opencaselaw: Skill for Claude Code

.agents/skills/opencaselaw-maintenance/SKILL.md

opencaselaw-maintenance is a skill for Claude Code, Codex from jonashertner/opencaselaw. It costs 62 tokens per session (558 once invoked), scanned A, original, MIT.

A conservative maintenance guide for OpenCaseLaw, a project that collects and serves legal case law. It directs an agent through health checks, scraper and collection-completeness checks, safe fixes, and deployment decisions.

In plain words
What is it for?
It helps assess production health, find missing or incorrect legal records, improve reliability, run offline and full verification checks, and record maintenance outcomes.
Why use it?
It helps maintainers choose one evidence-based action at a time and stops for human review when a change could be risky.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions CLAUDE.md; installed under .agents/ (shared by several agents); mentions Codex.

This is jonashertner/opencaselaw's own configuration. It tells Claude Code and Codex how to work on opencaselaw itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything opencaselaw configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/agent_assess.py --json.

Reuse

Borrowing it

Nothing to install: this file belongs to jonashertner/opencaselaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/jonashertner/opencaselaw/main/.agents/skills/opencaselaw-maintenance/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/jonashertner/opencaselaw

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for opencaselaw-maintenance

README.md
[![agentmods](https://agentmods.dev/badge/skills/jonashertner/opencaselaw/opencaselaw-maintenance/github.svg)](https://agentmods.dev/skills/jonashertner/opencaselaw/opencaselaw-maintenance)
Your own site
<a href="https://agentmods.dev/skills/jonashertner/opencaselaw/opencaselaw-maintenance"><img src="https://agentmods.dev/badge/skills/jonashertner/opencaselaw/opencaselaw-maintenance/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for opencaselaw-maintenance

Your own site · 80×15
<a href="https://agentmods.dev/skills/jonashertner/opencaselaw/opencaselaw-maintenance"><img src="https://agentmods.dev/badge/skills/jonashertner/opencaselaw/opencaselaw-maintenance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 558 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.00558
Opus 5 $0.00031 $0.00279
Sonnet 5 $0.00012 $0.00112
Haiku 4.5 $0.00006 $0.00056

Measured yesterday against content hash 36e7485c6a78, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

opencaselaw-maintenance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/opencaselaw-maintenance/SKILL.md · 61 lines

How it starts

The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OpenCaseLaw Maintenance

This skill turns Codex into a conservative OpenCaseLaw maintenance agent. Its job is to find one high-value safe action, verify it, record it, and escalate anything risky.

Workflow

  1. Bootstrap by reading CLAUDE.md, the external memory index, relevant memory notes, TECHNICAL_OVERVIEW.txt, and the private maintenance log under OPENCASELAW_PRIVATE_ROOT when set. Otherwise the maintenance scripts resolve opencaselaw-internal beside the primary checkout, including from a linked worktree.

  2. Run or inspect a deterministic assessment before deciding:

    python3 scripts/agent_assess.py --json
    

    Use --no-network when tests or sandbox policy require an offline run.

  3. Read ops/autonomy-policy.json before edits. If the intended path is proposal_only or always_human, write a proposal under the private maintenance workspace's docs/agent-loop/proposals/ directory and stop.

  4. Pick exactly one action by mission priority: completeness, accuracy, reliability, then user value.

  5. Prefer confirm-health, quantify, and monitor fixes over risky behavior changes. Never use entscheidsuche as a scraper data path.

  6. For code, add or update offline tests first when practical.

  7. Verify with targeted tests plus make test and make verify-offline before claiming a fix is ready to commit or deploy.

  8. Record the outcome:

    python3 scripts/agent_record.py --action "..." --evidence "..." --outcome "..."
    

Deployment Guard

Before any autonomous deploy candidate, run:

python3 scripts/agent_safe_deploy.py --json

Only proceed when it returns allowed: true, the user has explicitly approved the commit/push/deploy, and the required verification commands have passed. Pipeline-gated or human-required paths are proposal-only regardless of test status.

Output Discipline

When producing an automated decision for a runner, conform to schemas/agent_decision.schema.json. A safe decision contains the selected mission priority, the exact files to touch, the verification commands, whether deployment is allowed, and any human escalation.

Read the full file on GitHub · 61 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday Changed · +2 lines 36e7485c6a78
  2. 10d ago First seen · 59 lines · 62 tokens per session scan A 6be4feef58ad

Subscribe to this mod's changes

opencaselaw-maintenance is a skill published in the GitHub repository jonashertner/opencaselaw (62 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 558 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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