checksum-skill

checksum-skill is a skill for Claude Code, Codex from hellogxp/skill-runtime-intelligence. It costs 17 tokens per session (79 once invoked), scanned A, original, Apache-2.0.

A deterministic checker for a bundled experiment fixture, meaning the fixed test data and files supplied with the task.

In plain words
What is it for?
Use it to run the verification script and return the single JSON result it produces without changing files.
Why use it?
It prevents hand-calculated or altered results from being accepted when the required validation must come from the provided script.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to run the verification script and return the single JSON result it produces without changing files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hellogxp/skill-runtime-intelligence/checksum-skill
Install

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.

Any agent
npx skills add hellogxp/skill-runtime-intelligence --skill checksum-skill
Clone the repo
git clone --depth 1 https://github.com/hellogxp/skill-runtime-intelligence

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 checksum-skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/hellogxp/skill-runtime-intelligence/checksum-skill/github.svg)](https://agentmods.dev/skills/hellogxp/skill-runtime-intelligence/checksum-skill)
Your own site
<a href="https://agentmods.dev/skills/hellogxp/skill-runtime-intelligence/checksum-skill"><img src="https://agentmods.dev/badge/skills/hellogxp/skill-runtime-intelligence/checksum-skill/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 checksum-skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/hellogxp/skill-runtime-intelligence/checksum-skill"><img src="https://agentmods.dev/badge/skills/hellogxp/skill-runtime-intelligence/checksum-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 79 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.
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 $0.00017 $0.00079
Opus 5.5 $0.00007 $0.00032
Sonnet 5.5 $0.00003 $0.00016
Haiku 4.5 $0.00002 $0.00008

Measured 1mo ago against content hash 0df4598647f7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

checksum-skill 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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/verify.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

experiments/live_agent/cross_agent_fixture/skills/checksum-skill/SKILL.md · 14 lines

What it actually says

Checksum Skill

For the experiment task:

  1. Run python3 skills/checksum-skill/scripts/verify.py.
  2. Return only the single JSON object printed by the script.
  3. Do not calculate the checksum yourself.
  4. Do not modify any file.
Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 1mo ago First seen · 14 lines · 17 tokens per session scan A 0df4598647f7

Subscribe to this mod's changes

checksum-skill is a skill published in the GitHub repository hellogxp/skill-runtime-intelligence (7 stars, last pushed yesterday), licensed Apache-2.0. It adds 17 tokens to every session and 79 once invoked, about $0.0001 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

otel-telemetrygen

Build safe, version-pinned telemetrygen commands for synthetic OTLP traces, metrics, and logs. Use for “send sample traces to this collector,” “load-test an OTLP endpoint,” “generate traffic to verify a processor, OTTL transform, or tail sampling,” and “produce test data for dashboards.” Also use when choosing…

ollygarden/opentelemetry-agent-skills · 102 tokens

phoenix-observability

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

davila7/claude-code-templates · 47 tokens

langsmith-observability

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

davila7/claude-code-templates · 45 tokens

langsmith-observability

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

synthetic-sciences/openscience · 45 tokens

dd-triage-flaky-test

Load when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.

DataDog/pup · 34 tokens

dd-unblock-pr

Load when investigating a failing PR CI pipeline or checking PR health. Attributes each CI failure as flaky, infra, or regression, proposes a targeted action, and reports code coverage.

DataDog/pup · 40 tokens