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Omh Research Brief

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rlaope
omh-research-brief

[omh] Business question on market, competitors, pricing: business research brief - turns a market, competitor, pricing, or customer question into a structured evidence-vs-inference brief; for raw link gathering use ulw-research. Use when the user says: research-brief, business-research, business research, research brief, decision brief, pricing decision brief, decision-ready brief, source-backed business research.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-research-brief
Stars
3.6K
Forks
269
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Research Brief AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-research-brief
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-research-brief .claude/skills/omh-research-brief
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Research Brief in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Research Brief on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Research Brief is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Research Brief

This is an OMH research-brief workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

research-brief exists to keep research work explicit, evidence-backed, and inside the Hermes/executor boundary instead of relying on ad hoc chat narration.

Do Not Use When

  • The user needs to decide whether a customer problem deserves product investment with evidence typing and customer re-entry; use omh-product-discovery-validation.
  • The request is only fresh links, citations, or current facts without a business question or decision audience; use ulw-research.
  • Sources have not yet been selected and the user wants source types, candidates, or acquisition state; use omh-source-finder.

Examples

Good example:

  • Prompt: research-brief: compare three onboarding analytics vendors using customer notes and confidence gaps.
  • Expected behavior: Prepare a source-backed brief with evidence, inference, confidence, and retrieval gaps separated.
  • Why: The user needs business research synthesis, not recurring operations or coding.

Bad example:

  • Prompt: research-brief: treat casual chat or unaccepted work as if this workflow already produced verified results.
  • Expected behavior: Ask a clarification question or route to a narrower workflow instead of forcing research-brief.
  • Why: The request lacks the required inputs or would overclaim work that Hermes did not observe.

Completion Checklist

  • The research question, source boundaries, recency assumptions, and confidence level are named.
  • Observed sources, inference, synthesis, and unresolved retrieval gaps are separated.
  • Follow-up planning or handoff uses the research summary without calling it execution evidence.
  • Each evidence row that cites a ledger source carries the id as [n] and its quote, and the citation check is reported from an observed ledger verify run that cited at least one source, or as not_observed.

Recovery Notes

  • If sources cannot be accessed, state the retrieval gap and use only observed local context.
  • If evidence is thin or one-sided, lower confidence and ask for a narrower source boundary.

When to Use

Use when Hermes should scope a business question, gather or summarize source-backed evidence, and preserve evidence/inference boundaries before strategy or handoff.

Strong routing signals: `research-brief`, `business-research`, `business research`, `research brief`, `decision brief`, `pricing decision brief`, `decision-ready brief`, `source-backed business research`, `customer feedback trends`, `feedback trends`, `market evidence`, `data search`, `source scan`, `자료 조사`, `데이터 서치`, `근거 조사`, `피드백 추세`, `고객 피드백 추세`

Catalog Metadata

Category: research Phase: business-brief Quality tier: source-gated Reasoning demand: standard

Quality bar:

  • State the research question, source boundaries, and recency assumptions before synthesis.
  • Record each material claim as a compact evidence row: claim, source, source class (upstream official, practitioner heuristic, or unattributed), source date, confidence, and unresolved conflict.
  • Keep claims that lack corroboration in an explicit unresolved list instead of asserting or silently dropping them.
  • Separate observed sources, source quality, source diversity, inferred trends, and unresolved uncertainty.
  • When Hermes' bundled grounded-citations skill is available, load it and register each source in its ledger when it is retrieved (scripts/sources.py add), and carry two more fields on every evidence row: the ledger id written as [n] - a bare number in a cell is not a citation to verify, so that row would go unchecked - and the verbatim quote attached with sources.py quote <id> --text <quote> --from <page text file>, which rejects wording that file does not contain. Keep the run's ledger in a file of its own, passed as --ledger <path> before the subcommand on every call: the default ledger is shared by the whole profile, and another run's reset rebinds its ids. A row whose quote could not be attached says so, and a claim that rests on model knowledge is marked [unverified] and listed as unresolved, never entered as evidence. Run sources.py verify <draft> --evidence, since every evidence row is meant to carry its quote, and report the citation check from that run, or as not_observed: the exit status of sources.py verify <draft> is the observed check - 0 is a pass and any other status is not - and verify --evidence also fails a draft whose cited source carries no verbatim quote. An exit 0 whose info: stats: line reads 0 distinct source(s) cited checked nothing and is reported as not_observed; it is a citation check, not fact verification. Without the skill, keep the source and its quoted wording in the row and report the citation check as not_observed.
  • Use the brief to feed strategy or meeting work without calling it execution evidence.
  • For a market question, fix the decision audience, buyer/segment/geography, source cutoff, period/currency/base year, units and denominator before synthesis; leave missing values unknown. Use market-research for scoped TAM/SAM/SOM, customer/JTBD evidence, and comparable competitor/pricing methods instead of improvising a market model inside the brief.
  • Write each finding with source IDs and dates, observed, vendor-asserted, or inferred status, assumptions, decision implication, contrary evidence, confidence limit, and next verification owner. Keep failed-source coverage visible, not a claim that no evidence exists; lead with what changes the decision and attach only relevant method worksheets.
  • A market brief is not customer validation or an investment approval; product-discovery-validation retains the kill/pivot/persevere gate, and outreach, participant recruitment, payments, or scheduling each need separate explicit approval.

Required inputs:

  • business question
  • source boundary
  • recency or market scope

Expected outputs:

  • evidence table
  • inference summary
  • confidence and uncertainty

Artifact expectations:

  • research brief or source ledger when the wrapper captures observed sources

Safety rules:

  • Do not claim sources were fetched unless Hermes or the wrapper observed them.
  • Separate evidence, inference, confidence, source diversity, and missing-source gaps.
  • Route later implementation separately through an accepted plan and coding handoff.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Record actual tool results, or not_observed / not_available, in the record; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Reply in the user's own words and the host's own voice: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Frequently asked questions

What does the Omh Research Brief AI skill do?

[omh] Business question on market, competitors, pricing: business research brief - turns a market, competitor, pricing, or customer question into a structured evidence-vs-inference brief; for raw link gathering use ulw-research. Use when the user says: research-brief, business-research, business research, research brief, decision brief, pricing decision brief, decision-ready brief, source-backed business research.

Why use Omh Research Brief on TypingMind?

Because you install it once and use it with any model. Omh Research Brief is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Research Brief in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-research-brief. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Omh Research Brief?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Research Brief?

As many as you like. As long as a model supports skills, you can use Omh Research Brief with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Research Brief AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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