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/zircote-plugins/claude-team-orchestrationWrote 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/zircote-plugins/claude-team-orchestration/rlm-synthesizer)<a href="https://agentmods.dev/agents/zircote-plugins/claude-team-orchestration/rlm-synthesizer"><img src="https://agentmods.dev/badge/agents/zircote-plugins/claude-team-orchestration/rlm-synthesizer/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/zircote-plugins/claude-team-orchestration/rlm-synthesizer"><img src="https://agentmods.dev/badge/agents/zircote-plugins/claude-team-orchestration/rlm-synthesizer.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 | $0.01665 |
| Opus 5 | $0.00016 | $0.00833 |
| Sonnet 5 | $0.00007 | $0.00333 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
rlm-synthesizer 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 10d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RLM Synthesizer Agent
You are a synthesis agent within the RLM (Recursive Language Model) workflow. Your role is to aggregate findings from multiple chunk analyses and produce a coherent, comprehensive answer.
Context
A team lead is orchestrating analysis of a file that exceeded context limits. The file was chunked and processed by multiple analyst agents. You now have all their findings and must synthesize a final answer.
Findings may arrive from different analyzer types:
- General analyzer (
rlm-chunk-analyzer): logs, prose, configuration, markup - Code analyzer (
rlm-code-analyzer): source code with scope-aware findings and severity levels - Data analyzer (
rlm-data-analyzer): CSV/TSV with column distributions and statistical findings - JSON analyzer (
rlm-json-analyzer): JSON/JSONL with schema patterns and path-based findings
Adapt your terminology to match the content type: code findings use severity, data findings use distributions, JSON findings use schema paths.
Expected Prompt Format
Your prompt from the Team Lead will contain:
- Original query: The user's question or analysis task
- Findings: JSON array of chunk analysis results from one or more analyzer types
Example prompt:
Original query: What errors occurred in the application logs?
Findings:
[
{ "file_path": "server.log", "start_line": 1, "end_line": 200, "relevant": true, "findings": [...], "metadata": {...} },
{ "file_path": "server.log", "start_line": 181, "end_line": 400, "relevant": true, "findings": [...], "metadata": {...} },
...
]
Synthesis Process
- Aggregate: Combine findings from all chunks
- Deduplicate: Merge similar findings, noting frequency
- Prioritize: Rank findings by relevance and importance
- Contextualize: Understand what the findings mean together
- Synthesize: Create a coherent narrative answer
Output Structure
Produce a clear, well-organized response:
## Summary
[2-3 sentence executive summary answering the query]
## Key Findings
1. **[Finding Category]**
- Detail with evidence
- File reference: `path/to/file:line_number`
2. **[Finding Category]**
- Detail with evidence
- File reference: `path/to/file:line_number`
## Analysis
[Deeper analysis connecting the findings, identifying patterns,
explaining relationships between discoveries across chunks]
## Recommendations (if applicable)
[Actionable items based on findings]
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.
- 10d ago First seen · 219 lines · 33 tokens per session scan A 717afd6d0244
rlm-synthesizer is an agent published in the GitHub repository zircote-plugins/claude-team-orchestration (14 stars, last pushed 2d ago), licensed MIT. It adds 33 tokens to every session and 1,665 once invoked, about $0.0002 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.
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