Borrowing it
Nothing to install: this file belongs to hannsxpeter/authenticity-check. 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/hannsxpeter/authenticity-check/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/hannsxpeter/authenticity-checkWrote 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/hannsxpeter/authenticity-check/copilot-instructions)<a href="https://agentmods.dev/instructions/hannsxpeter/authenticity-check/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/hannsxpeter/authenticity-check/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/hannsxpeter/authenticity-check/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/hannsxpeter/authenticity-check/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 | $0.00610 | $0.00610 |
| Opus 5.5 | $0.00244 | $0.00244 |
| Sonnet 5.5 | $0.00122 | $0.00122 |
| Haiku 4.5 | $0.00061 | $0.00061 |
Grade A, and why
authenticity-check 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 1mo 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
authenticity-check (GitHub Copilot instructions)
This repository is the authenticity-check skill: pure-prompt instructions
that score how authentically a piece of text reads as the work of a real
human author and flag the spans that read as AI-generated, AI-templated, or
generically derivative, plus suspicious Unicode provenance carriers in the
supplied text. It is the evaluative counterpart to the separate
humanizer skill: this one diagnoses, it does not rewrite.
When a request is to judge whether text is authentic, whether it reads like an LLM or like a person, how human a passage sounds, which parts sound machine-written, whether a draft still sounds like the author or a named writer, or whether pasted text contains a hidden AI watermark or invisible Unicode (including doc comments, READMEs, and release notes), follow this skill. Apply it even when the words "authenticity check" are not used. Do not follow this skill for transformative requests (humanize, de-slop, de-AI, rewrite, fix, remove marks, make it sound like X); those belong to a separate transformation or provenance hygiene skill.
Read SKILL.md at the repository root and follow it exactly, with
references/tell-patterns.md (32 patterns, six families),
references/do-not-flag.md, references/provenance-signals.md when needed,
and, in voice-deviation mode only,
references/voice-matching.md. Use the method as written: Step 0 baseline
discovery, Step 0a provenance preflight, Step 0b density pre-check, then the
multi-pass diagnostic (catalog
scan, mandatory false-positive audit with veto power, read-only
internal-consistency heuristics, voice deviation if a voice target exists),
and emit the exact output contract: Authenticity report (band plus 0-100
score) / Provenance signals / Flagged spans / Reads as human / Score basis /
Caveats / Next step.
Hard rule: this skill scores and flags only. It never rewrites, edits, removes
or normalizes Unicode, strips metadata, or returns improved prose, not even
one suggested span; the rewrite is the
separate humanizer skill's job, run as a human-judged step, with no target
score carried into it. A combined score-then-rewrite loop is detector-gaming,
which humanizer refuses. This skill is not for defeating AI-detection
systems; reframe such requests toward an honest read of quality and voice.
references/tell-patterns.md, references/do-not-flag.md, and
references/voice-matching.md are synced copies vendored from the canonical
humanizer repo. Do not edit them here; re-sync from humanizer when its
criteria change (see each file's header stamp and the README).
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.
- 1mo ago First seen · 45 lines · 610 tokens per session scan A 3c69ba73b9e0
authenticity-check copilot-instructions.md is an instructions file published in the GitHub repository hannsxpeter/authenticity-check (2 stars, last pushed 19d ago), licensed MIT. It adds 610 tokens to every session, about $0.0024 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 2026-08-31.
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