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Kai Hook Bench

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cgallic
kai-hook-bench

Generate, rank, and gate a reusable hook bank — sourced pains × named hook formula families, scored on clarity/specificity/curiosity/proof-backing, gated at the 10/16 ad threshold, delivered in 3 lengths with per-hook provenance and downstream routing. Use when "hook bank", "generate hooks", "hook generator", "write hooks for", "ad hooks", "scroll stoppers", "opening lines", "hook ideas", "first 3 seconds", "hook matrix", "I need 50 hooks", or any request to batch-produce attention-openers for ads, social, or video.

Overview

Publishercgallic
Repositorykai-cmo-harness
Skill namekai-hook-bench
Stars
61
Forks
8
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cgallic on GitHub. Read the source before you install it.

Installation

Install the Kai Hook Bench AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-hook-bench
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cgallic/kai-cmo-harness.git /tmp/kai-cmo-harness
mkdir -p .claude/skills
cp -r /tmp/kai-cmo-harness/harness/skills-v2/kai-hook-bench .claude/skills/kai-hook-bench
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kai Hook Bench in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kai Hook Bench on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kai Hook Bench is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Objective

A ranked, provenance-tagged hook bank that downstream skills can pull from without re-checking the claims: sourced pains crossed with named formula families, hooks matching a known loser pattern killed before they reach ranking, survivors scored and gated, and the top set delivered in three lengths with a source behind every claim. This skill produces the hook bank only. /kai-social batches full social posts, /kai-ad-campaign builds full ad campaigns, /kai-write writes single finished pieces, /kai-repurpose fans a pillar into derivatives — they consume this bank. Concept-level portfolio strategy (Persona × Desire × Angle bench, budgets, kill rules) belongs to knowledge/playbooks/combinatorial-creative-bench.md via /kai-ad-campaign; this skill fills the hook field of those bench rows.

Done when

Work type internal-research — floor E2/C2/O0 (harness/eco-floors.yaml). The bank is an internal asset; nothing here posts, uploads, or mutates a live channel, so SHIPPED is terminal and the outcome debt belongs to the consuming skill.

  • E2 — hook-bank.md exists and satisfies the declared structure: ranked, three lengths per hook, provenance line, routing line, plus the ledgers (_sources.md, _data-gaps.md, _proof-inventory.md, rejection-rules.md, rejects.md).
  • C2 — the declared gates pass at their thresholds, stricter than this work type's default: four_us_score at 10/16 and banned_word_check on the bank, plus every candidate run against the rejection rules and voice regexes.
  • O0 — no outcome obligation. Hook performance is graded when a downstream piece hits its 30-day check.

Constraints

  • MARKETING.md first. Read it from the project root before asking discovery questions. If it does not exist, build it from the codebase (CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, email/ad/analytics config) using the template carried in /kai-email-system, and confirm the draft. Do not ask the user what the product is.

  • Five inputs load before anything is generated:

    • Personas — from knowledge/personas/_persona-index.md (or the client's own in MARKETING.md), 1-3 picked via its industry/pain tables. Note each persona's evidence status; hypothesis personas are fine for ideation but get flagged in provenance.
    • Sourced pains — prefer workspace/offer-builder/pain-table.md if it exists (every row already carries a source). Otherwise mine pains with sources: load harness/references/audit-data-provenance.md, then
      bash
      python -m scripts.audit.collect --url <business-url> --mode sales_external --workflow hook-bench --out workspace/hook-bench/data
      plus, where available: Reddit digests (hand off to /kai-reddit-listen if no profile exists in scripts/reddit_monitor/profiles/), user-provided call notes or reviews (cite file path), explicit WebSearch (URL + retrieval date), or python scripts/intel/brand_pulse.py <brand> --domain <domain>. Write workspace/hook-bench/pain-inputs.md with the same columns as the offer-builder pain table.
    • Winners — knowledge/playbooks/what-works.md. Extend proven angles first. If empty, record "no winners yet — all hooks are experiments" in provenance.
    • Rejection rules — memory/what-doesnt-work.md + memory/lessons.md, one row per banned pattern with the memory line that banned it, written to workspace/hook-bench/rejection-rules.md. Seed rules currently in memory: the binary-contrast construction ("It's not X, it's Y" — reads as LinkedIn slop) and study percentages reframed as promises. Also load the voice-pattern regexes carried in /kai-gate.
    • Proof inventory — workspace/hook-bench/_proof-inventory.md with claim, source path/URL, and permission status. Sources in order: workspace/proof-library/ if present (owned by /kai-proof-builder — hand off there rather than rebuilding it), case studies already in workspace/ (new ones via /kai-case-study), data/content_log.json 30-day winners, collector output.
  • No source, no row. Never invent "top pains from Reddit", review counts, or frequency percentages. Unsourceable pains go in workspace/hook-bench/_data-gaps.md. An empty proof inventory is a finding logged there, never a license to fabricate testimonials or numbers. Log every source in workspace/hook-bench/_sources.md (URL/path, method, retrieval date).

