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/techwolf-ai/ai-first-toolkit/setup-knowledge-basenpx skills add techwolf-ai/ai-first-toolkit --skill setup-knowledge-basegit clone --depth 1 https://github.com/techwolf-ai/ai-first-toolkitWhat 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 | $0.00047 | $0.01201 |
| Opus 5 | $0.00023 | $0.00600 |
| Sonnet 5 | $0.00009 | $0.00240 |
| Haiku 4.5 | $0.00005 | $0.00120 |
Grade A, and why
setup-knowledge-base 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 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.
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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base Setup
Interactive setup that creates a structured, evidence-backed knowledge base in your project.
When to Use
- Starting a new knowledge base from scratch
- Adding a KB to an existing project
Step 1: Discover Purpose
Ask the user (one question at a time, use AskUserQuestion):
- What is this KB for? (e.g., product documentation, security/compliance, company knowledge, sales enablement, internal policies)
- Who will query it? (e.g., you personally, your team, AI agents answering questions)
- What categories make sense? Suggest 3-5 based on the domain, let the user adjust.
Example category suggestions by domain:
- Product docs: technical, integrations, deployment, security, faq
- Company knowledge: general, policies, processes, teams, faq
- Sales enablement: product, competitive, customer-success, pricing, faq
- Security/compliance: security, compliance, technical, general, faq
Step 2: Confirm Plan
Before creating anything, present the plan to the user:
I'll create this structure in your project:
kb/
.kb-config.yaml
index.md
scopes/
_default.yaml
README.md
{category1}/
{category2}/
{category3}/
scripts/
kb-index.py
kb-verify.py
kb-validate.py
kb-search.py
CLAUDE.md (or append to existing)
Ready to proceed?
Wait for user confirmation.
Step 3: Scaffold the KB
Read the template files from this plugin and adapt them for the user's project:
-
Read
templates/kb/.kb-config.yamlfrom this plugin. Createkb/.kb-config.yamlin the user's project, replacing the placeholder categories with the ones chosen in Step 1. Replace{{today}}with today's date. -
Read
templates/kb/index.mdfrom this plugin. Createkb/index.md, adding seed keywords for each chosen category. For example, if the user chose "security" and "technical", add initial keyword entries:| encryption, certificates, access | security/ | | API, architecture, deployment | technical/ |
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 · 132 lines · 47 tokens per session scan A 6b9a15be973e
setup-knowledge-base is a skill published in the GitHub repository techwolf-ai/ai-first-toolkit (96 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 1,201 once invoked, about $0.0002 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
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
brainstorming
Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small.
writing-specs
Use when a workflow step drafts or revises a spec artifact — a goal-and-requirements, an architecture, or a module SPEC — or when a workflow skill names it at such a step. The shared quality bar for specs — not a workflow, nothing to execute.
review
Multi-agent code review for Rust CLI/TUI. UX Review inactive (terminal UI — no web frontend). Code Review only. Includes Rust/clippy-specific checklist.
verify
Self-healing verification loop (test → clippy → fmt).
next
Session start - check progress, suggest next task.