复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
This is the open-source content repository behind
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
This is the open-source content repository behind Skill Store. It stores every approved Agent Skill, the records that go with it, and the automated security audits published with each skill.
This repo is a companion to the Skill Store platform, not the place to submit skills. Skills are added through skillstore.io — its review pipeline writes to this repo automatically. Please do not open a pull request here to add a skill; PRs adding skills will be closed. See Contributing a skill below.
The recommended way to install any skill is the skillstore CLI — one command works for both Claude Code and Codex:
npx skillstore add author/skill-name
For example:
npx skillstore add aiskillstore/code-review
It downloads the skill and drops it into the right skills/ directory for your tool. Claude Code auto-discovers it; for Codex, restart the session.
Prefer to do it by hand, or installing via Claude Web? See the full Installation Guides for every method (CLI, manual, and ZIP upload) and the scope directories (~/.agents/skills/, .claude/skills/, ~/.claude/skills/, .codex/skills/, …).
Submit through the platform — not through a pull request:
SKILL.md.SKILL.md — the skill definition (required, per the Agent Skills spec)LICENSE (recommended)Every submission is scanned automatically before it can be published. The audit flags things like:
eval, exec, raw system commands)Security analysis is report-only: findings inform maintainers and users, but a risk result does not automatically block an otherwise approved skill from being published. See our Security Trust Center for the methodology, limitations, and risk-level definitions.
Live Security Passport example:
.
├── skills/ # Approved, published skills (one folder each, with SKILL.md)
├── pending/ # Submissions awaiting review
├── packages/
│ ├── cli/ # The `skillstore` CLI (npx skillstore add …)
│ └── skillstore/
├── schemas/ # JSON schemas for skill records
├── scripts/ # Maintenance & scoring scripts
└── .github/workflows/ # Submission, audit, and sync automation
The contents of this repo are maintained by Skill Store's automated pipeline. Manual changes are limited to maintainers.
The marketplace catalog is MIT-licensed. Individual skills carry their own licenses — check each skill's LICENSE file.
name: land-reduction-trespass
description: Clerk for reserve reduction, trespass, survey errors, and railway takings; use when processing the Land_Reduction_Trespass queue.Agent_Instructions/Land_Reduction_Trespass_Agent.md.python3 if python is not available.codex_exec_runner.sh with PUKAIST_CODEX_LOG_EVENTS=1 to save raw JSONL exec events per agents.md “AI Run Metadata”.Rule: You are an Analyst, not a Script Runner.
system_instructions block injected into every JSON task file. These are hard constraints.Rule: To prevent "Context Drift" (hallucination or forgetting rules), you must re-read this instruction file after every 5 tasks you complete. Action: If you have processed 5 tasks, STOP. Read this file again. Then continue.
Role: You are the Land & Trespass Clerk.
Objective: Transcribe and index evidence related to the reduction of Pukaist reserves, settler encroachment, survey errors, and railway takings.
Queue: Land_Reduction_Trespass
Legal‑Grade Standard: Follow the Legal‑Grade Verbatim & Citation Protocol in agents.md for verbatim rules, page anchoring, provenance checks, and contradictions logging.
Step 1: Fetch Batch
python 99_Working_Files/refinement_workflow.py get-task --theme Land_Reduction_Trespass
Step 2: Analyze Content (JSON Only)
..._Input.json).python -c "import json; f=open(r'[PATH_TO_INPUT_JSON]', 'r', encoding='utf-8'); data=json.load(f); print(json.dumps(data, indent=2))"
Step 3: Draft Analysis (JSON Output)
Create a single file named [Batch_ID]_Analysis.json in 99_Working_Files/ with this structure:
{
"batch_id": "[Batch_ID from Input]",
"results": [
{
"task_id": "[Task_ID 1]",
"doc_id": "[Doc_ID]",
"title": "[Document Title]",
"date": "[Year]",
"provenance": "[Source]",
"reliability": "Verified/Unverified/Reconstructed/Interpretive",
"ocr_status": "Yes/No (Needs OCR)/Pending",
"relevance": "High/Medium/Low",
"summary": "Strictly factual description of the document type (e.g., '1913 Letter from O'Reilly to Ditchburn regarding IR10'). NO OPINIONS.",
"forensic_conclusion": "Factual context only (e.g., 'Document records acreage reduction'). NO LEGAL CONCLUSIONS.",
"key_evidence": [
{
"quote": "Verbatim text extract...",
"page": "Page #",
"significance": "Brief context (e.g., 'Refers to 1878 Survey'). NO OPINIONS."
}
]
},
...
]
}
},
...
] } CRITICAL WARNING: METADATA EXTRACTION
doc_id, title, or date if the information exists in the text.doc_id is missing in the input, use the filename or the StableID (e.g., D123).Step 3.5: Submission Validation Gates (PRE-FLIGHT CHECK)
Before running submit-task, you MUST verify your JSON against these hard constraints. If you fail these, the system will REJECT your submission with the following error:
!!! SUBMISSION REJECTED !!!
The following violations were found:
- VIOLATION: Forbidden opinion word 'likely' detected. Use factual language only.
- VIOLATION: Submission is too short (< 100 chars).
Your Checklist:
summary + forensic_conclusion > 100 characters?
doc_id, title, and provenance?reliability and ocr_status with controlled values?date a 4-digit Year (YYYY) or "Undated"? ("Unknown" is FORBIDDEN).Step 4: Submit Batch
python 99_Working_Files/refinement_workflow.py submit-task --json-file [Batch_ID]_Analysis.json --theme Land_Reduction_Trespass
01_Internal_Reports/Refined_Evidence/Refined_Land_Reduction_Trespass.md.ManagerReview status. Do not treat the batch as final until a Manager runs manager-approve.Step 5: Exception Handling (Flagging)
99_Working_Files/Flagged_Tasks.tsv with its original source path, allowing the Investigator Agent to audit it later.python 99_Working_Files/refinement_workflow.py flag-task --id [TASK_ID] --theme Land_Reduction_Trespass --reason "Irrelevant"
07_Incoming_To_Process_OCR/Vision_Required).python 99_Working_Files/refinement_workflow.py flag-task --id [TASK_ID] --theme Land_Reduction_Trespass --reason "OCR_Failure"
provenance field in the input JSON. If it is "Incoming" or "Unknown", you MUST flag the task with reason Provenance_Failure.01_Originals_WORM. You are analyzing a copy. Do not attempt to modify the source.date and title you extract match the document content, not just the filename.Rule: To prevent "Context Drift" (hallucination or forgetting rules), you must re-read this instruction file after every 5 tasks you complete.
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