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/agentproto/ts/tiktoknpx skills add agentproto/ts --skill tiktokgit clone --depth 1 https://github.com/agentproto/tsWrote 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/skills/agentproto/ts/tiktok)<a href="https://agentmods.dev/skills/agentproto/ts/tiktok"><img src="https://agentmods.dev/badge/skills/agentproto/ts/tiktok.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.00890 |
| Opus 5 | $0.00021 | $0.00445 |
| Sonnet 5 | $0.00008 | $0.00178 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
tiktok 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 3d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TikTok — recon plan (hardest: anti-bot signing)
Internal API: https://www.tiktok.com/api/<endpoint>/ with a large param
set plus anti-bot signing: msToken (cookie + param), X-Bogus /
_signature / X-Gnarly — generated by TikTok's obfuscated webmssdk JS.
Hand-crafting signed requests from scratch is impractical; use the page's own
signer or its own fetches.
The winning move: stay in-page, reuse TikTok's signer
Because we run inside the loaded tiktok.com page, the signing code is already present. Two viable strategies (capture which works):
- Reuse the page's fetch path — trigger the action via the UI (scroll feed,
open a video, open comments) and read the resulting
/api/JSON responses fromlist_network_requests/ a fetch-hook. Lowest-friction for READS. - Call the in-page signer — TikTok exposes signing via globals
(
window.byted_acrawler/ the SDK's sign fn) used to buildX-Bogus. If reachable, sign your own request params in-page, then fetch. Capture the exact global + signature param names live (they change).
Auth: cookies (sessionid, tt_csrf_token, msToken) ride same-origin; writes
need sessionid + valid signature.
Endpoints (capture live; param sets are huge + signed)
| Purpose | endpoint |
|---|---|
| For-You feed | /api/recommend/item_list/ |
| A user's videos | /api/post/item_list/?secUid=<…> |
| User detail | /api/user/detail/?uniqueId=<handle> |
| Comments (who/likes) | /api/comment/list/?aweme_id=<id> |
| Video detail | /api/item/detail/?itemId=<id> |
| Search | /api/search/general/full/?keyword=<q> |
| WRITE: like (digg) | /api/commit/item/digg/ |
| WRITE: follow | /api/commit/follow/user/ |
| WRITE: comment | /api/comment/publish/ |
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.
- 3d ago First seen · 77 lines · 42 tokens per session scan A 28bc9ed7f408
tiktok is a skill published in the GitHub repository agentproto/ts (5 stars, last pushed 3d ago), licensed Apache-2.0. It adds 42 tokens to every session and 890 once invoked, about $0.0002 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.
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