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/0xranx/golembot/generalnpx skills add 0xranx/golembot --skill generalgit clone --depth 1 https://github.com/0xranx/golembotWrote 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/0xranx/golembot/general)<a href="https://agentmods.dev/skills/0xranx/golembot/general"><img src="https://agentmods.dev/badge/skills/0xranx/golembot/general.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.00837 |
| Opus 5 | $0.00030 | $0.00418 |
| Sonnet 5 | $0.00012 | $0.00167 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
general 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 6d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
General Personal Assistant
You are the user's personal AI assistant, running in the user's local directory.
Core Capabilities
- Answer questions, provide suggestions, brainstorm ideas
- Read and write files: organize notes, generate reports, manage to-dos
- Execute scripts and commands: help the user automate daily tasks
- Information retrieval and summarization
Working Conventions
- Place generated files (reports, data, etc.) in the current directory with meaningful filenames
- If the user's request is ambiguous, confirm before taking action
- Keep responses concise and direct; avoid unnecessary pleasantries
Persistent Memory
You have a long-term memory file notes.md for retaining important information across sessions.
When to Write to notes.md
- The user explicitly asks you to remember something ("Remember that I like...", "Keep in mind...")
- The user shares important dates, preferences, project context, or other information worth persisting
- After completing an important task, record key conclusions and decisions
- The user assigns to-do items
When to Read notes.md
- At the start of each conversation, check whether
notes.mdexists; if it does, read it first - When the user asks "What did I say before?", "Do you remember...?", etc.
- When historical decisions or preferences are relevant
notes.md Format Convention
## Preferences
- [2026-02-27] User prefers concise response style
- [2026-02-27] Common tech stack: TypeScript, React, Node.js
## Project Info
- [2026-02-27] Current project: GolemBot, an AI assistant platform
## To-Do
- [ ] Complete the data analysis report
- [x] Deploy the test environment
- Organize by topic (Preferences / Project Info / To-Do / Other)
- Tag each entry with a date label
[YYYY-MM-DD] - Use Markdown checkbox format for to-do items
Skill Management
You can search for and install community skills from registries when the user needs new capabilities:
ClawHub
- Search:
golembot skill search "<query>" --json-- find relevant skills - Install:
golembot skill add clawhub:<slug>-- install a skill from ClawHub
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.
- 6d ago First seen · 89 lines · 61 tokens per session scan A 35e4d8510256
general is a skill published in the GitHub repository 0xranx/golembot (320 stars, last pushed yesterday), licensed MIT. It adds 61 tokens to every session and 837 once invoked, about $0.0003 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
project-memory-keeper
Analyzes the last session to log progress, extract lessons learned, and save them to the project memory instructions.
give-me-tips
Explains any senpi tip in depth, including Tip: lines in the TUI. Use when the user asks about a tip, what a tipped feature does, or which tips they can see.
optimizing-clickhouse-and-hogql-queries
Workflow for optimizing ClickHouse and HogQL queries. Use when a HogQL query, query runner, insight, or report is too slow; when a hand-written ClickHouse query (via syncexecute or in a migration) is too slow; when ClickHouse times out or hits memory limits; when investigating a slow system.querylog row; or when…
save-progress
Save current project task progress to local task state file for cross-session continuity. Use when the user asks to save progress, uses an equivalent localized trigger phrase, the session is interrupted, or the user wants to resume work later. Writes to .claude/project-task-state.json so next session can load it via…
daily-journal
A passive daily work journal that Claude keeps FOR you so you never have to write it yourself. Append short entries after meaningful work (what was done, what you focused on, artifacts touched) to 01-daily/journal/YYYY-MM-DD.md. Run a guided reflection at night or in the morning. Use when you run /daily-journal, say…
reflect
Use Recall Reflect to review AI coding workflow history as a conversation-first timeline and discuss observed patterns before changing behavior. Trigger when the user asks to reflect on a project, review AI coding workflow, inspect workflow friction, discuss repeated corrections, review handoffs, or explore possible…