everything-claude-code/skills/hermes-generated/research-ops/SKILL.md

name, description, metadata

name	description	metadata
research-ops	Evidence-first research workflow for Hermes. Use when answering current questions, evaluating a market or tool, enriching leads, comparing strategic options, or deciding whether a request should become ongoing monitored data collection.	hermes tags generated research market discovery monitoring workflow verification

Research Ops

Use this when the user asks Hermes to research something current, compare options, enrich people or companies, or turn repeated lookups into an ongoing monitoring workflow.

Skill Stack

Pull these imported skills into the workflow when relevant:

• deep-research for multi-source cited synthesis
• market-research for decision-oriented framing
• exa-search for first-pass discovery and current-web retrieval
• continuous-agent-loop when the task spans user-provided evidence, fresh verification, and a final recommendation across multiple turns
• data-scraper-agent when the user really needs recurring collection or monitoring
• search-first before building new scraping or enrichment logic
• eval-harness mindset for claim quality, freshness, and explicit uncertainty

When To Use

• user says research, look up, find, who should i talk to, what's the latest, or similar
• the answer depends on current public information, external sources, or a ranked set of candidates
• the user pastes a compaction summary, copied research, manual calculations, or says factor this in
• the user asks should i do X or Y, compare these options, or wants an explicit recommendation under uncertainty
• the task sounds recurring enough that a scraper or scheduled monitor may be better than a one-off search

Workflow

1. Start from the evidence already in the prompt:
   • treat compaction summaries, pasted research, copied calculations, and quoted assumptions as loaded inputs
   • normalize them into user-provided evidence, needs verification, and open questions
   • do not restart the analysis from zero if the user already gave you a partial model
2. Classify the ask before searching:
   • quick factual answer
   • decision memo or comparison
   • lead list or enrichment
   • recurring monitoring request
3. Build the decision surface before broad searching when the ask is comparative:
   • list the options, decision criteria, constraints, and assumptions explicitly
   • keep concrete numbers and dates attached to the option they belong to
   • mark which variables are already evidenced and which still need outside verification
4. Start with the fastest evidence path:
   • use exa-search first for broad current-web discovery
   • if the question is about a local wrapper, config, or checked-in code path, inspect the live local source before making any web claim
5. Deepen only where the evidence justifies it:
   • use deep-research when the user needs synthesis, citations, or multiple angles
   • use market-research when the result should end in a recommendation, ranking, or go/no-go call
   • keep continuous-agent-loop discipline when the task spans user evidence, fresh searches, and recommendation updates across interruptions
6. Separate fact from inference:
   • label sourced facts clearly
   • label user-provided evidence clearly
   • label inferred fit, ranking, or recommendation as inference
   • include dates when freshness matters
7. Decide whether this should stay manual:
   • if the user will likely ask for the same scan repeatedly, use data-scraper-agent patterns or propose a monitored collection path instead of repeating the same manual research forever
8. Report with evidence:
   • group the answer into sourced facts, user-provided evidence, inference, and recommendation when the ask is a comparison or decision
   • cite the source or local file behind each important claim
   • if evidence is thin or conflicting, say so directly

Pitfalls

• do not answer current questions from stale memory when a fresh search is cheap
• do not conflate local code-backed behavior with market or web evidence
• do not ignore pasted research or compaction context and redo the whole investigation from scratch
• do not mix user-provided assumptions into sourced facts without labeling them
• do not present unsourced numbers or rankings as facts
• do not spin up a heavy deep-research pass for a quick capability check that local code can answer
• do not leave the comparison criteria implicit when the user asked for a recommendation
• do not keep one-off researching a repeated monitoring ask when automation is the better fit

Verification

• important claims have a source, file path, or explicit inference label
• user-provided evidence is surfaced as a distinct layer when it materially affects the answer
• freshness-sensitive answers include concrete dates when relevant
• recurring-monitoring recommendations state whether the task should remain manual or graduate to a scraper/workflow