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/joseairosa/recall/load-contextgit clone --depth 1 https://github.com/joseairosa/recallWrote 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/joseairosa/recall/load-context)<a href="https://agentmods.dev/commands/joseairosa/recall/load-context"><img src="https://agentmods.dev/badge/commands/joseairosa/recall/load-context.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 | $0.00018 | $0.00437 |
| Opus 5 | $0.00009 | $0.00218 |
| Sonnet 5 | $0.00004 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
load-context 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 4d 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
/load-context Command
Load large files into Recall's RLM (Recursive Language Model) system for processing content that exceeds context window limits.
Usage
/load-context <file_path> [task_description]
/load-context <glob_pattern> [task_description]
Examples
/load-context /var/log/app.log "Find all errors and their causes"
/load-context src/**/*.ts "Analyze code patterns and potential issues"
/load-context ./large-document.pdf "Summarize key points"
Behavior
When invoked, this command will:
-
Read the specified content:
- Single file: Read the entire file
- Glob pattern: Find matching files and concatenate content
-
Analyze content size:
- If <25K tokens: Suggest direct analysis (RLM not needed)
- If >25K tokens: Proceed with RLM
-
Create execution context:
- Store content externally in Recall
- Generate chain_id for tracking
- Detect optimal decomposition strategy
-
Return setup information:
- Chain ID for subsequent commands
- Estimated token count
- Recommended strategy
- Suggested next steps
Output
After loading, you'll receive:
RLM Context Created
-------------------
Chain ID: 01HXYZ...
Tokens: ~125,000
Strategy: filter (recommended)
Next steps:
1. Run /decompose 01HXYZ... to break down the analysis
2. Or manually call decompose_task with a custom strategy
Notes
- Large files (>1MB) may take time to process
- The task description helps optimize the decomposition strategy
- Use glob patterns to analyze multiple related files together
- Chain IDs are needed for all subsequent RLM operations
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.
- 4d ago First seen · 75 lines · 18 tokens per session scan A b7dfb0dc24ee
load-context is a command published in the GitHub repository joseairosa/recall (175 stars, last pushed 4d ago), licensed MIT. It adds 18 tokens to every session and 437 once invoked, about $0.0001 per session on Opus 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 commands, from other repositories
smartcompact
Pin important turns before compacting — pins survive compaction via SessionStart hook reinjection.
window
Set the context window Astral measures against (200K / 1M / custom / auto).
recall-maintain
Run maintenance on the memory system. Primary function: pressure-based demotion when working memory exceeds the word budget. Secondary: stale entry verification, contradiction checks, open loop review.
recall-init-ids
Add ^tr IDs to untagged memory entries in managed files. This is safe to run multiple times - it only tags entries that don't already have IDs.
recall-init
Initialize the Total Recall memory system in this project.
recall-write
Write a note to memory, applying the write gate. Default destination: daily log.