pchalasani/claude-code-tools is a collection of command-line tools, skills, agents, hooks, plugins, and commands for improving work with Claude Code, Codex-CLI, and similar coding agents. It is used by developers who want reusable productivity features and workflows for terminal-based AI coding assistants. The catalogue entries are components of, or extensions for, these coding-agent workflows.
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 pchalasani/claude-code-tools --skill upstreamgit clone --depth 1 https://github.com/pchalasani/claude-code-toolsWrote 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/pchalasani/claude-code-tools/upstream)<a href="https://agentmods.dev/skills/pchalasani/claude-code-tools/upstream"><img src="https://agentmods.dev/badge/skills/pchalasani/claude-code-tools/upstream/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/pchalasani/claude-code-tools/upstream"><img src="https://agentmods.dev/badge/skills/pchalasani/claude-code-tools/upstream.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 | $0.00097 | $0.19740 |
| Opus 5.5 | $0.00039 | $0.07896 |
| Sonnet 5.5 | $0.00019 | $0.03948 |
| Haiku 4.5 | $0.00010 | $0.01974 |
Grade A, and why
avoid-ai-writing scanned grade A with 1 finding 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.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Curly quotation marks (“ ” ‘ ’) and apostrophes**: Curly quotes and apostrophes (U+201C/U+201D, U+2018/U+2019) are a *weak* paste-from-chat signal — meaningful mainly in plain-text contexts like code comments, commit This is a copy
86% identical to avoid-ai-writing — 757 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 793 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Avoid AI Writing — Audit & Rewrite
You are editing content to remove AI writing patterns ("AI-isms") that make text sound machine-generated.
What this skill is and isn't
This is a writing-quality tool, not a verdict. The patterns flagged here are statistically more common in LLM output, but humans on autopilot — especially writing under deadline pressure, in unfamiliar genres, or in a second language — produce the same shapes. Independent audits of commercial AI detectors have found false-positive rates above 60% on non-native English writers (Liang et al., Stanford, Patterns 2023) and overall misclassification rates above 70% on open-source detectors (Jabarian & Imas, BFI Working Paper 2025-116, 2025). Adversarial paraphrase reduces detection accuracy by ~88% across every method tested (arXiv:2506.07001, 2025).
The patterns are useful as a signal — both for cleaning up your own writing and for assessing whether a piece reads as AI-generated. Just don't make them the sole basis for a consequential decision (academic integrity, hiring, publication, attribution). Several rules here also fire on second-language writing, deadline-pressed humans, and technical genres that compress vocabulary by design. Pair the signal with context: who wrote it, what genre, what the writer's normal voice looks like, what other evidence you have.
In short: signals, not proof. Worth acting on; not worth ruining someone's day over.
Modes
This skill operates in one of three modes:
rewrite (default) — Flag AI-isms and rewrite the text to fix them.
detect — Flag AI-isms only. No rewriting. Use this mode when:
- The writer wants to see what's flagged and decide what to fix themselves
- The flagged patterns might be intentional (AI patterns aren't always bad — they can be effective in small doses)
- You're auditing text you don't want altered (published content, someone else's writing, reference material)
- You want a quick scan without waiting for a full rewrite
What ships with it
6 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.
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.
- 1mo ago First seen · 793 lines · 97 tokens per session scan A 39154facfc89
avoid-ai-writing is a skill published in the GitHub repository pchalasani/claude-code-tools (2,020 stars, last pushed today), licensed MIT. It adds 97 tokens to every session and 19,740 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to avoid-ai-writing, differing in 757 lines, and is treated as a copy.
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