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/davidmatousek/agentic-oriented-development-kit/kb-querynpx skills add davidmatousek/agentic-oriented-development-kit --skill kb-querygit clone --depth 1 https://github.com/davidmatousek/agentic-oriented-development-kitWhat 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 | $0.00070 | $0.02480 |
| Opus 5 | $0.00035 | $0.01240 |
| Sonnet 5 | $0.00014 | $0.00496 |
| Haiku 4.5 | $0.00007 | $0.00248 |
Grade A, and why
kb-query 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.
This is a copy
100% identical to kb-query — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KB Query Skill
Purpose
Interactive Knowledge Base search with natural language queries, providing fast access to institutional knowledge through relevance-ranked results with quality scoring.
How It Works
Step 1: Accept Query
User provides search query or problem description:
User: "Search KB for database connection pool issues"
Step 2: Execute Search
Run KB search with intelligent parameters:
# Basic search
make kb-search QUERY="database connection pool"
# With category filter
make kb-search QUERY="JWT authentication" CATEGORY=AUTH
# With quality threshold
make kb-search QUERY="performance optimization" MIN_SCORE=70
# All parameters
make kb-search QUERY="redis cache" CATEGORY=CACHE MIN_SCORE=60 LIMIT=10
Step 3: Display Results
Show top results with relevance and quality scores:
Search Results for "database connection pool"
Found 3 patterns:
1. DB-005: PostgreSQL Connection Pool Optimization
Category: DB | Quality: 85/100 | Relevance: 9.2/10
Keywords: postgresql, connection-pool, optimization, performance
Problems solved:
- Connection pool exhaustion under load
- Slow query performance due to connection limits
2. PERF-002: Connection Pool Tuning Guide
Category: PERF | Quality: 78/100 | Relevance: 8.5/10
Keywords: connection-pool, tuning, performance, scalability
Problems solved:
- Optimizing pool size for workload
- Monitoring pool utilization
3. DB-002: Database Connection Retry Logic
Category: DB | Quality: 72/100 | Relevance: 7.1/10
Keywords: database, connection, retry, error-handling
Problems solved:
- Transient connection failures
- Graceful degradation on DB issues
Step 4: Offer Actions
Interactive follow-up options:
Actions:
1. View full content of a pattern (enter 1-3)
2. Search with different parameters
3. Browse by category
4. Exit
Your choice:
Step 5: View Full Content (if requested)
Display complete pattern details:
# Read full pattern file
cat docs/kb/patterns/DB-005-postgresql-connection-pool.md
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 · 392 lines · 70 tokens per session scan A 2566d849b386
kb-query is a skill published in the GitHub repository davidmatousek/agentic-oriented-development-kit (22 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 2,480 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to kb-query, differing in 0 lines, and is treated as a copy.
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