feat: add hermes-generated ops skills

skills/hermes-generated/research-ops/SKILL.md

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+---
+name: research-ops
+description: Evidence-first research workflow for Hermes. Use when answering current questions, evaluating a market or tool, enriching leads, or deciding whether a request should become ongoing monitored data collection.
+metadata:
+  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
+- `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 task sounds recurring enough that a scraper or scheduled monitor may be better than a one-off search
+
+## Workflow
+
+1. Classify the ask before searching:
+   - quick factual answer
+   - decision memo or comparison
+   - lead list or enrichment
+   - recurring monitoring request
+2. 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
+3. 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
+4. Separate fact from inference:
+   - label sourced facts clearly
+   - label inferred fit, ranking, or recommendation as inference
+   - include dates when freshness matters
+5. 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
+6. Report with evidence:
+   - 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 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 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
+- freshness-sensitive answers include concrete dates when relevant
+- recurring-monitoring recommendations state whether the task should remain manual or graduate to a scraper/workflow