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 cybernetix-lab/moss-harness --skill research-opsgit clone --depth 1 https://github.com/cybernetix-lab/moss-harnessWrote 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/cybernetix-lab/moss-harness/research-ops)<a href="https://agentmods.dev/skills/cybernetix-lab/moss-harness/research-ops"><img src="https://agentmods.dev/badge/skills/cybernetix-lab/moss-harness/research-ops/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/cybernetix-lab/moss-harness/research-ops"><img src="https://agentmods.dev/badge/skills/cybernetix-lab/moss-harness/research-ops.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 | $0.00039 | $0.00759 |
| Opus 5 | $0.00019 | $0.00380 |
| Sonnet 5 | $0.00008 | $0.00152 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
research-ops 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- research-ops — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Ops
Use this when the user asks to research something current, compare options, enrich people or companies, or turn repeated lookups into a monitored workflow.
This is the operator wrapper around the repo's research stack. It is not a replacement for deep-research, exa-search, or market-research; it tells you when and how to use them together.
Skill Stack
Pull these ECC-native skills into the workflow when relevant:
exa-searchfor fast current-web discoverydeep-researchfor multi-source synthesis with citationsmarket-researchwhen the end result should be a recommendation or ranked decisionlead-intelligencewhen the task is people/company targeting instead of generic researchknowledge-opswhen the result should be stored in durable context afterward
When to Use
- user says "research", "look up", "compare", "who should I talk to", or "what's the latest"
- the answer depends on current public information
- the user already supplied evidence and wants it factored into a fresh recommendation
- the task may be recurring enough that it should become a monitor instead of a one-off lookup
Guardrails
- do not answer current questions from stale memory when fresh search is cheap
- separate:
- sourced fact
- user-provided evidence
- inference
- recommendation
- do not spin up a heavyweight research pass if the answer is already in local code or docs
Workflow
1. Start from what the user already gave you
Normalize any supplied material into:
- already-evidenced facts
- needs verification
- open questions
Do not restart the analysis from zero if the user already built part of the model.
2. Classify the ask
Choose the right lane before searching:
- quick factual answer
- comparison or decision memo
- lead/enrichment pass
- recurring monitoring candidate
3. Take the lightest useful evidence path first
- use
exa-searchfor fast discovery - escalate to
deep-researchwhen synthesis or multiple sources matter - use
market-researchwhen the outcome should end in a recommendation - hand off to
lead-intelligencewhen the real ask is target ranking or warm-path discovery
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.
- 10d ago First seen · 113 lines · 39 tokens per session scan A f6006b8554c6
research-ops is a skill published in the GitHub repository cybernetix-lab/moss-harness (135 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 759 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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