research-ops
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.
Best use case
research-ops is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.
Teams using research-ops should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/research-ops/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How research-ops Compares
| Feature / Agent | research-ops | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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-search` for fast current-web discovery - `deep-research` for multi-source synthesis with citations - `market-research` when the end result should be a recommendation or ranked decision - `lead-intelligence` when the task is people/company targeting instead of generic research - `knowledge-ops` when 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-search` for fast discovery - escalate to `deep-research` when synthesis or multiple sources matter - use `market-research` when the outcome should end in a recommendation - hand off to `lead-intelligence` when the real ask is target ranking or warm-path discovery ### 4. Report with explicit evidence boundaries For important claims, say whether they are: - sourced facts - user-supplied context - inference - recommendation Freshness-sensitive answers should include concrete dates. ### 5. Decide whether the task should stay manual If the user is likely to ask the same research question repeatedly, say so explicitly and recommend a monitoring or workflow layer instead of repeating the same manual search forever. ## Output Format ```text QUESTION TYPE - factual / comparison / enrichment / monitoring EVIDENCE - sourced facts - user-provided context INFERENCE - what follows from the evidence RECOMMENDATION - answer or next move - whether this should become a monitor ``` ## Pitfalls - do not mix inference into sourced facts without labeling it - do not ignore user-provided evidence - do not use a heavy research lane for a question local repo context can answer - do not give freshness-sensitive answers without dates ## Verification - important claims are labeled by evidence type - freshness-sensitive outputs include dates - the final recommendation matches the actual research mode used
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