  • Reject at birth. Every candidate runs against rejection-rules.md and the kai-gate voice regexes before it enters the matrix. Kills are logged to workspace/hook-bench/rejects.md with the rule that killed them; they never reach ranking. Quantitative claims ("booked 40 calls", "took 6 weeks") come only from _proof-inventory.md or sourced pain data — otherwise rewrite qualitative or drop.

  • A proof-led hook with proof-backing 0 is cut or rewritten into another family; it may not ship as proof-led.

  • Platform ceilings — respect the tightest ceiling across a hook's target platforms:

    SurfaceConstraintSource
    X standard post280 chars totalknowledge/channels/twitter-x.md
    LinkedIn posthook lands in first ~210 chars (mobile "...see more" fold)knowledge/channels/linkedin-organic.md
    Meta adsprimary text 125 chars recommended; headline 40 charsknowledge/playbooks/ad-creative-best-practices.md
    TikTokvisual hook in first 0.7-2s, must work on mute; ad text 100 charsknowledge/channels/tiktok-algorithm.md; knowledge/playbooks/ad-creative-best-practices.md
    Google Search adsheadline 30 charsknowledge/playbooks/ad-creative-best-practices.md
  • Gate the top set after writing it into hook-bank.md:

    bash
    python scripts/quality_gates/four_us_score.py --file workspace/hook-bench/hook-bank.md    # 10/16 — ad/hook threshold
    python scripts/quality_gates/banned_word_check.py --file workspace/hook-bench/hook-bank.md

    Max 2 retry cycles; fix only the named failing dimension or word rather than rewriting the bank (see memory/lessons.md). After 2 failures, escalate to a human with the diagnosis and log it via python scripts/self_improvement/lesson_capture.py add. Scores, retries, and the rubric table go in workspace/hook-bench/_gate-report.md.

  • Approval doctrine. The bank is an internal asset. Nothing here posts, uploads, or mutates a live channel; downstream skills carry their own gates and human approval before anything ships.

Context

NeedLoad
Hook–Retain–Reward, 3-second paid rule, lead-magnet naming formula, Value Equation, content pillars, CLOSER "sell the vacation"knowledge/people/alex-hormozi-knowledge.md ("$100M Leads" and adjacent sections)
3-Second Rule, hook_type taxonomy (problem, proof, mechanism, contrast, offer, story, pattern interrupt), PAS/AIDA/BABknowledge/playbooks/ad-creative-best-practices.md
Angle types and the rule that a hook is an execution variable inside a P.D.A. conceptknowledge/playbooks/combinatorial-creative-bench.md
Persona hook lines and evidence statusknowledge/personas/_persona-index.md
Provenance modes and gap handlingharness/references/audit-data-provenance.md
Ad policy before any hook becomes an ad.claude/rules/architecture-and-memory.md + harness/references/ad-write-guardrails.md (owned by /kai-ad-campaign)

Seven formula families — cite the exact source section per family in formula-library.md, with 2-3 fill-in patterns and the family's known failure mode:

FamilyMechanismGrounding
pain-calloutName the specific sourced pain in the reader's languageHormozi "direct problem identification"; PAS Pain step; persona-index hook lines
identity-calloutName who this is for so the persona is obvious in 3 secondsHormozi paid-ads hook rule ("appeal to identity or location"); bench persona-clarity score
dream-outcomeLead with the vivid destination, status-aware, not the featureValue Equation numerator; CLOSER "sell the vacation"; BAB After step
proof-ledOpen with a real result, demo, or named example"Certainty of outcomes"; hook_type: proof; TikTok proof-first hook (knowledge/channels/tiktok-algorithm.md)
curiosity-gapBold or counterintuitive claim that opens an unresolved questionHormozi content pillar "counterintuitive takes" + Retain mechanics; AIDA Attention
common-mistakeContrast what the ICP does wrong with what worksHormozi content pillar "common mistakes"; hook_type: contrast; bench contrast angle
speed-effortCollapse the Value Equation denominator: outcome + timeframe + low effortLead magnet naming formula; MAGIC Interval; Time Delay / Effort & Sacrifice variables

Known degenerations to name per family: common-mistake collapses into the banned binary-contrast cliché; curiosity-gap into clickbait the body cannot reward; speed-effort into unsubstantiated timeframe claims that fail the compliance pass.

