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 jev_prosegit 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/jev_prose)<a href="https://agentmods.dev/skills/pchalasani/claude-code-tools/jev_prose"><img src="https://agentmods.dev/badge/skills/pchalasani/claude-code-tools/jev_prose/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/jev_prose"><img src="https://agentmods.dev/badge/skills/pchalasani/claude-code-tools/jev_prose.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 | $0.00047 | $0.00913 |
| Opus 5.5 | $0.00019 | $0.00365 |
| Sonnet 5.5 | $0.00009 | $0.00183 |
| Haiku 4.5 | $0.00005 | $0.00091 |
Grade A, and why
jev-prose 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- jev-prose — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jev prose check and revision
Use the jev-prose CLI to get independent, probabilistic editorial feedback.
It checks patterns, not authorship. The writer makes the editorial decisions.
Setup
Run jev-prose --help to check availability. From a claude-code-tools checkout,
install with uv tool install ./packages/jev-prose. The package carries the
persistent question bank; no global skill source paths are needed at runtime.
If no checkout or installed CLI is available, report that prerequisite rather
than substituting self-review for a Jev result.
Default authentication is CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN,
or an existing sysone account configuration and Wrangler login. The backend is
hosted Jev; the text and optional context are sent there. A configured alternative
can be selected with --backend typesafe or --url and --model. Do not print
secrets. An authentication or billing failure stops the loop.
Bounded loop
For each requested sentence, paragraph, or short section:
- Save the exact draft in a temporary UTF-8 file. Keep the original and track the revision count (initial draft is attempt 0). Preserve the user's intended facts, names, numbers, citations, caveats, voice, and literal quotations.
- Run
jev-prose check draft.txt --profile general > report.json. For papers, specifications, and formal technical prose use--profile formal. Usestrictonly when requested; it adds personal editorial preferences. Optional--audience,--voice, and--context context.txthelp avoid false positives. Keep these settings and the threshold fixed throughout the loop. - Read the report even when the command exits 1: that means findings, not a
failed call. Require
ran: true,ok: true, andanswered == expected. Exit 2, missing output, malformed JSON, or an incomplete check stops the loop as an error; never label the draft clean in those cases. - If
statusisclean, stop. Otherwise inspect the flagged questions and guidance against the actual draft. A high probability is not a command to edit: dismiss an inapplicable flag with a brief reason. Jev provides no evidence spans; locate the passage yourself without pretending it supplied an exact quote. Combine overlapping findings into one targeted revision. - If no useful revision follows, stop and disclose the unresolved or dismissed flags. Otherwise revise only the affected prose, increment the attempt count, and return to step 2. Allow at most three revisions and four checks total. Stop earlier if text repeats or feedback cycles without useful improvement. Always check the last revision; never end on an unchecked 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.
- 6d ago First seen · 75 lines · 47 tokens per session scan A 339ab3a3a882
jev-prose is a skill published in the GitHub repository pchalasani/claude-code-tools (2,016 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 913 once invoked, about $0.0002 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 2026-10-10.
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