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 skills add Pika-Labs/Pika-Plugins --skill baseball-trendgit clone --depth 1 https://github.com/Pika-Labs/Pika-PluginsWrote 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/pika-labs/pika-plugins/baseball-trend)<a href="https://agentmods.dev/skills/pika-labs/pika-plugins/baseball-trend"><img src="https://agentmods.dev/badge/skills/pika-labs/pika-plugins/baseball-trend.svg" alt="Measured on agentmods" height="20"></a>- Socket warn
- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 129 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00145 | $0.05551 |
| Opus 5 | $0.00072 | $0.02776 |
| Sonnet 5 | $0.00029 | $0.01110 |
| Haiku 4.5 | $0.00015 | $0.00555 |
Grade A, and why
baseball-trend 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 8d 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
baseball-trend
15-second ESPN-style broadcast cutaway of a user, sitting behind home plate at a fake Yankees vs Red Sox ALCS Game 3 game at Fenway Park, with two announcers naming them on air.
Fixed-recipe skill — the prompts below are calibrated. Substitute the username and keep the marked anchors intact.
Cost transparency gate
Before any paid MCP call, call identity_balance({verbose: true}) once. Surface the current balance, recent burn rate, and remaining runway, then gate the run with this exact message:
Estimated cost: about 3,000-5,500 credits (~$30-$55) for the GPT-image-2 broadcast still, one or two Kling v3-omni pro 15s renders (includes one Step 2 corrective retry with a changed payload), and post-flight analyze_media QA. This exceeds $5, so Reply
proceedto continue orcancelto stop.
Do not call any paid MCP tool until the user replies proceed. If the user replies cancel, stop without generating. This is the only yes/no gate; after proceed, the pipeline runs end-to-end.
Voice selection note
This skill uses Kling-omni's native broadcast commentary: two male announcers, matching MLB broadcast convention. The identity_voice setting is NOT consumed because this fixed recipe does not use agent-side TTS or custom voice IDs.
If the user wants a female-coded announcer or any custom voice, baseball-trend is the wrong skill. Route them to /pika:podcast with a baseball framing, which has an agent-side voice path and can honor explicit voice choices.
Stage 0 — Intake
If invoked with empty args and no usable prior context, print this menu and stop:
Who should appear in the fake MLB broadcast cutaway? Required:
- Name — exactly as it should appear in the chyron and announcer dialogue
- Reference photo — one front-facing or 3/4 portrait, local path or HTTPS URL
If only one field is missing, ask only for that field. Otherwise ask the two questions below one at a time.
1. Username (required) — used both in the broadcast chyron and in the announcers' commentary, e.g. "Jane Doe". Save as state.username. This replaces every literal ${username} in the prompts below.
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.
- 8d ago First seen · 267 lines · 145 tokens per session scan A 42a8e4b7c550
baseball-trend is a skill published in the GitHub repository Pika-Labs/Pika-Plugins (40 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 145 tokens to every session and 5,551 once invoked, about $0.0007 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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