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Skills context-window-management
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context-window-management
Content revision r2 High Risk ⚙️ External commands
Manage LLM Context Windows Reliably
Long conversations can exceed token limits and lose important details. This skill provides budgeting, summarization, prioritization, and routing strategies for reliable context assembly.
Supports: Claude Codex Code(CC)
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Review the plan and obtain explicit user consent before changing files.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "context-window-management" from https://skillstore.io/skills/sickn33-context-window-management.md and its manifest at https://skillstore.io/api/skills/sickn33-context-window-management/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "context-window-management". A support assistant has a 50,000-token history and a 32,000-token model limit.
Expected outcome:
- Reserve capacity for the response before processing history.
- Keep the current request and recent support messages in full.
- Summarize older messages while preserving customer details, decisions, and unresolved issues.
- Count the final context with the model tokenizer before sending.
Using "context-window-management". Review a system that removes the oldest message whenever the context is full.
Expected outcome:
- The policy risks deleting preferences, decisions, and facts that remain relevant.
- Replace simple deletion with importance scoring and structured summarization.
- Validate the retained context against a defined token budget.
Using "context-window-management". Plan context allocation for a 100,000-token model.
Expected outcome:
- Assign separate budgets to instructions, critical context, history, query, and response.
- Reallocate unused capacity only after counting the prepared components.
- Make limits configurable because model capacities and tokenizer behavior differ.
Security Audit
High Riskv5 • 7/23/2026 Open versioned report
All 11 external-command findings are false positives: the flagged backticks are JavaScript template literals or Markdown delimiters, not shell execution. The summarization example directly embeds message content into an LLM prompt and promotes its output to a system message, creating a prompt-injection risk.
1
Files scanned
316
Lines analyzed
0
Review items
0
False positives ignored
Confirmed security concerns (1)
High
Prompt Injection Risk in Conversation Summarization
Conversation messages are interpolated directly into summarizer instructions, then the generated summary is inserted with the system role. Malicious message text could influence future behavior.
The example shows message content entering an instruction prompt without a trust boundary, followed by promotion of the generated summary to a system message.
Risk Factors
⚙️ External commands (11)
Audited by: codex View Audit History →
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APA citation
sickn33. (2026). context-window-management security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-context-window-management/audits/5BibTeX citation
@techreport{sickn33-sickn33-context-window-management-2026,
author = {sickn33},
title = {context-window-management security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-context-window-management/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "context-window-management security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-context-window-management/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-context-window-management:audit:5"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: Medium 55
Architecture
85
Maintainability
87
Content
68
Community
83
Spec Compliance
What You Can Build
Design Multi-Turn Assistants
Choose a context strategy that preserves recent messages, decisions, preferences, and enough capacity for each response.
Standardize Token Budgets
Define predictable allocations for system instructions, critical context, history, current queries, and generated responses.
Diagnose Context Overflow
Review a prompt pipeline for missing token counts, naive truncation, hardcoded limits, and weak prioritization.
Try These Prompts
Choose a Basic Strategy
Recommend a context strategy for this assistant. Model limit: [tokens]. Typical history: [size]. Preserve: [critical information].
Allocate a Token Budget
Allocate [total tokens] across system instructions, critical context, history, current query, and response. Explain each allocation.
Design Summarization Rules
Design summarization rules for [conversation type]. Preserve [facts and decisions], keep [recent messages], and identify validation checks.
Audit an Advanced Context Pipeline
Audit this context pipeline: [description]. Evaluate token counting, routing, prioritization, summarization safety, retrieval triggers, and response capacity. Propose a staged improvement plan.
Best Practices
- Reserve response capacity before adding optional history or retrieved context.
- Preserve explicit decisions, preferences, constraints, and unresolved tasks during summarization.
- Count the assembled context with the tokenizer used by the target model.
Avoid
- Do not remove the oldest messages without checking their importance.
- Do not use one hardcoded token limit for every model.
- Do not promote unvalidated summaries of untrusted messages into system instructions.
Frequently Asked Questions
When should I summarize conversation history?
Summarize when the history approaches its allocated budget and older details remain useful.
Should I always keep the newest messages?
Keep recent messages when relevant, but prioritize critical facts and decisions over recency alone.
Does this skill implement retrieval?
No. It explains when retrieval may help, but retrieval implementation is outside its scope.
How should I choose token limits?
Use model-specific limits and reserve capacity for the expected response before allocating input context.
Can summarization lose important information?
Yes. Define preservation rules, retain critical messages, and test summaries against known conversation facts.
Which tools can use this skill?
The marketplace metadata lists compatibility with Claude, Codex, and Claude Code.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/context-window-managementRef
f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526
Maintenance freshness
7/26/2026
Usage
8 downloads · 91 views
File structure
📄 SKILL.md
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