Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/tttg2010/tj-short/skill)<a href="https://agentmods.dev/skills/tttg2010/tj-short/skill"><img src="https://agentmods.dev/badge/skills/tttg2010/tj-short/skill.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 5.1 | $0.00112 | $0.11975 |
| Opus 5 | $0.00056 | $0.05987 |
| Sonnet 5 | $0.00022 | $0.02395 |
| Haiku 4.5 | $0.00011 | $0.01197 |
Grade A, and why
tj-short 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 today.
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 — 863 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TJ Short
TJ Short is a Codex Skill for ecommerce short dramas.
Version: short-drama-ecommerce v0.8.30
v0.8.30 Changelog
- Added
Script Fidelity and Traceability Ruleafter two real production failures: a shot table silently dropped 2 of 4 script dialogue lines with no shot ever picking them up, and a first-frame generation added an unrequested prop (a physical invitation card the script never described) that survived review and was inherited into the final video. - Added dialogue/action ID tagging (
[D-<scene>-<n>],[A-<scene>-<n>]) required in the episode script, and a coverage ledger required at the end of Phase 2 mapping every ID to a shot or a stated deliberate cut. 100% coverage is a hard gate before Phase 3. - Added a
SOURCE ANCHORblock requirement: every Phase 4/5 prompt document must open with the exact tagged script lines the shot derives from, so generation happens against the source, not a paraphrase of a paraphrase. - Added a containment diff check for every image/video generation: prompts must carry MUST HAVE and MUST NOT HAVE lists, and review must answer a mandatory, non-skippable "what did the output add that the prompt never asked for?" field. First-frame drift is a hard gate (cheap retake); video-level drift is a soft gate (record + human decides) unless it violates an explicit MUST NOT HAVE item, which always escalates to blocking.
- Added
保真核对.mdto Required Deliverables: one file per episode carrying the coverage ledger and containment diffs. - Clarified this adds no dedicated LLM calls: coverage is deterministic ID-string matching, and containment diffs are filled during review that already happens when a human looks at generated output.
- Added
US-Market Localization Hard Rule: when target region is 美国 / US / United States, the story, dialogue, characters, scene text, subtitles, CTA, compliance wording, and project manifest must fit the United States market instead of translating Chinese ecommerce short-drama habits into English. - Added current production split: salpx/gpt-image-2 is the default image layer for storyboard grids and first frames; MiniMax H3 is the default video layer for raw clips. salpx video models remain optional alternatives when the user selects them.
- Source: architecture review by 肖老师 (xiao-laoshi-agent) after two script-fidelity failures surfaced in a real project; see
docs/methodology.mdSection 18 for the full diagnosis and design rationale.
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.
- today Changed · +106 lines · +14 tokens per session f168c9c33b43
- 6d ago First seen · 757 lines · 98 tokens per session scan A bfd536125756
tj-short is a skill published in the GitHub repository tttg2010/tj-short (11 stars, last pushed 3d ago), licensed MIT. It adds 112 tokens to every session and 11,975 once invoked, about $0.0006 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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