Generation matrix. N hooks per pain × family cell (default N=2; top 5 pains by frequency signal gives up to 70 candidates). Each candidate records hook_id (HB-{pain#}-{family}-{nn}), pain row ref, persona, family, draft text, proof ref (or none), and target platforms. Ranking rubric: 0-2 per dimension (0 = fails, 1 = weak, 2 = strong), ranked by total /8, ties broken by winner-adjacency, each score justified in a phrase citing the pain row or proof ref:

Dimension2 means
ClarityUnderstood in one read, works out of context, no setup needed
SpecificityNamed persona/situation/number — could not be about any product
CuriosityOpens a question the reader needs the next line to close
Proof-backingClaim is backed by an _proof-inventory.md row or sourced pain quote

Bank entry format — the top ~25-30 hooks, ranked, each written as ### #3 · HB-2-proof-led-01 (score 7/8) followed by five lines: short (overlay / one-liner, ≤ 60 chars — fits every ceiling above), mid (caption / primary text, ≤ 125 chars — Meta primary-text ceiling), long (opener, 1-3 sentences, ≤ 210 chars before the fold — LinkedIn/long-caption first lines), provenance (pain # with source URL/path and date · family · proof-inventory row · persona with evidence status), and routing (the consuming skill and placement, e.g. /kai-ad-campaign Meta TOF · /kai-social TikTok overlay). The three lengths carry the same promise — same pain, same claim, no escalation of the claim as length grows.

Consumer routing — append this table to the bank:

ConsumerUseThey own
/kai-socialshort + mid as post/overlay openersfull post body, hashtags, schedule; contract harness/skill-contracts/social-post.yaml
/kai-ad-campaignshort as ad headline seed, mid as primary text seed, hook_id fills the bench row's hook fieldplatform policy check (per-platform table in .claude/rules/architecture-and-memory.md + harness/references/ad-write-guardrails.md), funnel mapping, upload
/kai-writelong as first-line/opener for single pieces (script, email, article)framework, contract, full draft

Output — workspace/hook-bench/: _sources.md, _data-gaps.md, _proof-inventory.md, data/ (collector output, if mined here), pain-inputs.md (only when workspace/offer-builder/pain-table.md is absent), rejection-rules.md, rejects.md, formula-library.md, generation-matrix.md, _gate-report.md, hook-bank.md. Hand-offs — do not re-specify these jobs: pains not yet mined → /kai-offer-builder (its pain table is this skill's preferred input); proof library missing or stale → /kai-proof-builder, customer-story proof → /kai-case-study; ongoing pain listening → /kai-reddit-listen; hooks into posts → /kai-social, into ads → /kai-ad-campaign, into single pieces → /kai-write (brief first via /kai-brief); independent re-gate of the bank → /kai-gate; diagnosing underperforming hooks after 30-day grades → /kai-retro.

Feedback loop. When downstream 30-day checks grade a hook-led piece, winners feed knowledge/playbooks/what-works.md automatically; graded underperformers get their hook family or pattern diagnosed into memory/what-doesnt-work.md via /kai-retro, which tightens rejection-rules.md on the next run.

Escalate when

  • No pain can be sourced — the bank would be invention rather than generation.
  • The proof inventory is empty but the request is specifically for proof-led hooks, or a hook the user wants makes a claim no source supports or a timeframe/results claim that would fail platform policy or advertising law.
  • Two gate retries failed on the same dimension, or every candidate in a family dies to the rejection rules (the angle itself is a known loser).

Frequently asked questions

What does the Kai Hook Bench AI skill do?

Generate, rank, and gate a reusable hook bank — sourced pains × named hook formula families, scored on clarity/specificity/curiosity/proof-backing, gated at the 10/16 ad threshold, delivered in 3 lengths with per-hook provenance and downstream routing. Use when "hook bank", "generate hooks", "hook generator", "write hooks for", "ad hooks", "scroll stoppers", "opening lines", "hook ideas", "first 3 seconds", "hook matrix", "I need 50 hooks", or any request to batch-produce attention-openers for ads, social, or video.

Why use Kai Hook Bench on TypingMind?

Because you install it once and use it with any model. Kai Hook Bench is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kai Hook Bench in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-hook-bench. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kai Hook Bench?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kai Hook Bench?

As many as you like. As long as a model supports skills, you can use Kai Hook Bench with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kai Hook Bench AI skill free?

It is published on GitHub by cgallic. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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