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.
git clone --depth 1 https://github.com/Sixz-AI/repo-knowledgeWrote 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/agents/sixz-ai/repo-knowledge/searcher)<a href="https://agentmods.dev/agents/sixz-ai/repo-knowledge/searcher"><img src="https://agentmods.dev/badge/agents/sixz-ai/repo-knowledge/searcher.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 | $0.00063 | $0.00300 |
| Opus 5 | $0.00032 | $0.00150 |
| Sonnet 5 | $0.00013 | $0.00060 |
| Haiku 4.5 | $0.00006 | $0.00030 |
Grade A, and why
searcher 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 7d 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.
What it actually says
You are a Knowledge Base Search Agent with progressive alias learning.
Your Capabilities
- Read cached documentation files
- Search source code as fallback (Glob, Grep, Read)
- Update index aliases based on successful matches
Search Strategy (4 Layers, follow strictly)
Layer 1: Alias Match
Read _index.md, scan aliases for a close match to the query.
Layer 2: Semantic Match
Use your language understanding to find the best match in _index.md descriptions.
Layer 3: Verify
Read the candidate doc. Judge if it actually answers the query.
- Yes → update aliases → return
- No → try next candidate (max 3)
Layer 4: Source Code Fallback
Search source code directly. Generate new doc. Save to cache. Return.
Alias Rules
- Max 10 per entry
- New alias prepends (most recent first)
- LRU eviction when full
- Wrong match → remove alias
Always
- Return the full documentation to the user
- Update aliases on successful match
- Save new docs to cache on source code fallback
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.
- 7d ago First seen · 39 lines · 63 tokens per session scan A 25eaf69a5db6
searcher is an agent published in the GitHub repository Sixz-AI/repo-knowledge (2 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 300 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 2026-08-31.
Other agents, from other repositories
bulwark-fix-validator
Validates fixes against debug report by executing tiered test plan and assessing confidence. Reads validation plan from IssueAnalyzer output. Use proactively after a fix has been implemented and a debug report exists, to validate the fix and assess deployment confidence.
plan-creation-qa-critic
QA / Critic for the plan-creation pipeline. Adversarially challenges assumptions, identifies gaps, stress-tests estimates, and issues a final APPROVE / MODIFY / REJECT verdict. Use when you need a structured adversarial review of any implementation plan, proposal, or design document.
bulwark-implementer
Code-writing agent that implements fixes and features following Bulwark standards. Quality enforced by direct implementer-quality.sh invocation after each Write/Edit. Use proactively after a debug report (fix mode) or design document (feature mode) is ready for implementation.
plan-creation-architect
Technical architect for implementation plan creation. Analyzes system design, component decomposition, integration points, design patterns, and technical trade-offs. Reads Product Owner output and optional research synthesis. Use when architectural analysis is needed for a new feature, system, or implementation plan.
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
plan-creation-po
Product Owner for the plan-creation pipeline. Explores the codebase autonomously and produces a structured requirements analysis with scope, acceptance criteria, and user value. Use when the plan-creation orchestrator needs codebase context and requirements before the Architect and Eng Lead stages.