Oma Market logo

Oma Market

OrganizationPopular
first-fluke
oma-market

Research customer pain points, trends, and competitors through the OMA market engine. Use for market discovery or voice-of-customer analysis.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-market
Stars
1.3K
Forks
149
Bundled files
8
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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Market 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.
  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/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-market .claude/skills/oma-market
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Market 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 Oma Market 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 Oma Market 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.

Market Research Agent - Community Signal Intelligence

Scheduling

Goal

Run the upstream last30days research engine (always the latest release, managed by oma) for community-signal research, then frame the result for the user's intent (pain / trend / competitor / discovery) with strategic frameworks and save one brief under .agents/results/market/.

Intent signature

  • User asks about pain points, user complaints, or voice-of-customer signals for a product or category.
  • User asks what is trending, growing, or declining in a space this week or month.
  • User asks how one product compares to another in community sentiment or positioning.
  • User asks for discovery or exploratory market research on a topic, a person, a company, or a ticker.

When to use

  • Extracting real user pain points from community posts (Reddit with real upvotes and top comments, HN, X, Bluesky, GitHub Issues)
  • Detecting trends in a category over a window (--days 7|30|90|180)
  • Competitor sentiment analysis and SWOT / Porter's 5F positioning
  • Open-ended discovery research (--discover), person mode, hiring signals (--hiring-signals), follow-up drills (--drill)

When NOT to use

  • General web research without market framing -> use oma-search directly
  • Academic literature -> use oma-scholar
  • Live dashboards or scheduled monitoring -> oma schedule <action> wrapping this skill

Expected inputs

  • Topic string; optional --intent pain|trend|competitor|discovery (else classified per resources/intent-rules.md)
  • Optional window (--days), --vs <entity> (competitor), --frameworks auto|none|swot,5f,pestel
  • Any native last30days flag (see oma market run --help) — passed through verbatim

Expected outputs

  • Single markdown brief at .agents/results/market/{topic-slug}-{YYYYMMDD}.md
  • First line: the engine's badge (🌐 last30days v{VERSION} · synced {date}); body per the upstream OUTPUT CONTRACT; framework sections appended per intent; engine footer preserved
  • Raw engine artifacts under market.save_dir (default .agents/results/market/raw/)
yaml
outputs:
  - name: market-brief
    description: Single LAW-compliant markdown brief with framework sections
    artifact: ".agents/results/market/*.md"
    required: true

Dependencies

  • oma market resolve / oma market run — engine location, Python 3.12+ resolution, --save-dir default
  • The upstream SKILL.md at the resolved engine root (skillMd in oma market resolve --json) — the authoritative research contract
  • resources/intent-rules.md, resources/frameworks/, resources/output-laws.md, resources/execution-protocol.md

Control-flow features

  • oma market detect-trap gate before anything else (exit 2 = REFUSE, exit 4 = invalid)
  • Engine is always the latest release: oma market resolve refreshes the managed copy (throttled) and falls back to the cached copy offline; a pinned market.path / LAST30DAYS_HOME opts out
  • Sources needing keys/cookies auto-skip inside the engine; keyless sources (Reddit, HN, GitHub, Polymarket, arXiv, Techmeme, Digg, web) always run
  • Framework auto-toggle by intent (pain/trend → SWOT; competitor → SWOT + Porter's 5F; discovery → SWOT + PESTEL)

Structural Flow

Entry

  1. Run oma market detect-trap "<topic>". Exit 2 → surface the REFUSE reason and reframe suggestion, stop.
  2. Run oma market resolve --json. ok: false → report reason (missing engine → oma market update; missing Python → the install hint) and stop. Never fall back to WebSearch-only synthesis and present it as market research.
  3. Read the upstream contract at engine.skillMd top to bottom. It is long by design; do not skim. Treat engine.root as its SKILL_DIR.
  4. Classify intent per resources/intent-rules.md; map to engine flags and framework set.

Scenes

  1. PREPARE: detect-trap, resolve, read upstream SKILL.md, classify intent.
  2. UPSTREAM STEPS: follow the upstream SKILL.md exactly — Step 0 (first-run setup wizard, consent-driven), intent parsing, Step 0.45 (its own query-quality preflight), Step 0.5 / 0.55 (handle, subreddit, hashtag resolution when WebSearch is available), Step 0.75 (query plan). Skip only its "Runtime Preflight" Python-hunt block: oma market run already resolved the interpreter.
  3. RUN: wherever the upstream contract says "${LAST30DAYS_PYTHON}" "${SKILL_DIR}/scripts/last30days.py" <args>, run oma market run <args> with the same arguments (foreground, 5-minute timeout, --emit=compact). --save-dir is added automatically from market.save_dir unless you pass one.
  4. SYNTHESIZE: produce the brief exactly as the upstream OUTPUT CONTRACT dictates (badge first line, Ranked Evidence Clusters, LAWs). Then append the framework sections selected for the intent, using only clusters present in the engine output as evidence (resources/frameworks/).
  5. FINALIZE: run the self-check in resources/output-laws.md, write .agents/results/market/{topic-slug}-{YYYYMMDD}.md, preview the first 50 lines.

