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skill-creator-canonical技能创建器规范

Agent Skill

skill-creator-canonical 用于辅助安全审计、权限检查和凭据风险排查,适合在 OpenClaw 中需要复核安全边界、认证流程或敏感配置时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

7,393

周安装

305

GitHub Stars

公开资料未说明

下载量

2,416
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-creator-canonical(技能创建器规范)
来源仓库:https://github.com/ciklopentan/skill-creator-canonical
安装命令:
openclaw skills install skill-creator-canonical
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install skill-creator-canonical

简介

skill-creator-canonical 提供标准化技能开发与质量审核流程。

  • 适合在 OpenClaw 中需要确保技能符合安全、权限与架构规范时使用。
  • 内置安全检查清单,覆盖凭据管理、API 边界与依赖验证。
  • 使用前应准备详细的技能描述与预期行为文档。
  • 注意审核结果需人工复核,尤其涉及敏感操作权限时。

SKILL.md

name
skill-creator
description
Create, edit, improve, or audit AgentSkills. Use when creating a new skill from scratch, restructuring an existing skill, auditing skill quality, or making a skill easier for weaker AI models and agent runtimes to follow.

Skill Creator

Use this skill to author or improve real AgentSkills. Keep SKILL.md executable. Move heavy explanation into references/. Move repeated deterministic work into scripts/.

Execution

Definitions

  • <skill-dir> = the directory that contains the target SKILL.md.
  • <output-dir> = the parent directory where a new skill folder should be created.
  • <tool-scripts> = the scripts/ folder inside the skill-creator tool directory.
  • <review-artifact> = the patched SKILL.md text or the short self-check written during Step 6, emitted in the current output channel for the environment.

Step 0 — Choose the path

  1. Classify the request using this table, then set the path.
If the request containsThen path is
create a new skill, new skill called, build a skill namednew-skill
restructure, reorganize, split into references, major rewritemajor-rewrite
audit, review this skill, check skill qualityaudit
otherwisecontinue to step 2
  1. For edits to existing files, set the path to small-local-edit only if all are true:

- only SKILL.md changes - no scripts/, references/, or assets/ files are added or removed - the total change is 15 lines or fewer - no step order or branch condition changes

  1. If all conditions in 0.2 are met, skip to Step 5. Otherwise continue to Step 1.

Stop when one path is chosen.

Step 1 — Understand the real task

  1. Collect the trigger phrases the user would actually say and write them into the task note.
  2. Collect the main success path, add it to the task note, and output that section.
  3. Collect the edge cases or failure modes that matter, add them to the task note, and output that section.
  4. State the before/after behavior in one sentence, add it to the task note, and output that sentence.
  5. Ask exactly one short question only if both are true, then output that question:

- a required input is missing for the next safe step - that input cannot be reasonably defaulted

  1. Otherwise continue and output the task note.

Output: a short task note containing triggers, success path, edge cases, and before/after behavior. Stop when the task note exists.

Step 2 — Design the skill structure

  1. Decide what belongs in SKILL.md.
  2. Decide what belongs in scripts/.
  3. Decide what belongs in references/.
  4. Decide what belongs in assets/.
  5. Put each recurring item in exactly one home, record that mapping, and output the mapping.
  6. If the same code would be rewritten repeatedly, move that code to scripts/ and record the destination.
  7. If the same explanation would be re-written repeatedly, move that explanation to references/ and record the destination.
  8. If the same output starter files would be recreated repeatedly, move those files to assets/ and record the destination.

Output: a short bulleted list mapping each target file or folder to its single purpose. Stop when every recurring item has exactly one home.

Step 3 — Initialize or normalize the skill folder

  1. For a new skill, run python3 <tool-scripts>/init_skill.py <skill-name> --path <output-dir> [--resources scripts,references,assets] [--examples] and create the starting skill folder.
  2. For an existing skill, normalize the folder name, remove placeholder files, and remove stray docs that do not belong in a skill package, then output the normalized folder state.
  3. Keep only files that directly help the skill do its job, then stop when the folder is normalized.

Output: a normalized skill folder. Stop when the skill folder contains only the files the skill actually needs.

Step 4 — Author or revise the skill

  1. Locate the structure plan from Step 2 and use it as the authoring input, then continue authoring.
  2. If the structure plan does not exist or is incomplete, stop and return to Step 2.
  3. Write frontmatter with name and description.
  4. In description, say what the skill does and when to use it.
  5. Write the body in imperative form.
  6. Put the fastest useful action near the top of the file, then output the reordered section.
  7. Use one action per sentence in execution sections, then output the rewritten execution block.
  8. Add explicit branch rules when the next step depends on conditions, then output the patched branch.
  9. Add explicit outputs or stop conditions when order matters, then output the patched step.
  10. Add navigation cues for extra material, then output the revised section list.
  11. Create or update supporting files in scripts/, references/, and assets/ when the plan requires them, then output the drafted files.

Read references/authoring-guide.md when you need authoring principles or folder-design rules. Purpose: guide structure. Read references/progressive-disclosure.md when SKILL.md is getting long or supports multiple variants. Purpose: split content. Read references/workflows.md when the task needs a reusable multi-step pattern. Purpose: choose workflow. Read references/output-patterns.md when the skill must emit a specific artifact or report. Purpose: shape output. Output: the drafted or revised skill files. Stop when the skill files match the structure plan.

