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 Stijnman/grok-custom-skills --skill mega-context-managergit clone --depth 1 https://github.com/Stijnman/grok-custom-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/stijnman/grok-custom-skills/mega-context-manager)<a href="https://agentmods.dev/skills/stijnman/grok-custom-skills/mega-context-manager"><img src="https://agentmods.dev/badge/skills/stijnman/grok-custom-skills/mega-context-manager/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/stijnman/grok-custom-skills/mega-context-manager"><img src="https://agentmods.dev/badge/skills/stijnman/grok-custom-skills/mega-context-manager.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 | $0.00035 | $0.00313 |
| Opus 5.5 | $0.00014 | $0.00125 |
| Sonnet 5.5 | $0.00007 | $0.00063 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
mega-context-manager 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.
What it actually says
Mega Context Manager
When to Use
- User says manage context or task matches this capability
- User says too much context or task matches this capability
- User says chunk document or task matches this capability
- User says context budget or task matches this capability
Workflow
- Measure token estimate of inputs.
- If over budget: chunk, summarize chunks, index.
- Retrieve relevant chunks per subtask only.
- Drop stale chunks after task completion.
Integrations
semantic-memory-managerpredictive-cache-managerpersistent-memory-bridge
Error Handling
| Failure | Response |
|---|---|
| Retrieval miss | Expand query; include adjacent chunks. |
Gotchas
- Summaries lose detail; keep raw chunks for citation.
Example
Input: User request matching triggers above. Output: Structured result per workflow with integrations invoked as needed.
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 · 48 lines · 35 tokens per session scan A f0cb079dfbce
mega-context-manager is a skill published in the GitHub repository Stijnman/grok-custom-skills (13 stars, last pushed 4d ago), licensed MIT. It adds 35 tokens to every session and 313 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 2026-08-31.
Other skills, from other repositories
cross-task-learner
Enable agent loops to learn from similar past tasks and share patterns across loops.
ralph-memory
Manage Al semantic memory entries — list, query, and clear lessons learned across loop iterations.
session-explore
Investigate past AI session activity with cited catalog search, timelines, tool analytics, and bounded comparisons across providers.
reflection-injection
Inject relevant past reflections into agent context at iteration start so agents learn from prior mistakes without repeating them.
session-harvest
Extract cited decisions, requirements, risks, entities, and relationships from session history and review exact candidate versions before memory promotion.
ralph-reflect
View and manage agent loop reflections and episodic memory.