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 frankxai/claude-skills-library --skill neutrongit clone --depth 1 https://github.com/frankxai/claude-skills-libraryWrote 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/frankxai/claude-skills-library/neutron)<a href="https://agentmods.dev/skills/frankxai/claude-skills-library/neutron"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/neutron/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.
<a href="https://agentmods.dev/skills/frankxai/claude-skills-library/neutron"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/neutron.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00101 | $0.01345 |
| Opus 5.5 | $0.00040 | $0.00538 |
| Sonnet 5.5 | $0.00020 | $0.00269 |
| Haiku 4.5 | $0.00010 | $0.00135 |
Grade A, and why
neutron 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neutron
Cut waste, never reasoning. Neutron decides how a task runs. It does not decide what the answer is, and it never trades correctness for a smaller bill.
Run the five steps in order. Skip a step when it clearly does not apply: a one-line question needs none of them. Total overhead is one short message.
1. Tighten the ask
Only when the request is multi-part, vague or long. Rewrite it as one block with five parts. Do not do the task yet.
- Goal and done-condition. What exists or passes when this is finished.
- Scope. Exact paths, repos, or systems. Name what is out of scope.
- Constraints. Versions, style, things that must not change.
- Output shape. Diff, file, table, one-line verdict. Length cap if one matters.
- Known facts. What the user already established, so it is not re-derived.
Tightening removes repetition and ambiguity. It does not shorten sentences into fragments. Compressing the user's prompt makes answers longer and worse, because the model has to guess what was cut. Keep the user's meaning whole and their words where they carry it.
If the ask is genuinely ambiguous in a way that changes the work, ask one question. Otherwise state the assumption and go.
2. Pick the shape
Tiers are relative: smallest, mid and largest model available to you. Map them to current model names from your provider's list, or from a routing skill if one is installed (for example model-routing). Effort is the reasoning budget, low, medium or high.
| Work | Shape | Tier | Effort |
|---|---|---|---|
| Find, list, locate, count | Search subagent, returns paths and a verdict | Smallest | Low |
| Mechanical edit, rename, format, fixture | Inline, one pass | Small or mid | Low |
| Implement against a clear spec | Inline, test first | Mid | Medium |
| Ambiguous design, security, money, final review | Inline | Largest | High |
| 3 or more independent chunks, each over about 10 tool calls | Parallel subagents, one per chunk | Match each chunk | Match each chunk |
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.
- yesterday First seen · 86 lines · 101 tokens per session scan A ccf838f3242b
neutron is a skill published in the GitHub repository frankxai/claude-skills-library (47 stars, last pushed yesterday), licensed MIT. It adds 101 tokens to every session and 1,345 once invoked, about $0.0004 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 2026-10-15.
Other skills, from other repositories
token-saver
Token optimization guide for Claude Code. This skill should be used when the user wants to reduce API costs, learn model routing strategies, or understand how to use free/cheaper tools (Ollama, Haiku) instead of burning expensive Claude tokens on routine tasks. Triggers on: save tokens, reduce costs, token tips, cheap…
claude-handoff
A handoff tool that prepares a summary of the current conversation for another coding agent and starts that agent in the background.
nav-compact
Clear conversation context while preserving knowledge via context marker. Use when user says "clear context", "start fresh", "done with this task", or when approaching token limits.
python-cli-ops
CLI application patterns for Python. Triggers on: cli, command line, typer, click, argparse, terminal, rich, console, terminal ui.
quick-win-session
Generates guided first-action flows that help users achieve a meaningful result within 60 seconds to boost retention. Use when user wants quick win onboarding, time-to-value optimization, or first success moments.
ship
Commit, push, create PR(s), and auto-finalize — full automation pipeline. Handles uncommitted changes and recently created PRs.