Customer Research
Nexus-JPF/note-companion
When the user wants to conduct, analyze, or synthesize customer research.
A skill your agent uses when paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot…
Install 51 Audience Research Global
$ npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install minhnv0807/ai-business-skills 51-audience-research-global --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/en/51-audience-research-global .claude/skills/51-audience-research-global && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "51-audience-research-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/51-audience-research-global into .claude/skills/51-audience-research-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "51-audience-research-global", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/51-audience-research-globalType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install minhnv0807/ai-business-skills 51-audience-research-global --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/en/51-audience-research-global .agents/skills/51-audience-research-global && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "51-audience-research-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/51-audience-research-global into .agents/skills/51-audience-research-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "51-audience-research-global", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install minhnv0807/ai-business-skills 51-audience-research-global --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/en/51-audience-research-global .cursor/skills/51-audience-research-global && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "51-audience-research-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/51-audience-research-global into .cursor/skills/51-audience-research-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "51-audience-research-global", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/minhnv0807/ai-business-skills.git --path skills/en/51-audience-research-global--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install minhnv0807/ai-business-skills 51-audience-research-global --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/en/51-audience-research-global .gemini/skills/51-audience-research-global && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "51-audience-research-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/51-audience-research-global into .gemini/skills/51-audience-research-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "51-audience-research-global", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install minhnv0807/ai-business-skills 51-audience-research-globalInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/en/51-audience-research-global .github/skills/51-audience-research-global && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "51-audience-research-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/51-audience-research-global into .github/skills/51-audience-research-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "51-audience-research-global", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install minhnv0807/ai-business-skills 51-audience-research-global --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/en/51-audience-research-global .opencode/skills/51-audience-research-global && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "51-audience-research-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/51-audience-research-global into .opencode/skills/51-audience-research-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "51-audience-research-global", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
51-audience-research-global- GitHub stars
- 609
- Token cost
- ~2.8k tokens
- SKILL.md length
- 1,128 words
- Files
- 1
- Skills in repo
- 142
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot…
- Works in 6 steps: Core audience profile → Psychographics and behavior → Targeting settings per platform → …
- Paid ad AUDIENCES must be researched and defined before launch — target profile
- SKILL.md covers Information gathering, Principles, Workflow and Output structure, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
51 Audience Research Global is an agent skill from minhnv0807/ai-business-skills. Use when paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot tiering, lookalike seeds, exclusions, and ranked targeting hypotheses to test. Trigger on 'audience research', 'who should I target', 'interest targeting', 'Meta audience for this product', 'TikTok targeting ideas', 'my targeting is too broad'. Also use when launch is imminent and the ad set targeting is still empty. Not for — persona and JTBD…
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Marketing & SEO, covering Market research and User stories. It works with TikTok. The repository describes itself as: 138 bilingual AI marketing skills (69 VN + 69 Global) for Claude Code, OpenCode, Codex, VS Code. Four role SOP packs — content, design, performance, leader ops — plus strategy… The licence is MIT.
When your agent uses it
- Paid ad AUDIENCES must be researched and defined before launch — target profile
- Interest and behavior mapping per platform
- Audience sizing
- Lookalike seeds
Example prompts
- “audience research”
- “who should I target”
- “interest targeting”
- “/51-audience-research-global”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 0360adc. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
51 Audience Research Global loads about 2.8k tokens when it runs. Until then it costs about 191 tokens; SKILL.md has 1,128 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from minhnv0807/ai-business-skills at commit 0360adc, republished under its MIT licence (© minhnv0807). 1,128 words, ~2,812 tokens.
Download SKILL.mdSave it as
.claude/skills/51-audience-research-global/SKILL.md (or your agent's skills folder).- name
- 51-audience-research-global
- description
- Use when paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot tiering, lookalike seeds, exclusions, and ranked targeting hypotheses to test. Trigger on 'audience research', 'who should I target', 'interest targeting', 'Meta audience for this product', 'TikTok targeting ideas', 'my targeting is too broad'. Also use when launch is imminent and the ad set targeting is still empty. Not for — persona and JTBD depth for content, see `09-customer-insight-global`; warm retargeting tiers, see `56-retargeting-plan-global`; where the budget goes, see `54-media-plan-global`; the campaign hierarchy, see `52-account-structure-global`.
- metadata.version
- 1.0.1
- metadata.category
- performance
- license
- MIT
- triggers
- audience research, target audience, interest targeting, audience for campaign, who should I target, Meta audience, TikTok audience, lookalike seed
- output
- File .md — audience profile, ready-to-paste targeting settings per platform, cold/warm/hot tiers, lookalike seed brief, and a ranked list of targeting…
- related
- product-marketing-context-global, 09-customer-insight-global, 08-competitor-research-global, 10-reverse-kpi-global, 52-account-structure-global…
Audience Research (Global)
Wrong targeting burns budget no matter how good the copy or creative is. This is the first step of the performance chain: 51 -> 10-reverse-kpi-global -> 54-media-plan-global -> 53-tracking-setup-global -> 52-account-structure-global. If there is no customer insight yet, run 09-customer-insight-global first.
