research-ops

Use it when:: You ask for something current, a comparison, a look-up on people or companies, or a question you'll keep asking.
It does:: Sorts the ask into a quick fact, a comparison, an enrichment pass or a monitoring candidate, then takes the lightest search that answers it.
You get:: An answer split into evidence, inference and recommendation, with dates on time-sensitive claims and a call on whether it should become a monitor.
Unlike ECC's deep-research:: This one picks which research skill to use and how to label the result, and escalates to that one for multi-source synthesis.
Works with:: ECC's exa-search, deep-research, market-research, lead-intelligence and knowledge-ops skills.

It routes searches to ECC's exa-search, deep-research, market-research and lead-intelligence skills; install those for its full stack.

TL;DR

An operator layer over ECC's research skills. The agent first sorts what you already gave it into established facts, things to verify and open questions, so it doesn't restart from zero. It picks a lane: a quick factual answer, a comparison or decision memo, a people or company enrichment pass, or a question likely to recur. Then it takes the lightest path that works: exa-search for fast discovery, deep-research when synthesis across sources matters, market-research when the result should be a recommendation, lead-intelligence when the real ask is ranking targets, and knowledge-ops to store the result afterwards. It skips a heavy research pass when local code or docs already answer the question. Every important claim is labelled as a sourced fact, user-supplied context, inference or recommendation, and freshness-sensitive answers carry concrete dates. If you're likely to ask the same question again, it says so and recommends a monitor instead of repeating the search by hand.

Summarizes the skill at d2ef268.

npx skills add https://github.com/affaan-m/ecc --skill research-ops

skills.sh page Source on GitHub All skills from affaan-m/ecc

Total Downloads: 5,400 on skills.sh, Nov 9, 2026
Repo stars: 299,776 affaan-m/ecc
First seen: May 19, 2026 on skills.sh

Weekly Downloads

446

+73% over the 8 weeks to Nov 9

Weekly values

Week to	Installs
Sep 21	258
Sep 28	160
Oct 5	139
Oct 12	231
Oct 19	408
Oct 26	400
Nov 2	470
Nov 9	446

Category	category:writing category:browser category:software-engineering Articles, docs, internal comms, research write-ups and prose that doesn't read as generated. Drive browsers, scrape pages and search the web. Everything for building software: planning, coding, debugging, design, frameworks, cloud, agents and skills.
Type	type:workflow A multi-step process the agent follows: interview, debug, run a TDD loop, review.
Effect	effect:none Adds no side effects of its own. It shapes how the agent thinks, writes or reviews. Guidelines and routers are always effect:none, even when the task they support edits code.
Needs	needs:none Instructions only. Works in any agent that supports skills.
Publisher	by:community Everyone else. Unverified vendor claims default to community.
Topics	research web-search

SKILL.md

From affaan-m/ecc at d2ef268. Checked daily, last changed Nov 6, 2026.

Frontmatter

name: research-ops
description: 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.
metadata:
  origin: ECC

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

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