support?

Agent Spectrum is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Agent Spectrum?

It is built and maintained by hzz780 (@hzz780); the current version is v0.1.0.

💬 Comments