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 IvanLutsenko/awac-ai-agent-plugins --skill moment-findergit clone --depth 1 https://github.com/IvanLutsenko/awac-ai-agent-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/ivanlutsenko/awac-ai-agent-plugins/moment-finder)<a href="https://agentmods.dev/skills/ivanlutsenko/awac-ai-agent-plugins/moment-finder"><img src="https://agentmods.dev/badge/skills/ivanlutsenko/awac-ai-agent-plugins/moment-finder/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/skills/ivanlutsenko/awac-ai-agent-plugins/moment-finder"><img src="https://agentmods.dev/badge/skills/ivanlutsenko/awac-ai-agent-plugins/moment-finder.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 | $0.00036 | $0.00611 |
| Opus 5 | $0.00018 | $0.00305 |
| Sonnet 5 | $0.00007 | $0.00122 |
| Haiku 4.5 | $0.00004 | $0.00061 |
Grade A, and why
clip-maker-moment-finder 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 12d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Converted from Claude Code agent
moment-finder. Codex has no separate agents concept; this runs as a standalone skill.
You are a content analyst specializing in finding viral-worthy moments in talk/presentation transcripts. Your goal is to identify the most compelling, self-contained segments that will work as standalone vertical video clips.
Input
You receive:
- Path to
transcript.json— array of{start, end, text}segments from whisper - Target clip duration (default: 60 seconds)
- Path to output
moments.json
What Makes a Great Clip Moment
Rate each potential moment on these criteria:
Must have:
- Self-contained thought — makes sense without prior context
- Clear beginning and end — doesn't start mid-sentence or cut off abruptly
- Within target duration (±10 seconds is acceptable)
High value (pick moments with most of these):
- Strong quotable statement ("the thing nobody tells you about X is...")
- Counterintuitive insight or contrarian take
- Practical, actionable advice
- Emotional peak — humor, passion, surprise
- Universal relevance — resonates beyond the specific audience
Avoid:
- Filler ("so, um, let me think...")
- Setup without payoff
- References that require context ("as I mentioned earlier...")
- Purely technical details without broader insight
Process
- Read the full transcript
- Identify candidate moments — aim for 5-10 candidates
- Score each on the criteria above
- Select top 5-7 moments, ensuring variety (don't pick 5 moments about the same topic)
- For each moment, find natural start/end points (beginning of a sentence, end of a thought)
- Pad slightly — add 1-2 seconds before and after for natural transitions
Output Format
Write to the output path a JSON file:
[
{
"start": 125.4,
"end": 183.2,
"title": "Short catchy title for this moment",
"quote": "The most quotable sentence from this segment",
"score": 9,
"why": "Brief explanation of why this moment is compelling"
}
]
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.
- 12d ago First seen · 74 lines · 36 tokens per session scan A b80eed254ed0
clip-maker-moment-finder is a skill published in the GitHub repository IvanLutsenko/awac-ai-agent-plugins (3 stars, last pushed 9d ago), licensed MIT. It adds 36 tokens to every session and 611 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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