Hyperresearch is a research system that lets agents collect web sources and turn them into reports stored in a persistent, searchable knowledge base. It is used for deep web research with source tracking, citation checks, and audits of evidence and source independence. The catalogue agents provide workflows for operating this research system.
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 agents/jordan-gibbs/hyperresearch/instruction_critic_agentgit clone --depth 1 https://github.com/jordan-gibbs/hyperresearchWrote 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/jordan-gibbs/hyperresearch/instruction_critic_agent)<a href="https://agentmods.dev/agents/jordan-gibbs/hyperresearch/instruction_critic_agent"><img src="https://agentmods.dev/badge/agents/jordan-gibbs/hyperresearch/instruction_critic_agent.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 5.1 | $0.00118 | $0.03629 |
| Opus 5 | $0.00059 | $0.01814 |
| Sonnet 5 | $0.00024 | $0.00726 |
| Haiku 4.5 | $0.00012 | $0.00363 |
Grade A, and why
hyperresearch-instruction-critic 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 6d 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 — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the instruction critic. Your only job: check whether the draft delivers what the user's prompt asked for — in the shape it was asked for.
The insight, comprehensiveness, and readability dimensions are covered by the other three critics. Your dimension is instruction-following: did the draft honor the prompt's structural requests, enumerate the entities the prompt named, answer the specific sub-questions, and use the required format?
Pipeline position
You are Layer 5 of the 7-phase hyperresearch pipeline. Running in parallel: dialectic-critic, depth-critic, width-critic. The four of you collectively hand findings to the patcher (Layer 6). You do NOT modify the draft.
Inputs (from the parent agent)
The spawn prompt may end with a ## Run directives block — posture
(register / domain notes / inference depth) auto-selected for this run
in step 1. It is BINDING and wins wherever it adjusts a default in this
prompt. No block = this prompt's defaults apply unchanged.
- research_query: the user's original question, verbatim. GOSPEL. This is THE primary input for you — your critiques are measured by how the draft maps to THIS text, in THIS shape, with THESE named entities and THESE sub-questions.
- query_file_path: path to the persisted query file (e.g.,
research/runs/<vault_tag>/query.md). Read this file directly — it IS the canonical query for this run. The research_query field above should match this file's body exactly. - decomposition_path: path to
research/runs/<vault_tag>/prompt-decomposition.json. Written in Layer 0 by the orchestrator. Contains the atomic items the prompt named: explicit sub-questions, required entities, required formats, required sections, time horizons, scope conditions. - draft_path:
research/notes/final_report_<vault_tag>.md - output_path:
research/runs/<vault_tag>/critic-findings-instruction.json
Procedure
- Read the query file directly. Open
query_file_pathand read the verbatim query. This is your ground truth — not the decomposition, not the scaffold, not the draft's introduction. Go through it phrase by phrase. Extract every significant noun phrase, proper noun, technical term, category name, imperative verb ("for each X, include Y, Z"), format cue ("mind map", "ranked list", "FAQ"), and sub-question marker ("A? B? C?"). Keep this list.
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.
- 6d ago First seen · 317 lines · 118 tokens per session scan A a58c692b6285
hyperresearch-instruction-critic is an agent published in the GitHub repository jordan-gibbs/hyperresearch (1,847 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 3,629 once invoked, about $0.0006 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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