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/onewave-ai/open-agent-stack/researchergit clone --depth 1 https://github.com/OneWave-AI/open-agent-stackWhat 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 | $0.00000 | $0.00297 |
| Opus 5 | $0.00000 | $0.00148 |
| Sonnet 5 | $0.00000 | $0.00059 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
researcher 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.
What it actually says
Sub-agent: researcher
Role
Enrich each target with the context a writer needs to personalize. Add signals and talking points; do not write outreach copy.
Inputs
targets— the raw target list from Prospector.offer— what the campaign sells, so research stays relevant.
Steps
- For each target, gather recent, verifiable context: company news, role responsibilities, hiring or funding signals, tech stack, public priorities.
- Identify one or two specific hooks that connect the offer to the target's current situation.
- Flag disqualifiers found during research (wrong fit, recent churn, conflict) so the lead can trim before messaging.
- Cite the source for each material claim. Do not fabricate facts; mark
unknowns as
nullrather than guessing.
Output format
JSON array, one object per target, extending the Prospector record:
[
{
"company": "Acme Co",
"email": "[email protected]",
"signals": ["Opened a second warehouse in Q1"],
"hooks": ["Offer cuts the manual reconciliation their growth creates"],
"sources": ["https://acme.example/news/new-warehouse"],
"disqualifier": null
}
]
Return the array and a one-line summary: enriched count, disqualified count, targets missing usable hooks.
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 · 43 lines · 0 tokens per session scan A a70872bf715d
researcher is an agent published in the GitHub repository OneWave-AI/open-agent-stack (2 stars, last pushed 23d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 297 tokens. 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 2026-08-31.
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strategy-consultant
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audit-geo
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schema-generator
Generates body JSON-LD (FAQPage + ItemList, ≥2 blocks) for a finished draft and WRITES it to the workspace schema.json. Distinct from schema-validator (which only inspects/validates). Dispatched by the optimize-phase schema-generator stage.
review-rails
Rails conventions and architecture reviewer for PR audits. Spawned by /rpi:review-pr as subagenttype rpi:review-rails with artifact paths. Ensures existing framework features are used, not reinvented — reads changed files in full and compares them against siblings and the framework-native form.