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 closedloop-ai/claude-plugins --skill iterative-retrievalgit clone --depth 1 https://github.com/closedloop-ai/claude-pluginsWrote 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/closedloop-ai/claude-plugins/iterative-retrieval)<a href="https://agentmods.dev/skills/closedloop-ai/claude-plugins/iterative-retrieval"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/iterative-retrieval/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/closedloop-ai/claude-plugins/iterative-retrieval"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/iterative-retrieval.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 | $0.00022 | $0.01300 |
| Opus 5.5 | $0.00009 | $0.00520 |
| Sonnet 5.5 | $0.00004 | $0.00260 |
| Haiku 4.5 | $0.00002 | $0.00130 |
Grade A, and why
iterative-retrieval 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iterative Retrieval Skill
This skill enables orchestrators to iteratively refine sub-agent queries through follow-up questions, ensuring sub-agents gather sufficient context before the orchestrator accepts their output. This addresses the problem where orchestrators have semantic context that sub-agents lack, leading to incomplete summaries.
Target Audience
This skill is designed for the /code orchestrator only (code.md slash command). Sub-agents do not use this skill - they simply respond to queries. The orchestrator is responsible for evaluating responses and deciding whether to resume with follow-ups.
Note: Sub-agents do NOT see this skill documentation in their context. Only the orchestrator can invoke and follow the protocol. Sub-agents are unaware they're part of an iterative retrieval loop - they just respond to queries and follow-ups as normal requests.
Usage Model
This skill is optional and opt-in. Not every sub-agent call benefits from iterative refinement - simple lookups or well-defined queries don't need it. Invoke this skill when you anticipate that a sub-agent may return incomplete context due to semantic gaps.
The 4-Phase Protocol
This protocol provides a structured approach to iterative context gathering. All 4 phases represent the recommended workflow, but phases 2-4 are optional if the initial response is sufficient. Exercise judgment - if Phase 2 evaluation shows context is sufficient on first pass, phases 3-4 aren't needed.
Phase 1: Initial Dispatch
Define and dispatch the initial query with full context:
- Define PRIMARY OBJECTIVE: Clearly state what you ultimately need to accomplish
- Formulate INITIAL QUERIES: Specific questions or search criteria for the sub-agent
- Dispatch with BOTH: Send both the queries AND the primary objective to provide semantic context
- Store AGENT ID: Keep the
agent_idreturned from theTask(...)call for potential continuation via SendMessage
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 111 lines · 22 tokens per session scan A 55debbb04138
iterative-retrieval is a skill published in the GitHub repository closedloop-ai/claude-plugins (122 stars, last pushed today), licensed Apache-2.0. It adds 22 tokens to every session and 1,300 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 2026-09-07.
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