Borrowing it
Nothing to install: this file belongs to 863401402/she-love-me. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/863401402/she-love-me/main/AGENTS.mdgit clone --depth 1 https://github.com/863401402/she-love-meWrote 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/instructions/863401402/she-love-me/agents-md)<a href="https://agentmods.dev/instructions/863401402/she-love-me/agents-md"><img src="https://agentmods.dev/badge/instructions/863401402/she-love-me/agents-md/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/instructions/863401402/she-love-me/agents-md"><img src="https://agentmods.dev/badge/instructions/863401402/she-love-me/agents-md.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 | $0.00445 | $0.00445 |
| Opus 5 | $0.00222 | $0.00222 |
| Sonnet 5 | $0.00089 | $0.00089 |
| Haiku 4.5 | $0.00044 | $0.00044 |
Grade A, and why
she-love-me AGENTS.md 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 21 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Repository Focus
- This repository packages the
she-love-meworkflow for analyzing WeChat or QQ chat histories and generating an HTML relationship report. - It also supports optional WeChat emoji export:
messages.jsoncan include emoji metadata, andscripts/export_emojis.pycan download/store emoji assets and generatereports/emojis_preview.html. - Current preferred export layout is per-contact bundles under
data/contacts/<联系人>__<hash>/, where chat records and emoji records are separated but linked (messages.json+emojis.json). - The unified skill entrypoint for all tools is
.agents/skills/she-love-me/SKILL.md(Claude Code, OpenClaw, Codex, Cursor, Copilot, Gemini CLI). - Analysis and data-source knowledge is split across
references/under that directory; SKILL.md is the control plane. - For a new Windows WeChat user, agents should execute the full
weflow-clisetup/export path inreferences/data-sources.md, including requesting required install/network/admin approvals. CipherTalk CLI and its official desktop MCP are the automatic fallbacks.
Codex Guidance
- When a user asks to analyze chat logs with this project, prefer the repo skill
she-love-me. - Keep the working directory at the repository root when following the skill workflow.
- Keep generated or sensitive outputs under
vendor/,data/, andreports/; do not move personal chat data into tracked files. - If the user asks about stickers/emojis, use the selected contact bundle's
messages_path, then runscripts/export_emojis.py. - Prefer
--output-dir data/contactsover a shareddata/messages.jsonwhenever exporting a specific contact, so different users do not overwrite each other. - Do not stop at giving setup commands when the user asks for analysis. Run environment checks, install the selected exporter, initialize it, export JSON, convert it, and continue to the report; pause only for login, approval, token, or contact selection.
- If the user wants to invoke the skill explicitly in Codex, they can mention
$she-love-me.
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.
- 9d ago First seen · 21 lines · 445 tokens per session scan A 90a0a36e0fb5
she-love-me AGENTS.md is an instructions file published in the GitHub repository 863401402/she-love-me (778 stars, last pushed 26d ago), licensed MIT. It adds 445 tokens to every session, about $0.0022 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 2026-08-30.
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