Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add live-neon/skills --skill context-verifiergit clone --depth 1 https://github.com/live-neon/skillsWrote 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.
[](https://agentmods.dev/skills/live-neon/skills/context-verifier)<a href="https://agentmods.dev/skills/live-neon/skills/context-verifier"><img src="https://agentmods.dev/badge/skills/live-neon/skills/context-verifier.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.03149 |
| Opus 5 | $0.00011 | $0.01574 |
| Sonnet 5 | $0.00004 | $0.00630 |
| Haiku 4.5 | $0.00002 | $0.00315 |
Grade A, and why
context-verifier 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 7d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
context-verifier (検証)
Unified skill for file hash computation, integrity verification, severity tagging, and context packet creation. Consolidates 3 granular skills into a single verification system.
Trigger: 明示呼出 (explicit invocation)
Source skills: context-packet, file-verifier, severity-tagger
Installation
openclaw install leegitw/context-verifier
Dependencies: None (foundational skill)
Standalone usage: This skill is fully functional standalone. It provides file integrity verification that other skills in the suite depend on. Install this first when adopting the Neon Agentic Suite.
Data handling: This skill performs local-only operations. Hash computation uses standard
SHA256 algorithms locally — no file contents are sent to any model, API, or external service.
Results are written to output/context-packets/ in your workspace. The skill reads config from
.openclaw/context-verifier.yaml or .claude/context-verifier.yaml only.
File access scope: This skill reads user-specified files for hash computation. The metadata
declares config and output paths only — the skill will read ANY file path you provide to
/cv hash, /cv verify, or /cv packet. Use caution with sensitive files.
What This Solves
AI agents sometimes operate on stale data — editing a file that changed since it was read, or trusting cached content that's now outdated. This skill prevents that by:
- Computing hashes of files before and after operations
- Detecting changes between read and write
- Generating context packets with verifiable checksums for review workflows
The insight: Trust but verify. The file you read might not be the file you're about to edit. Check first.
Usage
/cv <sub-command> [arguments]
Sub-Commands
| Command | CJK | Logic | Trigger |
|---|---|---|---|
/cv hash |
哈希 | file→SHA256(content) | Explicit |
/cv verify |
検証 | file×hash→match✓∨mismatch✗ | Explicit |
/cv tag |
標記 | file→severity∈{critical,important,minor} | Explicit |
/cv packet |
包装 | files[]→{path,hash,severity,timestamp}[] | Explicit |
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.
- 7d ago First seen · 422 lines · 22 tokens per session scan A 98816235ee60
context-verifier is a skill published in the GitHub repository live-neon/skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 3,149 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
codegraph
Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection…
stuck
Diagnose frozen, stuck, or slow Qwen Code sessions on this machine. Scans for problematic processes, high CPU/memory usage, hung subprocesses, and debug logs. Use /stuck or /stuck to focus on a specific process.
structured-debugging
Hypothesis-driven debugging methodology for hard bugs. Use this skill whenever you're investigating non-trivial bugs, unexpected behavior, flaky tests, or tracing issues through complex systems. Activate proactively when debugging requires more than a quick glance — especially when the first attempt at a fix didn't…
memory-leak-debug
Diagnose memory leaks in the Qwen Code CLI using heap snapshots and the chrome-devtools CLI. Use when investigating high memory usage, unbounded growth, or suspected object retention issues.
bugfix
Fix a bug from a GitHub issue, following the reproduce-first workflow. Use when the user asks to fix a bug, investigate a GitHub issue, or debug a user-reported problem. Takes a GitHub issue URL or number as input.
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.