AutoClip is an AI video-processing system that downloads videos, finds notable moments, cuts them into clips, and groups the results into collections. It is for people who want to turn YouTube, Bilibili, or uploaded videos into highlight content, and its catalogue add-ons extend the system's workflows.
Borrowing it
Nothing to install: this file belongs to zhouxiaoka/autoclip. 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/zhouxiaoka/autoclip/main/AGENTS.mdgit clone --depth 1 https://github.com/zhouxiaoka/autoclipWrote 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/zhouxiaoka/autoclip/agents-md)<a href="https://agentmods.dev/instructions/zhouxiaoka/autoclip/agents-md"><img src="https://agentmods.dev/badge/instructions/zhouxiaoka/autoclip/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/zhouxiaoka/autoclip/agents-md"><img src="https://agentmods.dev/badge/instructions/zhouxiaoka/autoclip/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 | $0.00699 | $0.00699 |
| Opus 5.5 | $0.00280 | $0.00280 |
| Sonnet 5.5 | $0.00140 | $0.00140 |
| Haiku 4.5 | $0.00070 | $0.00070 |
Grade A, and why
autoclip 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 19d 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
AutoClip quality and release requirements
Read docs/TESTING_ACCEPTANCE.md before changing code or preparing a release. Use RELEASE_CHECKLIST.md for release operations.
- Diagnose the latest report using its version, platform and build. Preserve unrelated work, user data and secrets. Use isolated data directories for every test.
- For a production defect, reproduce the triggering conditions and show the regression fails before the fix and passes afterward. Test boundaries between configuration, requests, persistence, runtime and UI; test count alone is not acceptance.
- Run
python scripts/quality_gate.py --report /absolute/local/path/quality.jsonbefore claiming a candidate passes source checks. Install dependencies first. The report identifies the commit and tracked changes. A targeted run is useful during development; a release requires all groups and the exact tag's CI. - Python correctness lint, frontend lint/types/tests/build, backend tests, version/readme contracts and release-gate regressions are blocking. Existing general Python style warnings remain debt; never waive new runtime errors. Add corresponding checks when changing provider routing, migrations, retries, SQLite, optional runtimes, downloads or telemetry.
- Keep all eight UI locales consistent. Change the event producer, query/measurement contract and validation acceptance together.
- Before creating any release tag, build the untagged candidate branch with Desktop Build, complete every required installed-package case on both platforms and every repair regression, and pass Internal Acceptance. Bind evidence to the exact source SHA and package hashes. The receipt always identifies the current source and packages. After a source/version/package change, rebuild when necessary and re-run affected assertions; previously passed unaffected assertions may be inherited only through the audited impact-inheritance contract in docs/TESTING_ACCEPTANCE.md, preserving their original source, package hashes and execution time. Pending, failed or unknown assertions cannot be inherited. Do not consume a public version for internal repairs. An authorized annotated tag must carry
Internal-Acceptance-Run: RUN_ID; the tag build rejects missing, failed or mismatched acceptance. - Use the final installed package on Windows x64 and macOS arm64 for the required acceptance cases. Record OS, VM/emulation status, portable runtime, package SHA-256, source commit and outcomes. A source checkout, loopback model fixture, wheel test or successful CI does not replace desktop UI/real-model acceptance.
- A candidate with pending/failed/skipped required cases, unresolved blockers, no observation samples or mismatched evidence is NO-GO. Do not bypass this by extending an observation period or merely disclosing the gap. Fix and re-test first.
scripts/release_acceptance.pyand the Release Acceptance workflow must pass before Promote / Halt Release. Never promote directly withgh release edit, change an existing tag, claim a local fix is shipped, or close an issue before release and report-specific verification.- Hotfixes start from the affected tag and contain only necessary repairs and regression tests. Keep unrelated quality cleanup and new features separate; bring the fixes back to main.
- Paid calls, contacting testers, publishing, release promotion and rollback need authorization from the user's request/session. Prepare concrete evidence before asking for any missing authorization. Never upload raw PostHog/Sentry exports, credentials, personal paths or user media to the public repository; only reviewed sanitized acceptance summaries belong there.
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.
- 19d ago Changed · +64 tokens per session 13ecfd8164b8
- 21d ago Changed · +1 lines · +95 tokens per session 858636e580e8
- 22d ago First seen · 15 lines · 540 tokens per session scan A be27bd3103a5
autoclip AGENTS.md is an instructions file published in the GitHub repository zhouxiaoka/autoclip (9,702 stars, last pushed today), licensed MIT. It adds 699 tokens to every session, about $0.0028 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 2026-10-03.
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