Information gathering
Read .agents/product-marketing-context-global.md and any output from 09-customer-insight-global. If information is missing, ask up to 4 questions:
- What product or service will run ads? Price point and market tier (mass / mid / premium)?
- What existing customer data is available? Age, gender, geography, purchase behavior, past customer list, CRM export, pixel data.
- Which platforms and which markets? Meta / Google / TikTok / YouTube / LinkedIn / Pinterest — and which countries. Primary objective: lead gen / conversion / traffic / awareness?
- Planned budget and target CPA/CPL? If not calculated yet, run
10-reverse-kpi-global.
Principles
- Geography is the biggest cost lever, before interests. Per
references/benchmarks-global.md, Tier 1 markets (US, Canada, Australia, Western EU) run 6-7x the CPM of Tier 2 (SEA, LATAM). US Meta CPM sits at $15-25 vs $2-6 in Brazil/LATAM. Decide the market before debating interest stacks. - Research before copy and before campaign build. Never the reverse.
- Broad first, narrow only with evidence. Modern delivery algorithms optimize well on a wide pool plus strong creative. Narrow only when segment data proves it.
- One clear interest theme per ad set. Stacking many unrelated interests makes it impossible to tell which one worked.
- Write pain points in the customer's own words, not marketer language.
- Size for spend, not for a magic number. A cold ad set must be large enough to spend its daily budget for at least 7 days without frequency passing 2.5. A tight interest stack saturates fast in a Tier 1 market where CPM is $13-20.
- This is a living document. Update it after 3-5 days of live data by comparing CPA per segment.
Workflow
1. Core audience profile
Build from real data (CRM, analytics, order history, platform audience insights) plus 09-customer-insight-global:
| Field | Detail |
|---|---|
| Age | [primary band + secondary band] |
| Gender | [actual split from data, not assumption] |
| Markets | [countries/regions you can actually ship to or serve] |
| Market tier | Tier 1 (US/CA/AU/W.EU) / Tier 2 (SEA/LATAM) / mixed |
| Income / budget band | [must match the price point] |
| Job or role | [primary segment; required for B2B] |
| Primary device | Mobile / Desktop / Both |
| Language | [ad language per market] |
For B2B, add company size, industry, seniority, and buying committee role.
2. Psychographics and behavior
- Top pain points: 3-5, phrased the way customers say them.
- Buying motivation: what they want to gain, what they want to avoid.
- Purchase context: card-first checkout, subscription comfort, review dependence, return-policy sensitivity.
- Online behavior: platforms used, peak hours per market timezone, content formats they engage with.
3. Targeting settings per platform
Only build blocks for platforms that will actually run. Each block should be paste-ready into the ads manager.
- Meta Ads: age, gender, locations (list countries explicitly, exclude where you cannot fulfill); detailed targeting with 10-15 related interests grouped into 2-3 themes for separate testing; behaviors; exclusions (past purchasers, submitted leads); a broad-vs-narrow recommendation. Note whether Advantage+ audience will be used as a control.
- Google Ads: in-market audiences; custom segments built from 10-15 high-intent search terms; affinity; Customer Match if a consented list exists. Search intent beats demographic targeting here.
- TikTok Ads: age, gender, interests, behaviors (recent video interactions), device OS, creator-adjacent targeting.
- YouTube Ads: custom segments from search terms, placements (specific channels/videos), topics, life events.
- LinkedIn Ads (B2B only): job title, function, seniority, company size, industry, member skills, matched company lists. Expect CPM $30-100+ per
references/benchmarks-global.md— validate the economics with10-reverse-kpi-globalbefore committing. - Pinterest Ads: interests, keywords, actalike audiences. Strongest for home, fashion, DIY, and female 25-54.
4. Cold / warm / hot tiers
| Tier | Definition | Signal | Source |
|---|---|---|---|
| Hot | Landing page visit, add to cart, checkout started, form opened, open sales conversation | Pixel/CAPI events, CRM | Meta, Google, TikTok, CRM |
| Warm | Video watched >50%, page or post engagement, link click, follow, email opened | Engagement custom audiences, ESP segments | Meta, TikTok, email platform |
| Cold | Does not know the brand | Interest, behavior, broad, lookalike, search intent | All platforms |
Rule: cold and warm/hot must live in separate campaigns with different messages (see 56-retargeting-plan-global). Always exclude warm and hot from cold campaigns so the data stays clean.
Show full SKILL.md (429 more words)Show less
5. Lookalike and seed audiences
| Seed | Minimum seed size | Lookalike % | Platform | Purpose |
|---|---|---|---|---|
| Past purchasers | >= 100 | 1-3% | Meta | Find people like the best customers |
| Qualified leads | >= 500 | 1-5% | Meta | Scale lead generation |
| Video viewers 75% | >= 1000 | Broad | TikTok | Scale awareness |
| Email / CRM list | >= 300 | 1-5% | Meta, Google Customer Match | Extend from first-party data |
Seed quality beats seed size: purchasers outperform leads, leads outperform viewers. Do not build a lookalike from a seed below the minimum. Any uploaded customer list must have marketing consent on record — see the consent section in 53-tracking-setup-global.
