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.
npx agentmods add commands/wshobson/agents/context-restoregit 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-restore)<a href="https://agentmods.dev/commands/wshobson/agents/context-restore"><img src="https://agentmods.dev/badge/commands/wshobson/agents/context-restore.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 | $0.00000 | $0.01023 |
| Opus 5 | $0.00000 | $0.00511 |
| Sonnet 5 | $0.00000 | $0.00205 |
| Haiku 4.5 | $0.00000 | $0.00102 |
Grade A, and why
context-restore 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 2d 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-restore — 100% identical, 15 lines differ
- context-restore — 100% identical, 15 lines differ
- context-restore — 100% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Restoration: Advanced Semantic Memory Rehydration
Role Statement
Expert Context Restoration Specialist focused on intelligent, semantic-aware context retrieval and reconstruction across complex multi-agent AI workflows. Specializes in preserving and reconstructing project knowledge with high fidelity and minimal information loss.
Context Overview
The Context Restoration tool is a sophisticated memory management system designed to:
- Recover and reconstruct project context across distributed AI workflows
- Enable seamless continuity in complex, long-running projects
- Provide intelligent, semantically-aware context rehydration
- Maintain historical knowledge integrity and decision traceability
Core Requirements and Arguments
Input Parameters
context_source: Primary context storage location (vector database, file system)project_identifier: Unique project namespacerestoration_mode:full: Complete context restorationincremental: Partial context updatediff: Compare and merge context versions
token_budget: Maximum context tokens to restore (default: 8192)relevance_threshold: Semantic similarity cutoff for context components (default: 0.75)
Advanced Context Retrieval Strategies
1. Semantic Vector Search
- Utilize multi-dimensional embedding models for context retrieval
- Employ cosine similarity and vector clustering techniques
- Support multi-modal embedding (text, code, architectural diagrams)
def semantic_context_retrieve(project_id, query_vector, top_k=5):
"""Semantically retrieve most relevant context vectors"""
vector_db = VectorDatabase(project_id)
matching_contexts = vector_db.search(
query_vector,
similarity_threshold=0.75,
max_results=top_k
)
return rank_and_filter_contexts(matching_contexts)
2. Relevance Filtering and Ranking
- Implement multi-stage relevance scoring
- Consider temporal decay, semantic similarity, and historical impact
- Dynamic weighting of context components
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.
- 2d ago First seen · 172 lines · 0 tokens per session scan A 965e6a943deb
context-restore is a command published in the GitHub repository wshobson/agents (39,428 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,023 tokens. 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
ctx
Search agent history or trace code to its original agent session.
enrich
Enrich project memory by mining 100 recently merged PRs: extracts decisions, conventions, gotchas, and architectural facts from PR discussions, review comments, and PR bodies.
remember
Save something to agent memory - picks project or user scope automatically from context.
memory
Read and follow Operator Team OS/4. Workflows/memory.md.
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.