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/daffy0208/ai-dev-standards/knowledge-base-managernpx skills add daffy0208/ai-dev-standards --skill knowledge-base-managergit clone --depth 1 https://github.com/daffy0208/ai-dev-standardsWrote 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/daffy0208/ai-dev-standards/knowledge-base-manager)<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/knowledge-base-manager"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/knowledge-base-manager.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 | $0.00047 | $0.04152 |
| Opus 5 | $0.00023 | $0.02076 |
| Sonnet 5 | $0.00009 | $0.00830 |
| Haiku 4.5 | $0.00005 | $0.00415 |
Grade A, and why
Knowledge Base Manager 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 — 733 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base Manager
Build and maintain high-quality knowledge bases for AI systems and human consumption.
Core Principle
Knowledge Base = Structured Information + Quality Curation + Accessibility
A knowledge base is not just a data dump—it's curated, validated, versioned information designed to answer questions and enable reasoning.
When to Use Knowledge Bases
Use Knowledge Bases When:
- ✅ Need to answer factual questions consistently
- ✅ Information changes frequently and needs version control
- ✅ Multiple sources need to be unified and reconciled
- ✅ Provenance and citation tracking is critical
- ✅ Building AI systems that need grounded, verifiable information
- ✅ Organizational knowledge needs to be preserved and searchable
- ✅ Complex domain with interconnected concepts
Don't Use Knowledge Bases When:
- ❌ Static documentation is sufficient (use docs + search)
- ❌ No one will maintain/update it (knowledge rot guaranteed)
- ❌ Simple FAQ covers all questions (<50 items)
- ❌ Information doesn't change (static site faster/cheaper)
- ❌ Team lacks resources for curation
Knowledge Base Types: Decision Framework
1. Document-Based Knowledge Base (RAG)
What it is: Collection of documents, chunked and embedded for semantic search
Best for:
- Technical documentation
- Support articles, FAQs
- Policy documents
- Research papers
- Blog content
- User manuals
Strengths:
- Easy to add new documents
- Preserves full context
- Natural for text-heavy content
Weaknesses:
- Hard to query relationships ("Who works where?")
- Duplicate information across documents
- Difficult to keep facts consistent
Use: rag-implementer skill + vector-database-mcp
2. Entity-Based Knowledge Base (Knowledge Graph)
What it is: Network of entities (people, places, things) connected by relationships
Best for:
- Organizational charts
- Product catalogs with relationships
- Social networks
- Recommendation systems
- Fraud detection
- Supply chain tracking
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 733 lines · 47 tokens per session scan A ad5199a931a5
Knowledge Base Manager is a skill published in the GitHub repository daffy0208/ai-dev-standards (36 stars, last pushed 8mo ago), licensed MIT. It adds 47 tokens to every session and 4,152 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.
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