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 commands/opensesh/karimo-overview/hookifygit clone --depth 1 https://github.com/opensesh/karimo-overviewWhat 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 | $0.00012 | $0.01884 |
| Opus 5 | $0.00006 | $0.00942 |
| Sonnet 5 | $0.00002 | $0.00377 |
| Haiku 4.5 | $0.00001 | $0.00188 |
Grade C, and why
hookify scanned grade C with 1 finding 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 2d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- Label: Short description (e.g., "Block rm -rf") How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hookify - Create Hooks from Unwanted Behaviors
FIRST: Load the hookify:writing-rules skill using the Skill tool to understand rule file format and syntax.
Create hook rules to prevent problematic behaviors by analyzing the conversation or from explicit user instructions.
Your Task
You will help the user create hookify rules to prevent unwanted behaviors. Follow these steps:
Step 1: Gather Behavior Information
If $ARGUMENTS is provided:
- User has given specific instructions:
$ARGUMENTS - Still analyze recent conversation (last 10-15 user messages) for additional context
- Look for examples of the behavior happening
If $ARGUMENTS is empty:
- Launch the conversation-analyzer agent to find problematic behaviors
- Agent will scan user prompts for frustration signals
- Agent will return structured findings
To analyze conversation: Use the Task tool to launch conversation-analyzer agent:
{
"subagent_type": "general-purpose",
"description": "Analyze conversation for unwanted behaviors",
"prompt": "You are analyzing a Claude Code conversation to find behaviors the user wants to prevent.
Read user messages in the current conversation and identify:
1. Explicit requests to avoid something (\"don't do X\", \"stop doing Y\")
2. Corrections or reversions (user fixing Claude's actions)
3. Frustrated reactions (\"why did you do X?\", \"I didn't ask for that\")
4. Repeated issues (same problem multiple times)
For each issue found, extract:
- What tool was used (Bash, Edit, Write, etc.)
- Specific pattern or command
- Why it was problematic
- User's stated reason
Return findings as a structured list with:
- category: Type of issue
- tool: Which tool was involved
- pattern: Regex or literal pattern to match
- context: What happened
- severity: high/medium/low
Focus on the most recent issues (last 20-30 messages). Don't go back further unless explicitly asked."
}
Step 2: Present Findings to User
After gathering behaviors (from arguments or agent), present to user using AskUserQuestion:
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.
- 2d ago First seen · 232 lines · 12 tokens per session scan C 7560e58edc6c
hookify is a command published in the GitHub repository opensesh/karimo-overview (11 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 1,884 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
doctor
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dashboard
Phase 3 monitoring — System health, execution insights, and velocity analytics.
run
Execute an approved PRD using feature branch workflow (v7.0). This command generates briefs, auto-reviews them, allows user iteration, and then orchestrates execution.
merge
Consolidate feature branch changes and create final PR to main. This completes the v5.0 feature branch workflow after all task PRs have been merged.
greptile-review
Execute the full Greptile review cycle on a PR, looping until score meets threshold or circuit breaker triggers.
feedback
Intelligent feedback capture with automatic complexity detection and adaptive investigation.