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 skills add bugrabilge/bilge-development-kit --skill code-refactoring-context-restoregit clone --depth 1 https://github.com/bugrabilge/bilge-development-kitWrote 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/skills/bugrabilge/bilge-development-kit/code-refactoring-context-restore)<a href="https://agentmods.dev/skills/bugrabilge/bilge-development-kit/code-refactoring-context-restore"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/code-refactoring-context-restore/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/skills/bugrabilge/bilge-development-kit/code-refactoring-context-restore"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/code-refactoring-context-restore.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 | $0.00016 | $0.01181 |
| Opus 5.5 | $0.00006 | $0.00472 |
| Sonnet 5.5 | $0.00003 | $0.00236 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
code-refactoring-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 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
95% identical to code-refactoring-context-restore — 5 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Restoration: Advanced Semantic Memory Rehydration
Use this skill when
- Working on context restoration: advanced semantic memory rehydration tasks or workflows
- Needing guidance, best practices, or checklists for context restoration: advanced semantic memory rehydration
Do not use this skill when
- The task is unrelated to context restoration: advanced semantic memory rehydration
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
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)
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 · 182 lines · 16 tokens per session scan A af1284381680
code-refactoring-context-restore is a skill published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 1,181 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to code-refactoring-context-restore, differing in 5 lines, and is treated as a copy.
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