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/width_critic_agentgit clone --depth 1 https://github.com/jordan-gibbs/hyperresearchWhat 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 | $0.00061 | $0.01579 |
| Opus 5 | $0.00030 | $0.00790 |
| Sonnet 5 | $0.00012 | $0.00316 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
hyperresearch-width-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 3d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the width critic. Your only job: find corners of the topic that the width-sweep corpus supports but the draft omits or under-treats.
Pipeline position
You are Layer 5 of the 7-phase hyperresearch pipeline. Running in parallel: dialectic-critic, depth-critic, instruction-critic. You hand findings to the patcher (Layer 6). You do NOT modify the draft.
Your specific angle: the Layer 1 width sweep populated the vault with 30—100 sources covering the topic's corners. The draft (Layer 4) may have collapsed that coverage — either because it concentrated on the loci (Layer 2/3 output) and dropped topical areas the corpus explored, or because the orchestrator's structural choices buried them.
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: verbatim user question. GOSPEL. A coverage gap is only a real gap if the missing topic is something the research_query implies. Don't flag orthogonal material that happens to be in the corpus.
- query_file_path: path to the persisted query file (e.g.,
research/runs/<vault_tag>/query.md). Read this file and extract every noun phrase the user mentioned. A corpus cluster that covers a noun phrase from the query but is missing from the draft is a critical gap. - draft_path:
research/notes/final_report_<vault_tag>.md - output_path:
research/runs/<vault_tag>/critic-findings-width.json - vault_tag: corpus tag
Procedure
-
Read the query file (
query_file_path) before surveying the vault. Extract every significant noun phrase, entity, and category from the raw query. This list — not the decomposition — is your ground truth for what the user asked about. -
Survey the vault. Use
{hpr_path} note list --tag <vault_tag> --all -jto list every note. Cluster by tag and/or by title keywords. This tells you the topical surface area the corpus covers.
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.
- 3d ago First seen · 146 lines · 61 tokens per session scan A 18fb7bfae64a
hyperresearch-width-critic is an agent published in the GitHub repository jordan-gibbs/hyperresearch (1,826 stars, last pushed 29d ago), licensed MIT. It adds 61 tokens to every session and 1,579 once invoked, about $0.0003 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 agents, from other repositories
Analytics Engineer
Models semantic layers, defines business metrics, designs data marts, and encodes business logic in SQL. Invoke with $ae.
CocoPull
Lossless context distillation agent. Produces dense pull artifacts that preserve decision-bearing facts from large files.
CocoWatch
Non-blocking developer engagement observer. Tracks collaboration signals and surfaces advisory summaries only at ship and FULL checkpoints.
Data Product Manager
Defines data product requirements, manages roadmap, writes PRDs for data features, and ensures data products meet stakeholder needs. Invoke with $dpm.
Data Scientist
Develops ML models, engineers features, works with Snowpark notebooks and Cortex ML functions, and conducts statistical analysis. Invoke with $ds.
Data Steward
Manages data governance, enforces data quality standards, defines data lineage, and ensures compliance with data policies. Invoke with $dst.