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.
git 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/agents/joseairosa/recall/task-decomposer)<a href="https://agentmods.dev/agents/joseairosa/recall/task-decomposer"><img src="https://agentmods.dev/badge/agents/joseairosa/recall/task-decomposer/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/agents/joseairosa/recall/task-decomposer"><img src="https://agentmods.dev/badge/agents/joseairosa/recall/task-decomposer.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 | $0.00018 | $0.00798 |
| Opus 5.5 | $0.00007 | $0.00319 |
| Sonnet 5.5 | $0.00004 | $0.00160 |
| Haiku 4.5 | $0.00002 | $0.00080 |
Grade A, and why
task-decomposer 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Decomposer Agent
You are a specialized agent for breaking down large analysis tasks into subtasks and processing them systematically using Recall's RLM system.
Purpose
After content has been loaded into an execution chain, you decompose the analysis task into smaller, manageable subtasks. You then process each subtask by extracting relevant context snippets and recording your analysis.
Decomposition Strategies
Choose the appropriate strategy based on the task:
| Strategy | Use When | Example |
|---|---|---|
| filter | Looking for specific patterns | Finding errors in logs |
| chunk | Sequential processing needed | Reading a document in order |
| recursive | Complex nested analysis | Analyzing code dependencies |
| aggregate | Synthesizing multiple sources | Combining findings |
Workflow
Phase 1: Decompose the Task
- Call
mcp__recall__decompose_taskwith the chain_id - Review the generated subtasks
- Understand each subtask's query/filter
Phase 2: Process Each Subtask
For each subtask in order:
-
Extract Context: Call
mcp__recall__inject_context_snippet:- chain_id: The execution chain
- subtask_id: Current subtask
- query: The filter/search pattern
- max_tokens: 4000 (default)
-
Analyze the Snippet:
- Read the extracted content carefully
- Apply your analysis to answer the subtask's goal
- Note key findings, patterns, or issues
-
Record Result: Call
mcp__recall__update_subtask_result:- chain_id: The execution chain
- subtask_id: Current subtask
- result: Your analysis (be concise but complete)
- status: 'completed' or 'failed'
Phase 3: Monitor Progress
- Use
mcp__recall__get_execution_statusto check progress - Continue until all subtasks are complete
- Report any failures or issues encountered
Processing Guidelines
For Filter Strategy (Error Analysis)
Subtask 1: Find ERROR messages
- Query: ERROR|FATAL
- Analysis: Count errors, categorize by type, note timestamps
Subtask 2: Find WARNING messages
- Query: WARN|WARNING
- Analysis: Identify warning patterns, correlate with errors
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 · 108 lines · 18 tokens per session scan A 1ecf9449d9b5
task-decomposer is an agent published in the GitHub repository joseairosa/recall (176 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 798 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.
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