Context Engineering Kit is a collection of skills, agents, hooks, instructions, commands, and plugins that shape how coding agents use context and carry out development work. It is for developers using Claude Code and other supported coding agents who want more predictable results. The catalogue entries are components from this collection.
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 NeoLabHQ/context-engineering-kit --skill thought-based-reasoninggit clone --depth 1 https://github.com/NeoLabHQ/context-engineering-kitWrote 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/neolabhq/context-engineering-kit/thought-based-reasoning)<a href="https://agentmods.dev/skills/neolabhq/context-engineering-kit/thought-based-reasoning"><img src="https://agentmods.dev/badge/skills/neolabhq/context-engineering-kit/thought-based-reasoning/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/neolabhq/context-engineering-kit/thought-based-reasoning"><img src="https://agentmods.dev/badge/skills/neolabhq/context-engineering-kit/thought-based-reasoning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 631 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00085 | $0.05353 |
| Opus 5 | $0.00043 | $0.02677 |
| Sonnet 5 | $0.00017 | $0.01071 |
| Haiku 4.5 | $0.00009 | $0.00535 |
Grade A, and why
thought-based-reasoning 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 5d 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 5d ago First seen · 659 lines · 85 tokens per session scan A 7a4c3581f5ac
thought-based-reasoning is a skill published in the GitHub repository NeoLabHQ/context-engineering-kit (1,673 stars, last pushed 13d ago), licensed GPL-3.0. It adds 85 tokens to every session and 5,353 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
Prompt Refiner
Improves AI prompts to be clearer, more specific, and produce more consistent outputs.
Prompt Injection Defense Auditor
Reviews LLM application prompts and input handling for direct and indirect prompt injection vulnerabilities, then writes defensive scaffolding.
seedance-antislop
Detect and remove hollow AI filler language, empty superlatives, and vague boosters that degrade Seedance 2.0 prompt quality. Use when a prompt feels generic, over-written, or 'AI-sounding', or when generation output looks bland and needs a quality pass.
seedance-camera
Specify camera movement, shot framing, multi-shot sequences, and anti-drift locks for Seedance 2.0. Covers dolly, crane, orbit, push-in, one-take, and storyboard reference methods. Use when writing camera instructions, shooting a scene with a specific angle or movement, or fixing a wandering or locked camera.
seedance-lighting
Specify lighting, atmosphere, and light transitions for Seedance 2.0 prompts using named light sources, core parameters, and atmosphere contracts. Use when the scene needs a specific mood, time of day, or lighting style, or when lighting is flat, inconsistent across shots, or clipping.
llm-redteam-overview
LLM red team category — full AATMF v3 tactic coverage (T01–T15). Routing skill: read this first to identify which tactic applies, then load the matching sub-skill. Maps to MITRE ATLAS where overlap exists.