Agentic Plugin Marketplace is a collection of reusable plugins, agents, skills, commands, and rules for coding-agent tools including Claude Code, Codex CLI, Cursor, OpenCode, Antigravity CLI, and GitHub Copilot. It is for developers assembling agentic workflows across multiple harnesses from shared Markdown sources, and the catalogue entries are examples or subsets of those workflow components.
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/wshobson/agentsWrote 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/commands/wshobson/agents/context-save)<a href="https://agentmods.dev/commands/wshobson/agents/context-save"><img src="https://agentmods.dev/badge/commands/wshobson/agents/context-save/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/commands/wshobson/agents/context-save"><img src="https://agentmods.dev/badge/commands/wshobson/agents/context-save.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 | $0.00012 | $0.00999 |
| Opus 5.5 | $0.00005 | $0.00400 |
| Sonnet 5.5 | $0.00002 | $0.00200 |
| Haiku 4.5 | $0.00001 | $0.00100 |
Grade A, and why
context-save 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 24d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- context-save — 100% identical, 39 lines differ
- context-save — 100% identical, 41 lines differ
- context-save — 100% identical, 41 lines differ
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Save Tool: Intelligent Context Management Specialist
Role and Purpose
An elite context engineering specialist focused on comprehensive, semantic, and dynamically adaptable context preservation across AI workflows. This tool orchestrates advanced context capture, serialization, and retrieval strategies to maintain institutional knowledge and enable seamless multi-session collaboration.
Context Management Overview
The Context Save Tool is a sophisticated context engineering solution designed to:
- Capture comprehensive project state and knowledge
- Enable semantic context retrieval
- Support multi-agent workflow coordination
- Preserve architectural decisions and project evolution
- Facilitate intelligent knowledge transfer
Requirements and Argument Handling
Input Parameters
$PROJECT_ROOT: Absolute path to project root$CONTEXT_TYPE: Granularity of context capture (minimal, standard, comprehensive)$STORAGE_FORMAT: Preferred storage format (json, markdown, vector)$TAGS: Optional semantic tags for context categorization
Context Extraction Strategies
1. Semantic Information Identification
- Extract high-level architectural patterns
- Capture decision-making rationales
- Identify cross-cutting concerns and dependencies
- Map implicit knowledge structures
2. State Serialization Patterns
- Use JSON Schema for structured representation
- Support nested, hierarchical context models
- Implement type-safe serialization
- Enable lossless context reconstruction
3. Multi-Session Context Management
- Generate unique context fingerprints
- Support version control for context artifacts
- Implement context drift detection
- Create semantic diff capabilities
4. Context Compression Techniques
- Use advanced compression algorithms
- Support lossy and lossless compression modes
- Implement semantic token reduction
- Optimize storage efficiency
5. Vector Database Integration
Supported Vector Databases:
- Pinecone
- Weaviate
- Qdrant
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.
- 24d ago Changed · +4 lines · +12 tokens per session 882d900c18e0
- 1mo ago First seen · 178 lines · 0 tokens per session scan A 4801dca4b039
context-save is a command published in the GitHub repository wshobson/agents (41,158 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 999 once invoked, about $0.0000 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.
Other commands, from other repositories
pull-learnings
Pulls shared organization patterns into local org-patterns.toon.
context
Read a file together with the code that is semantically and structurally related to it.
lessons
Lists the feedback memories captured for the current project (and globally) — the "lessons" learned from user corrections.
headroom
Headroom status — proxy, memory, and token savings.
localize-project
Create persistent project memory for this repo (spec, ADRs, session log) on top of the global Understudy team.
start-session
Start work session — load project context.