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 5.5 | $0.00005 | $0.00245 |
| Sonnet 5.5 | $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 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.
This is a copy
92% identical to deep-reflector — 40 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.
- 1mo 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 3mo ago), licensed MIT. It adds 13 tokens to every session and 613 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to deep-reflector, differing in 40 lines, and is treated as a copy.
Other agents, from other repositories
fixer
Use for quick fixes, hotfixes, urgent patches, and time-sensitive bug repairs.
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?'…
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…
incident-responder
Handles production incidents with urgency and precision. Use IMMEDIATELY when production issues occur. Coordinates debugging, implements fixes, and documents post-mortems.
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.
MAF Incident Responder
Use when a deployed MAF 1.3.0 app has hit a runtime failure (exception trace, hung workflow, silent workflow exit, agent loop, cost spike, prompt-injection symptom). Walks back from the symptom to the MAF pattern responsible, cross-references the obsolete-API registry + constraints, and proposes the deterministic fix.…