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/.github/copilot-instructions.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/copilot-instructions)<a href="https://agentmods.dev/instructions/863401402/she-love-me/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/863401402/she-love-me/copilot-instructions/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/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/863401402/she-love-me/copilot-instructions.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 | $0.00151 | $0.00151 |
| Opus 5 | $0.00076 | $0.00076 |
| Sonnet 5 | $0.00030 | $0.00030 |
| Haiku 4.5 | $0.00015 | $0.00015 |
Grade A, and why
she-love-me copilot-instructions.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.
What it actually says
she-love-me repository instructions
When the user asks to import or analyze WeChat/QQ chat history, read and follow .agents/skills/she-love-me/SKILL.md from the repository root.
For a new Windows WeChat user, execute the complete exporter workflow in .agents/skills/she-love-me/references/data-sources.md: check/install weflow-cli, initialize it, list sessions, export JSON, convert it, and continue through analysis/report generation. Fall back to CipherTalk CLI or its official desktop MCP automatically. Pause only for required user login, approval, token, or contact selection.
Keep sensitive inputs and generated outputs under vendor/, data/, or reports/. Never copy chat data into tracked files.
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 · 8 lines · 151 tokens per session scan A 75f288471dcb
she-love-me copilot-instructions.md is an instructions file published in the GitHub repository 863401402/she-love-me (778 stars, last pushed 26d ago), licensed MIT. It adds 151 tokens to every session, about $0.0008 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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