research-ops
by affaan-mresearch-ops is an evidence-first workflow for current-state research in ECC. Use it to find fresh facts, compare options, enrich people or companies, and produce recommendations grounded in public evidence plus any local context. It works as a routing layer for web research, citations, and repeatable decision support.
This skill scores 78/100, which means it is a solid listing candidate for Agent Skills Finder. It gives directory users enough signal to decide that it is a current-evidence research workflow with clear trigger conditions and related skill handoffs, though it would benefit from more explicit execution details and supporting files.
- Clear triggerability: it says to use the skill for current facts, comparisons, enrichment, recommendations, and recurring monitoring workflows.
- Operational guidance is present: it names the companion ECC skills to combine with it, including exa-search, deep-research, market-research, lead-intelligence, and knowledge-ops.
- Good install decision value: the frontmatter is valid, the body is substantial, and the skill frames itself as an operator wrapper around a research stack rather than a vague prompt.
- The repository has no supporting scripts, references, resources, or rule files, so users mostly rely on SKILL.md alone.
- The excerpt shows guardrails but the cut-off section suggests the workflow may not be fully explicit end-to-end, which leaves some room for execution guesswork.
Overview of research-ops skill
What research-ops does
The research-ops skill is an evidence-first workflow for current-state research in ECC. It helps you turn a vague “look this up” request into a structured process for finding fresh facts, comparing options, enriching people or companies, and producing a recommendation grounded in current public evidence plus any local context you provide.
Who should install it
Install the research-ops skill if you routinely need the research-ops for Web Research: fast discovery, multi-source synthesis, or decision support that depends on up-to-date information. It fits analysts, operators, sales/research teams, and assistants that need to separate sourced facts from inference instead of relying on stale memory.
What makes it different
This is not a standalone research engine. It is the operator wrapper around ECC research skills such as exa-search, deep-research, and market-research, with optional handoff to lead-intelligence and knowledge-ops. That makes it useful when you care about workflow selection, citation discipline, and turning repeat research into a monitored process.
How to Use research-ops skill
Install and wire it into your workflow
Run the research-ops install command from the repo context, then verify the skill is available alongside your other ECC skills. Since the repository is skill-only, the main value comes from using it as a routing layer: it tells the agent which research tool to use first and when to escalate from discovery to synthesis to recommendation.
Give it a research brief, not a topic
A weak prompt is: “Research Acme.” A stronger research-ops usage prompt is: “Research Acme’s current product positioning, pricing changes, and recent customer signals. Use fresh public sources only, cite what is fact vs inference, and return a concise recommendation on whether they are a fit for enterprise outreach.” The skill performs best when you specify:
- the decision you are trying to make
- the time sensitivity
- the source constraints
- the output format you want
Read the right files first
For practical setup, start with SKILL.md and then inspect any supporting ECC skill docs that the workflow references, especially README.md or related skill instructions in the wider repo. In this repository, the key information is concentrated in the main skill file, so the fastest path is to read the stack, the “When to Use” guidance, and the guardrails before prompting.
Use a two-pass workflow
For better research-ops usage, ask for:
- a discovery pass to identify the best current sources and what is still uncertain
- a synthesis pass that turns those sources into a comparison, enrichment, or recommendation
This reduces guesswork and makes it easier to catch gaps before the final answer is locked in.
research-ops skill FAQ
Is research-ops a replacement for a normal prompt?
No. A normal prompt can ask for research, but the research-ops skill adds process: it helps choose the right ECC research tool, enforce fresh-source discipline, and keep facts, evidence, and recommendations separated.
When should I not use it?
Do not use research-ops when the answer is stable, purely internal, or does not benefit from current public evidence. It is also a poor fit if you want a quick opinion without citations or if the task is already covered by a dedicated retrieval workflow.
Is it beginner-friendly?
Yes, if you can describe your goal clearly. The main learning curve is not the skill itself; it is giving a complete brief. If you state the decision, the audience, the date sensitivity, and the desired output, the skill is easy to use.
How does it fit the ECC ecosystem?
It is designed to coordinate with ECC-native research skills rather than compete with them. Use research-ops to decide the path, then let the specialized skill handle the actual search, synthesis, or recommendation step.
How to Improve research-ops skill
Start with the outcome you need
The fastest way to improve research-ops results is to name the end state up front: “shortlist,” “comparison table,” “risk memo,” “lead profile,” or “current-state summary.” That helps the workflow choose the right depth and prevents generic web research from drifting.
Add constraints that change the answer
Better inputs include geography, date range, audience, budget, source quality, and what must be excluded. For example: “Use only sources from the last 90 days,” “prioritize primary sources,” or “ignore forum posts.” These constraints materially improve the research-ops guide behavior and reduce noisy output.
Ask for explicit source handling
One common failure mode is mixing facts, assumptions, and recommendations. Improve results by requesting a structure like:
- sourced facts
- user-supplied evidence
- inference
- recommendation
That format makes the research easier to trust and easier to reuse.
Iterate after the first pass
If the first result is too broad, narrow the question to one decision variable and rerun. If it is too shallow, ask for more source diversity, stronger comparison criteria, or a monitored follow-up workflow. The research-ops skill improves most when you tighten scope instead of asking it to “do more” in the abstract.