Transitions

  • --vs <entity> or "A vs B" phrasing → competitor intent → upstream COMPARISON flow (two passes + head-to-head as its contract specifies) → SWOT + Porter's 5F.
  • Person / company / ticker topics → upstream person / hiring-signals / StockTwits handling applies unchanged.
  • engine.status: stale → include the note in the report (research ran on the cached engine version).

Failure and recovery

  • detect-trap exit 2 → REFUSE; do not run the engine; --force only on explicit user reconfirmation.
  • oma market resolve not ok → stop with the reason; no engine run.
  • Engine non-zero exit → report stderr verbatim; do not synthesize from partial stdout unless the upstream contract says the emitted compact output is still valid.
  • Upstream Python-version gate / setup wizard messages → relay to the user exactly as the upstream contract instructs.

Exit

  • Success: brief written with badge, clusters, frameworks, and engine footer; path reported.
  • Partial: engine ran with skipped sources (footer lists them) — say so; never pad with invented evidence.

Logical Operations

Actions

ActionSSL primitiveEvidence
detect-trap preflightVALIDATETopic arg, trap pattern rules
Resolve engine + PythonCALL_TOOLoma market resolve --json
Read upstream contractREADengine.skillMd
Classify intentSELECTresources/intent-rules.md
Upstream pre-research stepsINFERUpstream SKILL.md Steps 0–0.75
Run engineCALL_TOOLoma market run <args>
Synthesize + frameworksWRITEUpstream OUTPUT CONTRACT, resources/frameworks/
Self-check + write briefWRITEresources/output-laws.md, .agents/results/market/

Tools and instruments

  • oma market detect-trap <topic> (preflight gate)
  • oma market resolve [--refresh|--offline] [--json] (engine + Python resolution; managed latest)
  • oma market update (force-refresh the managed engine)
  • oma market run <engine args…> (passthrough to the resolved upstream engine’s Python entry point)

Canonical command path

bash
TOPIC="VS Code pain points"
oma market detect-trap "$TOPIC"
oma market resolve --json            # read .engine.skillMd, then follow it
# … upstream Steps 0 / 0.45 / 0.5 / 0.55 / 0.75 …
oma market run "$TOPIC" --plan "$QUERY_PLAN_FILE" --subreddits=vscode --emit=compact --save-suffix=v3

Resource scope

ScopeResource target
NETWORKInside the engine only (its per-source fetchers); GitHub for the managed engine refresh
LOCAL_FS~/.cache/oma-market/last30days/<tag>/ (engine), ~/.config/last30days/ (engine config, keys), .agents/results/market/ (brief + raw)
PROCESSoma market subcommands → the resolved upstream Python entry point

Preconditions

  • Topic passes detect-trap.
  • oma market resolve is ok (engine present; Python ≥ 3.12 found on PATH, via uv, or pinned with market.python / LAST30DAYS_PYTHON).

Effects and side effects

  • Writes the brief to .agents/results/market/{topic-slug}-{YYYYMMDD}.md and raw engine files to market.save_dir.
  • First run: the upstream setup wizard may write ~/.config/last30days/.env (with user consent) and, when Python 3.12 is absent but uv exists, may install a managed CPython 3.12 (~28 MB) after telling the user.

References

  • Execution protocol: resources/execution-protocol.md
  • Intent routing: resources/intent-rules.md
  • Output contract: resources/output-laws.md
  • Applicable framework: resources/frameworks/swot.md, resources/frameworks/porters-5f.md, or resources/frameworks/pestel.md (load only the selected framework)
  • Validation: resources/checklist.md
  • Recovery: resources/error-playbook.md
  • Upstream engine instructions: read the resolved engine.skillMd path from oma market resolve --json; upstream scripts are managed engine files, not bundled skill resources.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Oma Market AI skill do?

Research customer pain points, trends, and competitors through the OMA market engine. Use for market discovery or voice-of-customer analysis.

Why use Oma Market on TypingMind?

Because you install it once and use it with any model. Oma Market 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 Oma Market in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-market. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Oma Market?

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 Oma Market?

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

Is the Oma Market AI skill free?

Yes. It is published on GitHub by first-fluke 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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