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 Ootto-AI/claude-content-skills --skill hook-mininggit clone --depth 1 https://github.com/Ootto-AI/claude-content-skillsWrote 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/ootto-ai/claude-content-skills/hook-mining)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/hook-mining"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/hook-mining/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/ootto-ai/claude-content-skills/hook-mining"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/hook-mining.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00111 | $0.01149 |
| Opus 5 | $0.00056 | $0.00575 |
| Sonnet 5 | $0.00022 | $0.00230 |
| Haiku 4.5 | $0.00011 | $0.00115 |
Grade A, and why
hook-mining 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 11d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hook mining
Most AI hook writing is slop for one of two reasons: it copy-pastes a hook that worked for someone else, or it "rewrites" it until the English is flat and the psychology is gone.
This does neither. It keeps the data for what worked — the structure, the psychological mechanism, the rhythm — and swaps only the power words. Proven skeleton stays, skin changes.
1. Get real hooks in
You cannot mine what you do not have. The input is a CSV of hooks that actually performed:
- a Sandcastles (sandcastles.ai) export — it analyses the top videos in your niche and ranks the best hooks; export that tab to CSV
- any social-analytics export with a hook/title/caption column and a views/likes/score column
- competitor hooks pulled with the agent-reach skill, or breakdowns from reel-analyzer
Column names are never predictable, so don't hand-parse.
2. Mine it
python skills/hook-mining/mine_hooks.py hooks.csv [--top 40]
Auto-detects the hook column and the performance column, ranks, buckets by pattern, and flags the
power words already present. Writes hooks.mined.json. Run --selftest to verify the parsing.
Patterns it sorts into: result-proof · time-bound · contrarian · secret-gap · listicle ·
how-to · warning-fear · newsjack · comparison · question · curiosity-tease ·
plain-statement.
3. Remix — the part that matters
For each hook worth reusing, produce 3–5 alternatives under one rule:
Keep the psychology. Swap the power words.
Hold fixed: the pattern, the clause order, the specificity (a number stays a number), the rhythm and syllable shape, the tension and where it resolves, the point of view.
Swap only: the charged words — the verb, the intensifier, the noun carrying the emotion.
power_word_frequency in the mined JSON tells you which ones are earning their keep in your niche.
Pull replacements from there, not from a thesaurus.
proven: "You're using Claude wrong" [contrarian · you're + wrong]
✅ "You're prompting Claude backwards" swapped the verb + the charge word
✅ "You're building agents the slow way" same skeleton, same tension
❌ "Many people use Claude incorrectly" psychology gone, flat English → slop
❌ "You're using Claude wrong" verbatim copy → plagiarism, the platform buries it
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 98 lines · 111 tokens per session scan A f4cd44bccbca
hook-mining is a skill published in the GitHub repository Ootto-AI/claude-content-skills (28 stars, last pushed 18d ago), licensed MIT. It adds 111 tokens to every session and 1,149 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.
Other skills, from other repositories
scrapecreators-api
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio…
outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
ad-library-teardown
Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.
comment-mining
Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.