Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkitnpx agentmods add skills/techwolf-ai/ai-first-toolkit/kb-refreshWrote 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/techwolf-ai/ai-first-toolkit/kb-refresh)<a href="https://agentmods.dev/skills/techwolf-ai/ai-first-toolkit/kb-refresh"><img src="https://agentmods.dev/badge/skills/techwolf-ai/ai-first-toolkit/kb-refresh/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/techwolf-ai/ai-first-toolkit/kb-refresh"><img src="https://agentmods.dev/badge/skills/techwolf-ai/ai-first-toolkit/kb-refresh.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 | $0.00046 | $0.01221 |
| Opus 5.5 | $0.00018 | $0.00488 |
| Sonnet 5.5 | $0.00009 | $0.00244 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
kb-refresh 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 1mo 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KB Refresh
Add content from new sources or update existing KB entries from their original sources.
When to Use
- Adding a new knowledge source (Notion page, Slack channel, etc.) after initial setup
- Re-scraping sources to pick up recent changes
- Importing additional documents into the KB
Prerequisites
Check that kb/ and kb/.kb-config.yaml exist. If not, tell the user to run /setup-knowledge-base first.
Step 1: Understand Current KB
Read the KB config and index to understand what's already there:
kb/.kb-config.yaml
kb/index.md
Run the index script to see the current state:
python3 scripts/kb-index.py
Step 2: Discover Sources
Ask the user (use AskUserQuestion with multiSelect):
"What sources do you want to add or refresh?"
- Notion pages
- Slack channels
- Confluence pages
- Local files or folders
- Other
Collect Entry Points
For each selected source, ask the user for the entry point:
| Source | What to ask | MCP tool |
|---|---|---|
| Notion | Page URL (will scrape the page and all subpages recursively) | notion-fetch with the page URL, then notion-search or notion-get-page-descendants for child pages |
| Slack | Channel name(s) to extract knowledge from | slack_read_channel to read recent messages |
| Confluence | Space key or page URL | getConfluencePage + getConfluencePageDescendants for recursive scraping |
| Local files | Directory path or file paths | Read tool directly |
Step 3: Choose Processing Mode
Ask the user (use AskUserQuestion):
"Process one at a time or all in parallel?"
- One at a time (review each before continuing)
- All in parallel (faster, review at the end)
Step 4: Scrape and Extract
For each source, launch a subagent (or process sequentially, per the user's choice):
You are populating a knowledge base from an external source.
SOURCE: {source_type}: {url_or_path}
KB CATEGORIES (place entries in the most relevant one):
{list of categories from .kb-config.yaml}
EXISTING ENTRIES (avoid duplicating these):
{output from kb-index.py}
INSTRUCTIONS:
1. Read/scrape the source content using the appropriate tool
2. For Notion/Confluence: follow all child pages and subpages recursively
3. For Slack: focus on pinned messages, bookmarks, and high-signal threads (not casual chat)
4. Split the content into distinct topics. Create one .md file per topic, not one giant file.
5. If an existing entry covers the same topic, UPDATE it rather than creating a duplicate.
Read the existing file first, merge the new information, and update last_updated.
5a. For org-context KBs (company/team/personal knowledge, not just policy docs), actively hunt for these content types — they are the most commonly missed:
- **Stakeholders**: one entry per key person (role, ownership areas, how to reach them, what they care about). Without these, the KB can't answer "who should I talk to about X?".
- **Projects**: one entry per initiative (goal, owner, status, links). Distinct from generic "strategy" entries.
- **Repositories / codebases**: one entry per repo (purpose, key files, how to run, ownership).
- **Customer examples**: keep concrete names (e.g., "Acme Corp", "Globex") that make abstract concepts tangible. Don't strip them for anonymity unless the user asks.
6. For new entries, create a file in kb/{category}/ with this format:
---
title: "Topic Title"
description: "Brief one-liner for index lookup"
category: {category}
tags: [{relevant}, {tags}]
sources: ["{source_url_or_path}"]
last_updated: "{today's date}"
---
## Content
Write clear, quotable statements. Each fact should be independently citable.
7. Use lowercase-with-hyphens for filenames: product-overview.md, data-encryption.md
8. Preserve specifics: exact numbers, dates, names, versions
9. No opinions or speculation, only facts from the source
10. Skip content that is outdated, trivial, or not worth preserving
REPORT: When done, list all files created or updated with their category and a one-line description.
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.
- 1mo ago First seen · 126 lines · 46 tokens per session scan A 03acdb19fdb5
kb-refresh is a skill published in the GitHub repository techwolf-ai/ai-first-toolkit (135 stars, last pushed 21d ago), licensed MIT. It adds 46 tokens to every session and 1,221 once invoked, about $0.0002 per session on Opus 5.5. 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
fable-context-thrift
Use at the start of any multi-step task and during exploration — before reading files, searching, or re-checking completed work, especially when tempted to read whole files, re-verify known facts, or run independent lookups one at a time.
memory-management
Framework-agnostic project memory infrastructure for engineering tasks. Use for sparse recall, stale-memory handling, secret-safe durable checkpoints, and MCP/CLI fallback without consuming specialist slots.
backlog-memory
One skill for a backlog project's persistent memory. It does two jobs — learn (load the project's tasks, plans, docs, and memory into the session) and store (synthesize the project's state into persistent memory entries). When invoked without a mode it auto-picks: learn at the start of a fresh session, store after…
consolidate
Audit knowledge directories (.memory/, docs/, staging/, archive/) and Claude Code auto memory for stale, misplaced, or duplicated content. Generates an editable cleanup plan file, then executes approved actions after review. Use when: periodic knowledge cleanup, after project milestones, files feel scattered or…
working
Manage ephemeral .memory/ conversation state across multi-day sessions. Checkpoint todos, decisions, and questions; promote artifacts to permanent knowledge; clean up after promotion. Use when: the user says 'checkpoint', 'save working memory', 'promote working memory', 'clean up memory', wants to persist conversation…
intelligence-learn-from-context
Capture session lessons and apply to intelligence/ after approval.