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/moses607/socialforge/hook-machinenpx skills add moses607/socialforge --skill hook-machinegit clone --depth 1 https://github.com/moses607/socialforgeWhat 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 | $0.00093 | $0.00927 |
| Opus 5 | $0.00046 | $0.00464 |
| Sonnet 5 | $0.00019 | $0.00185 |
| Haiku 4.5 | $0.00009 | $0.00093 |
Grade A, and why
hook-machine 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 yesterday.
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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hook Machine
The hook is 90% of the outcome. On every feed, the first 1-3 seconds (video) or first line (text) decides whether the algorithm keeps showing your content — because it measures whether humans stopped. A great piece with a weak hook dies; a mediocre piece with a great hook travels. So generate many hooks and select ruthlessly; never ship the first one.
The mechanism a hook must trigger
A scroll-stopping hook creates an open loop the brain needs to close. It does one of: promises a specific outcome, breaks a pattern/expectation, names a tension the viewer feels, or makes a claim they must verify. If it does none of these, it's a topic label, not a hook.
Method — generate wide, then cut
- Extract the payload. What's the single most valuable/surprising thing in this content? The hook sells that, not the topic.
- Run the hook archetypes — generate 3-5 hooks per archetype:
- Contrarian — "Everything you've been told about X is wrong."
- Specific result — "How I got 12,000 followers in 9 days with 3 posts."
- Curiosity gap — "The one setting that doubled my reach (nobody talks about it)."
- Callout — "If you post daily and still get 200 views, watch this."
- Stakes/loss — "You're losing followers every time you do this."
- Story cold-open — "I almost deleted this account. Then this happened."
- Question they can't not answer — "Why do some 5-second clips get 2M views?"
- Number/list — "5 hooks that print reach."
- Sharpen with specificity. Replace vague words with numbers, timeframes, names. "Grow fast" → "0 to 10k in 30 days." Specific = believable = clickable.
- Front-load. The stopping word must be in the first 3 words. Cut every warm-up syllable ("So today I want to talk about…" → delete).
- Score & cut. Rate each hook 1-5 on: stops-the-scroll, curiosity, specificity, truthfulness. Ship only 4s and 5s.
Output template
## Hooks for: <content> | Platform: <x> | Audience: <who>
Payload (what the hook sells): <the one valuable thing>
### Top 5 (ranked)
1. "<hook>" — archetype: <..> — why it works: <..>
...
### Bench (10-15 more, by archetype)
<grouped list>
### Paired visual/text-on-screen (first frame)
<3 on-screen text options for the first frame/thumbnail>
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.
- yesterday First seen · 60 lines · 93 tokens per session scan A 96c718b61bf8
hook-machine is a skill published in the GitHub repository moses607/socialforge (2 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 927 once invoked, about $0.0005 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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