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 DevelopersGlobal/ai-agent-skills --skill context-loadinggit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skillsWrote 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/developersglobal/ai-agent-skills/context-loading)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/context-loading"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/context-loading/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/developersglobal/ai-agent-skills/context-loading"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/context-loading.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 | $0.00033 | $0.00552 |
| Opus 5.5 | $0.00013 | $0.00221 |
| Sonnet 5.5 | $0.00007 | $0.00110 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
context-loading 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.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
More context is not better context. Irrelevant context dilutes attention, increases cost, and slows inference. This skill enforces disciplined context loading: only the files, docs, and history that the current task requires.
When to Use
- Before starting any complex agent task
- When designing system prompts for production agents
- When context windows are filling up
Process
Step 1: Identify Required Context
- List the files/docs the agent needs to read to complete THIS specific task.
- For each item, ask: "Can the agent complete the task without this?" If yes, don't include it.
- Prioritize: system prompt → task definition → directly relevant code → supporting references.
Verify: Every item in context is directly necessary for the current task.
Step 2: Summarize, Don't Dump
- Long conversation history → summarize to key decisions and current state.
- Large files → extract only the relevant functions/sections.
- Entire docs → extract only the relevant sections.
- Previous agent output → extract only the conclusions and next steps.
Verify: No item in context exceeds what's needed from that source.
Step 3: Set Context Budgets
- Define token allocation for each context section:
- System prompt: ≤ 2,000 tokens
- Task definition: ≤ 500 tokens
- Code context: ≤ 4,000 tokens
- Conversation history (summarized): ≤ 1,000 tokens
- Stay well within model context limits (leave 30% buffer for output).
Verify: Total prompt fits within 70% of model context limit.
Step 4: Refresh Context for New Tasks
- Don't carry over context from a completed task to a new task.
- Start each distinct task with a fresh, minimal context.
- Re-introduce only what the new task genuinely needs.
Verification
- Context items limited to task-required items only
- Long content summarized before inclusion
- Token budget defined and respected
- Context window at ≤70% capacity
References
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 · 66 lines · 33 tokens per session scan A c6ad5c3428d5
context-loading is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (177 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 552 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
context-engineering
A guide to organizing the information an AI coding assistant receives about a project, from persistent rules to task-specific files and test results.
handoff
Compact the current conversation into a handoff document for a fresh agent.
doubt-driven-development
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git-workflow-and-versioning
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, splitting uncommitted work in a messy working tree into clean atomic commits, opening or reviewing a pull request (PR), pushing to a remote, or when you need to organize work across multiple…
observability-and-instrumentation
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.