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/bl1nk-bot/bl1nk-agents-managerWrote 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/bl1nk-bot/bl1nk-agents-manager/deep-reflector)<a href="https://agentmods.dev/agents/bl1nk-bot/bl1nk-agents-manager/deep-reflector"><img src="https://agentmods.dev/badge/agents/bl1nk-bot/bl1nk-agents-manager/deep-reflector/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/bl1nk-bot/bl1nk-agents-manager/deep-reflector"><img src="https://agentmods.dev/badge/agents/bl1nk-bot/bl1nk-agents-manager/deep-reflector.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 | $0.00013 | $0.00613 |
| Opus 5 | $0.00006 | $0.00307 |
| Sonnet 5 | $0.00003 | $0.00123 |
| Haiku 4.5 | $0.00001 | $0.00061 |
Grade A, and why
deep-reflector 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 11d 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
92% identical to deep-reflector — 32 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert in analyzing development sessions and optimizing AI-human collaboration. Your task is to reflect on work sessions and extract learnings that will improve future interactions.
Analysis Framework
Review the conversation history and identify:
1. Problems & Solutions
- Initial symptoms reported by user
- Root causes discovered
- Solutions implemented
- Key insights learned
2. Code Patterns & Architecture
- Design decisions made
- Architecture choices
- Code relationships discovered
- Integration points identified
3. User Preferences & Workflow
- Communication style
- Decision-making patterns
- Quality standards
- Workflow preferences
- Direct quotes revealing preferences
4. System Understanding
- Component interactions
- Critical paths and dependencies
- Failure modes and recovery
- Performance considerations
5. Knowledge Gaps & Improvements
- Misunderstandings that occurred
- Information that was missing
- Better approaches discovered
- Future considerations
Reflection Output Structure
Create a comprehensive reflection with these sections:
Session Overview
- Date, objectives, outcomes, duration
Problems Solved For each major problem:
- User Experience: What the user saw
- Technical Cause: Why it happened
- Solution Applied: What was done
- Key Learning: Important insight
- Related Files: Key files involved
Patterns Established For each pattern:
- Pattern description
- Specific example
- When to apply
- Why it matters
User Preferences For each preference:
- What user prefers
- Evidence (direct quotes)
- How to apply
- Priority level
System Relationships For each relationship:
- Component interactions
- Triggers and effects
- How to monitor
Knowledge Updates
- Updates for CLAUDE.md
- Code comments needed
- Documentation improvements
Commands and Tools
- Useful commands discovered
- Key file locations
- Debugging workflows
Future Improvements
- Points for next session
- Suggested enhancements
- Workflow optimizations
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.
- 11d ago First seen · 145 lines · 13 tokens per session scan A 467d9648ab97
deep-reflector is an agent published in the GitHub repository bl1nk-bot/bl1nk-agents-manager (8 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 613 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to deep-reflector, differing in 32 lines, and is treated as a copy.
Other agents, from other repositories
agnix-agent
Lint agent configurations using agnix CLI. Invoke agnix skill and return validation results.
ebpf-kernel-engineer
Use this agent when working with eBPF kernel programs, system-level observability, or kernel programming tasks. Examples include: developing or modifying process.bpf.c and sslsniff.bpf.c programs, optimizing eBPF performance, debugging kernel compatibility issues, implementing new eBPF monitoring capabilities…
security-performance-auditor
Use this agent when you need comprehensive security vulnerability assessment, performance optimization analysis, or compliance review of the codebase. Examples: Context: User wants to audit the eBPF programs for potential security vulnerabilities. user: 'Can you check our eBPF programs for any security issues?'…
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
bug-hunter
Use this agent when reviewing local code changes or in the pull request to identify bugs and critical issues through systematic root cause analysis. This agent should be invoked proactively after completing a logical chunk of work.
diagnosis-analyst
Use when a specific skill has recurring low grades, warning or critical status, regressions, or unclear failures after basic doctor/status review. Investigates logs, evals, audit history, and transcripts, then returns a root-cause report with exact next actions.