policy-application-checker

Read policies, application requirements, and forms, then turn them into a completeness checklist, risk list, and submission plan.

3,891 stars

Best use case

policy-application-checker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Read policies, application requirements, and forms, then turn them into a completeness checklist, risk list, and submission plan.

Teams using policy-application-checker should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/policy-application-checker/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/52yuanchangxing/policy-application-checker/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/policy-application-checker/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How policy-application-checker Compares

Feature / Agentpolicy-application-checkerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Read policies, application requirements, and forms, then turn them into a completeness checklist, risk list, and submission plan.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Policy & Application Checker

## Purpose

Read policies, application requirements, and forms, then turn them into a completeness checklist, risk list, and submission plan.

## Trigger phrases

- 检查材料是否齐全
- application checklist
- 读政策做清单
- submission readiness
- requirements checklist

## Ask for these inputs

- policy text or form
- deadline
- applicant profile
- required attachments
- known blockers

## Workflow

1. Extract mandatory requirements, deadlines, and conditional branches.
2. Transform them into a checklist with evidence slots.
3. Flag ambiguous wording and missing proof.
4. Sequence tasks into a submission plan backward from the deadline.
5. Never assume a requirement is satisfied without evidence.

## Output contract

- completeness checklist
- risk list
- submission timeline
- evidence tracker

## Files in this skill

- Script: `{baseDir}/scripts/checklist_builder.py`
- Resource: `{baseDir}/resources/checklist_template.md`

## Operating rules

- Be concrete and action-oriented.
- Prefer preview / draft / simulation mode before destructive changes.
- If information is missing, ask only for the minimum needed to proceed.
- Never fabricate metrics, legal certainty, receipts, credentials, or evidence.
- Keep assumptions explicit.

## Suggested prompts

- 检查材料是否齐全
- application checklist
- 读政策做清单

## Use of script and resources

Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft.
Use the resource file as the default schema, checklist, or preset when the user does not provide one.

## Boundaries

- This skill supports planning, structuring, and first-pass artifacts.
- It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions.


## Compatibility notes

- Directory-based AgentSkills/OpenClaw skill.
- Runtime dependency declared through `metadata.openclaw.requires`.
- Helper script is local and auditable: `scripts/checklist_builder.py`.
- Bundled resource is local and referenced by the instructions: `resources/checklist_template.md`.

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