Step 5 — Run the weak-model authoring pass

  1. Check every execution block for one action per sentence and report any violation.
  2. Check every execution block for explicit branch rules and patch missing ones.
  3. Check every ordered step for an output, a stop condition, or both, then patch any missing case.
  4. Replace ambiguous pronouns when the reference chain could break, then output the clarified wording.
  5. Replace vague judgment calls with decision rules when possible, then output the revised rule.
  6. Add Read ... when ... Purpose: ... navigation cues for any reference file that a weaker model must load deliberately.
  7. If the draft still needs fallback behavior for ambiguous downstream skills, add or update that fallback behavior explicitly.

Read references/weak-model-fallbacks.md when the skill being authored contains if appropriate, as needed, when necessary, or any step whose outcome depends on model judgment without a clear decision rule. Purpose: replace ambiguity with explicit fallback instructions. Output: the hardened draft plus a 3-line compliance note formatted exactly as:

  • Checked: [2 fragile rules that are now explicit]
  • Navigations: [new Read ... when ... Purpose: ... cues added, or none]
  • Assumption: [one sentence or None]

Stop when the draft and the compliance note exist.

Step 6 — Choose the review path

If all are true, skip deep review and write a 3-line self-check:

  • Step 0 chose small-local-edit
  • only SKILL.md was edited
  • no step order or branch condition was added, removed, or changed

Otherwise run full dual-thinking review. Runtime note: this path requires the dual-thinking skill to be installed and available in the user's environment.

  1. If dual-thinking is unavailable, emit exactly: REVIEW BLOCKED: dual-thinking skill not found. Run: clawhub install dual-thinking. If installation is not possible, ask the user whether to continue without deep review. Then stop the skill execution.
  2. If reviewing, use the real SKILL.md text.
  3. If reviewing, surface the top remaining failure mode and patch it.
  4. If the user declared a larger review round plan before Step 6 began, keep reviewing until that declared plan is complete unless validation blocks further honest review, then output the remaining round target.
  5. Otherwise continue only while a concrete remaining failure mode still exists. Stop once the active consultant and you both agree that further improvement would only add churn, then output the review result.
  6. If skipping, write exactly: what changed, what could break, and why deep review is safe to skip.

Output: <review-artifact>. Stop when <review-artifact> exists.

Step 7 — Validate

  1. Run python3 <tool-scripts>/quick_validate.py <skill-dir>.
  2. Run python3 <tool-scripts>/validate_weak_models.py <skill-dir>.
  3. Fix every validation error, then re-run validation and output the new validator state.
  4. Review warnings and either fix them or record why the warning is acceptable, then output that warning decision.
  5. Re-run both validators after each fix cycle and output the latest results.
  6. If either validator fails twice consecutively, output the raw error log, mark validation as blocked, and stop the entire skill execution. Do not proceed to Step 8. Do not loop indefinitely.

Output: validation results for both scripts. Stop when both validators succeed, the remaining warnings are explicitly accepted, or the 2-cycle retry limit is hit.

Step 8 — Package

  1. Read PACKAGING_CHECKLIST.md when packaging or publishing is in scope. Purpose: verify release gate.
  2. Run python3 <tool-scripts>/package_skill.py <skill-dir>.
  3. Optionally pass an explicit output directory if the task needs one.
  4. Confirm that the .skill archive was created.
  5. Confirm that the package contents and hygiene checks required by PACKAGING_CHECKLIST.md are satisfied, then output that confirmation.

Output: a packaged .skill archive plus the checklist confirmation. Stop when the package file exists and the checklist confirmation exists.

Step 9 — Publish if needed

  1. Publish only if the user asked for distribution; otherwise stop with a no-publish decision.
  2. Publish only after validation, packaging, and the checklist confirmation from Step 8 succeed; otherwise stop before publishing.
  3. Ensure .clawhubignore exists before publishing. If it is missing, create it with this baseline: .clawhub/, __pycache__/, .git/, .env*, *.skill, temp/, node_modules/, coverage/, .diagnostics/, .sessions/, .profile/, .daemon-ws-endpoint. If it already exists, update it to include any missing baseline line. Output the final file.
  4. Determine the release metadata before publishing. Set the publish command inputs for --slug, --name, --version, and --changelog, then output those values.
  5. Run clawhub skill publish <skill-dir> --slug <slug> --name "<name>" --version <semver> --changelog "<1-sentence summary>", then output the exact command and the raw CLI result.

Output: a published release or a deliberate no-publish decision. Stop when publication is complete or intentionally skipped.

Step 10 — Iterate from real usage

  1. Run this step only if the user explicitly provides post-deployment feedback, failure logs, or concrete runtime evidence; otherwise stop immediately.
  2. Decide whether the fix belongs in SKILL.md, scripts/, references/, or assets/, then record that decision.
  3. Apply the fix in the correct home, then output the changed artifact.
  4. Repeat Steps 5 through 9 only for the user-supplied feedback in scope, then output the updated result.

Output: an improved next version of the skill. Stop after applying the user-supplied feedback, or immediately if no feedback was provided.

Quick checks

  • Keep SKILL.md lean enough that navigation is cheap.
  • Prefer references/ for deep explanations, schemas, and long examples.
  • Prefer scripts/ for deterministic repeated operations.
  • Prefer assets/ for files used in the final output.
  • Do not add decorative files like README.md, INSTALLATION_GUIDE.md, or CHANGELOG.md unless the real environment explicitly requires them.

Completion standard

A skill is ready only when all are true:

  • the trigger description is clear
  • the structure matches the real task
  • recurring code and references live in the right place
  • the weak-model pass is complete
  • the required review path is complete
  • validation passes
  • packaging succeeds

If any item above is false, the skill is still in draft.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

70.59%
按下载量换算1,705

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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