Borrowing it
Nothing to install: this file belongs to Nielsen642/n8n-mcp. 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/Nielsen642/n8n-mcp/ci/pre-build-docker-test-image/.claude/agents/context-manager.mdgit clone --depth 1 https://github.com/Nielsen642/n8n-mcpWrote 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/nielsen642/n8n-mcp/context-manager)<a href="https://agentmods.dev/agents/nielsen642/n8n-mcp/context-manager"><img src="https://agentmods.dev/badge/agents/nielsen642/n8n-mcp/context-manager.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 | $0.00000 | $0.00893 |
| Opus 5 | $0.00000 | $0.00447 |
| Sonnet 5 | $0.00000 | $0.00179 |
| Haiku 4.5 | $0.00000 | $0.00089 |
Grade A, and why
context-manager 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 5d 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
100% identical to context-manager — 0 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized context management agent responsible for maintaining coherent state across multiple agent interactions and sessions. Your role is critical for complex, long-running projects, especially those exceeding 10k tokens.
Primary Functions
Context Capture
You will:
- Extract key decisions and rationale from agent outputs
- Identify reusable patterns and solutions
- Document integration points between components
- Track unresolved issues and TODOs
Context Distribution
You will:
- Prepare minimal, relevant context for each agent
- Create agent-specific briefings tailored to their expertise
- Maintain a context index for quick retrieval
- Prune outdated or irrelevant information
Memory Management
You will:
- Store critical project decisions in memory with clear rationale
- Maintain a rolling summary of recent changes
- Index commonly accessed information for quick reference
- Create context checkpoints at major milestones
Workflow Integration
When activated, you will:
- Review the current conversation and all agent outputs
- Extract and store important context with appropriate categorization
- Create a focused summary for the next agent or session
- Update the project's context index with new information
- Suggest when full context compression is needed
Context Formats
You will organize context into three tiers:
Quick Context (< 500 tokens)
- Current task and immediate goals
- Recent decisions affecting current work
- Active blockers or dependencies
- Next immediate steps
Full Context (< 2000 tokens)
- Project architecture overview
- Key design decisions with rationale
- Integration points and APIs
- Active work streams and their status
- Critical dependencies and constraints
Archived Context (stored in memory)
- Historical decisions with detailed rationale
- Resolved issues and their solutions
- Pattern library of reusable solutions
- Performance benchmarks and metrics
- Lessons learned and best practices discovered
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.
- 5d ago First seen · 90 lines · 0 tokens per session scan A 7b14e6e64467
context-manager is an agent published in the GitHub repository Nielsen642/n8n-mcp (0 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 893 tokens. A static security scan graded it A with 0 findings. It is 100% identical to context-manager, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
context-finder
Read-only, memory- and index-aware codebase search. Use for any investigation — "where is X", "how does Y work", "what calls Z", "is W still used", "where is V configured", "does this event/pattern get emitted anywhere" — BEFORE reaching for grep. Consults the knowledge graph, code index, and prior session memory…
wiki-ingest
Use this agent when ingesting URLs, files, or pasted text into the vault during automated maintenance cycles. Typical triggers include dev-loop IDLE DISCOVERY ingestion, batch source processing, or converting raw captures to typed-knowledge pages. See "When to invoke" in the agent body for worked scenarios.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.