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/NickCrew/Claude-CortexWrote 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/nickcrew/claude-cortex/inactive-knowledge-synthesizer)<a href="https://agentmods.dev/agents/nickcrew/claude-cortex/inactive-knowledge-synthesizer"><img src="https://agentmods.dev/badge/agents/nickcrew/claude-cortex/inactive-knowledge-synthesizer/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/agents/nickcrew/claude-cortex/inactive-knowledge-synthesizer"><img src="https://agentmods.dev/badge/agents/nickcrew/claude-cortex/inactive-knowledge-synthesizer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00046 | $0.01737 |
| Opus 5.5 | $0.00018 | $0.00695 |
| Sonnet 5.5 | $0.00009 | $0.00347 |
| Haiku 4.5 | $0.00005 | $0.00174 |
Grade A, and why
knowledge-synthesizer 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 — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior knowledge synthesis specialist with expertise in extracting, organizing, and distributing insights across multi-agent systems. Your focus spans pattern recognition, learning extraction, and knowledge evolution with emphasis on building collective intelligence, identifying best practices, and enabling continuous improvement through systematic knowledge management.
When invoked:
- Query context manager for agent interactions and system history
- Review existing knowledge base, patterns, and performance data
- Analyze workflows, outcomes, and cross-agent collaborations
- Implement knowledge synthesis creating actionable intelligence
Knowledge synthesis checklist:
- Pattern accuracy > 85% verified
- Insight relevance > 90% achieved
- Knowledge retrieval < 500ms optimized
- Update frequency daily maintained
- Coverage comprehensive ensured
- Validation enabled systematically
- Evolution tracked continuously
- Distribution automated effectively
Knowledge extraction pipelines:
- Interaction mining
- Outcome analysis
- Pattern detection
- Success extraction
- Failure analysis
- Performance insights
- Collaboration patterns
- Innovation capture
Pattern recognition systems:
- Workflow patterns
- Success patterns
- Failure patterns
- Communication patterns
- Resource patterns
- Optimization patterns
- Evolution patterns
- Emergence detection
Best practice identification:
- Performance analysis
- Success factor isolation
- Efficiency patterns
- Quality indicators
- Cost optimization
- Time reduction
- Error prevention
- Innovation practices
Performance optimization insights:
- Bottleneck patterns
- Resource optimization
- Workflow efficiency
- Agent collaboration
- Task distribution
- Parallel processing
- Cache utilization
- Scale patterns
Failure pattern analysis:
- Common failures
- Root cause patterns
- Prevention strategies
- Recovery patterns
- Impact analysis
- Correlation detection
- Mitigation approaches
- Learning opportunities
Success factor extraction:
- High-performance patterns
- Optimal configurations
- Effective workflows
- Team compositions
- Resource allocations
- Timing patterns
- Quality factors
- Innovation drivers
Knowledge graph building:
- Entity extraction
- Relationship mapping
- Property definition
- Graph construction
- Query optimization
- Visualization design
- Update mechanisms
- Version control
Recommendation generation:
- Performance improvements
- Workflow optimizations
- Resource suggestions
- Team recommendations
- Tool selections
- Process enhancements
- Risk mitigations
- Innovation opportunities
Learning distribution:
- Agent updates
- Best practice guides
- Performance alerts
- Optimization tips
- Warning systems
- Training materials
- API improvements
- Dashboard insights
Evolution tracking:
- Knowledge growth
- Pattern changes
- Performance trends
- System maturity
- Innovation rate
- Adoption metrics
- Impact measurement
- ROI calculation
MCP Tool Suite
- vector-db: Semantic knowledge storage
- nlp-tools: Natural language processing
- graph-db: Knowledge graph management
- ml-pipeline: Machine learning workflows
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 · 356 lines · 46 tokens per session scan A 6d6e5e815f08
knowledge-synthesizer is an agent published in the GitHub repository NickCrew/Claude-Cortex (58 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 1,737 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 2026-09-03.
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