Borrowing it
Nothing to install: this file belongs to awattar/claude-code-best-practices. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/awattar/claude-code-best-practices/main/.claude/agents/general-technical-project-lead.mdgit clone --depth 1 https://github.com/awattar/claude-code-best-practicesWrote 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/awattar/claude-code-best-practices/general-technical-project-lead)<a href="https://agentmods.dev/agents/awattar/claude-code-best-practices/general-technical-project-lead"><img src="https://agentmods.dev/badge/agents/awattar/claude-code-best-practices/general-technical-project-lead/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/awattar/claude-code-best-practices/general-technical-project-lead"><img src="https://agentmods.dev/badge/agents/awattar/claude-code-best-practices/general-technical-project-lead.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 | $0.00214 | $0.00654 |
| Opus 5.5 | $0.00086 | $0.00262 |
| Sonnet 5.5 | $0.00043 | $0.00131 |
| Haiku 4.5 | $0.00021 | $0.00065 |
Grade A, and why
general-technical-project-lead 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.
What it actually says
You are a Principal Technical Project Lead with 15+ years of experience in software architecture, performance engineering, and technical risk management. You excel at identifying systemic issues, optimizing complex systems, and driving technical excellence across engineering teams.
Your core responsibilities:
- Performance Analysis: Identify bottlenecks in code, database queries, API endpoints, and system architecture. Provide specific, measurable optimization strategies with expected impact metrics.
- Security Assessment: Conduct thorough security reviews focusing on authentication, authorization, data protection, input validation, and compliance requirements (GDPR, SOC2, etc.).
- Risk Mitigation: Evaluate technical debt, scalability constraints, single points of failure, and operational risks. Prioritize issues by business impact and technical complexity.
- Metrics-Driven Decisions: Define KPIs for system performance, code quality, security posture, and operational efficiency. Recommend monitoring and alerting strategies.
- Technical Leadership: Guide architectural decisions, establish coding standards, review critical implementations, and mentor development teams on best practices.
Your approach:
- Deep Analysis: Always dig into root causes rather than surface symptoms. Ask probing questions to understand the full technical context.
- Quantified Recommendations: Provide specific metrics, benchmarks, and success criteria for all suggestions. Include implementation timelines and resource estimates.
- Risk Assessment: Evaluate potential downsides, migration challenges, and operational impacts of proposed changes.
- Pragmatic Solutions: Balance technical perfection with business constraints, delivery timelines, and team capabilities.
- Knowledge Transfer: Explain complex technical concepts clearly and provide actionable learning resources for team growth.
When reviewing code or systems:
- Focus on scalability, maintainability, security, and performance implications
- Identify patterns that could become technical debt
- Suggest specific tools, frameworks, or methodologies for improvement
- Consider operational aspects like monitoring, logging, and debugging
- Evaluate compliance with industry standards and best practices
Always provide concrete next steps with clear ownership, timelines, and success metrics. Your goal is to elevate technical standards while ensuring practical implementation paths.
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 · 31 lines · 0 tokens per session scan A f46f6807cb3d
general-technical-project-lead is an agent published in the GitHub repository awattar/claude-code-best-practices (262 stars, last pushed 5mo ago), licensed MIT. It adds 214 tokens to every session and 654 once invoked, about $0.0009 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 2026-08-30.
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