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 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.

agentmods

npx agentmods add skills/onefav/wechat-article-formatter-workflow/reference_parser

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.

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. Scan, not verified.

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	$0.00025	$0.00110
Opus 5	$0.00013	$0.00055
Sonnet 5	$0.00005	$0.00022
Haiku 4.5	$0.00003	$0.00011

Measured 2d ago against content hash a1ac524c3431, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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

Reference Parser

Use WebFetch to fetch the provided WeChat URL.
Analyze the structure (e.g. is it an event recruitment, summary, or opinion piece?).
Output the required fields (e.g., ["time", "location"] for recruitment, ["photos"] for summary).
Output style guidelines based on the reference (color scheme, typography).

Changes

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.

2d ago First seen · 10 lines · 25 tokens per session scan A a1ac524c3431

Subscribe to this mod's changes

reference_parser is a skill published in the GitHub repository OneFav/wechat-article-formatter-workflow (2 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 110 once invoked, about $0.0001 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 2026-08-31.

Writing and docs

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