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.
npx agentmods add skills/asteroidhunter/did-ai-write-this/srcnpx skills add AsteroidHunter/did-ai-write-this --skill srcgit clone --depth 1 https://github.com/AsteroidHunter/did-ai-write-thisWhat 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 | $0.00226 | $0.01316 |
| Opus 5 | $0.00113 | $0.00658 |
| Sonnet 5 | $0.00045 | $0.00263 |
| Haiku 4.5 | $0.00023 | $0.00132 |
Grade A, and why
did-ai-write-this 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.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
- Direct user request — the user pastes text and asks whether it was AI-written, or any phrasing of that question ("is this AI?", "did ChatGPT write this?", "human or AI?", etc.). This is the only auto-trigger case.
- Proactive post-WebFetch check (opt-in only) — skipped by default because every call costs the user a Pangram credit. If the user has added an instruction to their
CLAUDE.mdalong the lines of "when researching, run did-ai-write-this on WebFetch'd pages before citing", honor that. Otherwise wait for an explicit ask — do not call this skill on every WebFetch. - Not for stylistic AI-detection guesses based on writing patterns — those are unreliable and this skill exists precisely to replace them with a calibrated, vendor-backed signal.
How to invoke
The CLI sits next to this SKILL.md and runs inside a self-contained venv populated by install.py. Always invoke through ${CLAUDE_SKILL_DIR} so the path works regardless of where the skill is installed.
Positional argument (short snippet, one shot):
${CLAUDE_SKILL_DIR}/.venv/bin/python ${CLAUDE_SKILL_DIR}/cli.py "the text to check"
From a file (longer documents):
${CLAUDE_SKILL_DIR}/.venv/bin/python ${CLAUDE_SKILL_DIR}/cli.py --file /path/to/document.txt
From stdin (piping output of another command, common for WebFetch content saved to a temp file or var):
cat /tmp/fetched.txt | ${CLAUDE_SKILL_DIR}/.venv/bin/python ${CLAUDE_SKILL_DIR}/cli.py --stdin
Per-paragraph attribution for mixed documents — adds a windows array showing which segments drove the overall label:
${CLAUDE_SKILL_DIR}/.venv/bin/python ${CLAUDE_SKILL_DIR}/cli.py --full --file /path/to/mixed_doc.txt
Force the output format if needed: --json (always JSON) or --pretty (always one-line summary). Default behavior is JSON when stdout is captured (your Bash tool case) and pretty when stdout is a terminal (user case), so the flags are usually unnecessary.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 79 lines · 226 tokens per session scan A 51e2e466ac07
did-ai-write-this is a skill published in the GitHub repository AsteroidHunter/did-ai-write-this (2 stars, last pushed 3mo ago), licensed MIT. It adds 226 tokens to every session and 1,316 once invoked, about $0.0011 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.