What is reference_parser?

A workflow step that reads a WeChat public-account article, a post published through WeChat, and analyses its writing style and structure. It also identifies information needed before creating a new article.

Use it when a new WeChat article should follow an existing post’s tone, structure, colours, or typography, and when you need to gather details such as dates, locations, or photos.

Writing in a similar style requires understanding the reference article and knowing which facts are missing. This turns the reference into style guidance and a list of questions.

reference_parser — Skill for Claude Code and Codex by OneFav

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 OneFav/wechat-article-formatter-workflow --skill reference_parser

Clone the repo

git clone --depth 1 https://github.com/OneFav/wechat-article-formatter-workflow

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/onefav/wechat-article-formatter-workflow/reference_parser/github.svg)](https://agentmods.dev/skills/onefav/wechat-article-formatter-workflow/reference_parser)

Your own site

<a href="https://agentmods.dev/skills/onefav/wechat-article-formatter-workflow/reference_parser"><img src="https://agentmods.dev/badge/skills/onefav/wechat-article-formatter-workflow/reference_parser/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/onefav/wechat-article-formatter-workflow/reference_parser"><img src="https://agentmods.dev/badge/skills/onefav/wechat-article-formatter-workflow/reference_parser.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 110 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.

Model	Per session	Once invoked
Fable 5.1	$0.00025	$0.00110
Opus 5.5	$0.00010	$0.00044
Sonnet 5.5	$0.00005	$0.00022
Haiku 4.5	$0.00003	$0.00011

Measured 2mo ago against content hash a1ac524c3431, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

reference_parser 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 2mo 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.

skills/reference_parser/SKILL.md · 10 lines

What it actually says