6. Targeting hypotheses to test
One line each, in the form "If we target [X], then [metric] will [Y], because [Z]". Hand these to 19-ab-test-setup-global and 52-account-structure-global.
| # | Hypothesis | Test variable | Metric | Priority |
|---|---|---|---|---|
| 1 | Broad plus strong creative is cheaper than a manual interest stack | Broad vs interest | CPA | High |
| 2 | [Interest theme A] sits closer to the pain than [theme B] | Interest A vs B | CPA, CTR | High |
| 3 | 1% purchaser lookalike converts better than 3% | Lookalike % | CPA, close rate | Medium |
| 4 | [Market X] delivers acceptable CPA despite higher CPM | Geo split | CPA, ROAS | Medium |
| 5 | [Age segment] converts better | Age split | CPA | Low |
Maximum 3-5 test ad sets at once. More than that splits budget too thin to reach a conclusion.
Output structure
File name: audience-research-[product]-[YYYYMMDD].md
# Audience Research — [Product]
Date: [YYYY-MM-DD] · Platforms: [list] · Markets: [countries] · Objective: [Lead/Conversion]
## 1. Core audience profile
| Age | Gender | Markets | Tier | Income band | Role | Device | Language |
## 2. Psychographics and behavior
- Pain points: [3-5, customer wording]
- Motivation: [gain / avoid]
- Purchase context: [payment, reviews, returns, subscription]
- Online behavior: [platforms, peak hours, preferred formats]
## 3. Targeting settings per platform
### Meta: [age/gender/geo/interest themes/behaviors/exclusions/Advantage+ control]
### Google: [...] · TikTok: [...] · LinkedIn: [...] · Pinterest: [...]
## 4. Audience tiers
| Tier | Audience | Signal | Campaign that uses it |
## 5. Lookalike and seed audiences
| Seed | Size | Lookalike % | Platform | Purpose | Consent status |
## 6. Targeting hypotheses (hand to A/B test)
| # | Hypothesis | Variable | Metric | Priority |
## 7. Sizing and overlap notes
- Estimated reach per ad set: [number]
- Days of spend the pool supports at planned budget: [number]
- Overlaps to avoid: [audiences likely to collide]
- Regional CPM expectation per `references/benchmarks-global.md`: [range]After the first live data
- After 3-5 days: compare CPA and lead quality per audience segment. Record winners and losers in the profile.
- Winning audiences expand into similar lookalike or broad pools (see
55-scaling-ads-global). Losing audiences get a written reason so nobody retests the same thing. - Monthly, reconcile the profile against CRM data: which source closes best. Cheap leads are not always good leads.
Related skills
09-customer-insight-global: run first — supplies insight, pain, and customer language.08-competitor-research-global: see who competitors target and with which angles (ad libraries).10-reverse-kpi-global: max CPA and budget before planning.54-media-plan-global: turns this audience map into channel and budget allocation.52-account-structure-global: turns targeting settings into ad set structure.56-retargeting-plan-global: detailed warm/hot tiering and messaging.
Quality checklist
- Profile built from real data (CRM, analytics, surveys), not guesses
- Market tier stated, with the CPM expectation from
references/benchmarks-global.md - Pain points written in customer language, not marketer language
- Targeting settings complete and paste-ready for every platform in the plan
- Three tiers defined (cold/warm/hot) with cross-exclusion rules
- Lookalike seeds meet minimum size and have documented consent
- 3-5 targeting hypotheses, each with a test variable and a metric
- Cold audience large enough to sustain 7 days of planned spend below frequency 2.5
- Plan in place to update the profile after 3-5 days of live data
© minhnv0807, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/en/51-audience-research-global of minhnv0807/ai-business-skills.
Open the folder on GitHubat commit 0360adc
Compare with similar skills
51 Audience Research Global next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| 51 Audience Research Global this skillminhnv0807/ai-business-skills | 609 | — | ~2.8k | Automated safety check: Pass | MIT | |
| Customer ResearchNexus-JPF/note-companion | 870 | 6 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Audience ResearchScrapeCreators/social-media-research-skills | 3.4k | — | ~635 | Automated safety check: Notes | MIT | |
| Comment MiningScrapeCreators/social-media-research-skills | 3.4k | — | ~1k | Automated safety check: Notes | MIT | |
| Customer Researchunifapi-agent/agents | 589 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Trend DiscoveryScrapeCreators/social-media-research-skills | 3.4k | — | ~789 | Automated safety check: Notes | MIT |
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Questions about 51 Audience Research Global
What does 51 Audience Research Global do?
A skill your agent uses when paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot…. 51 Audience Research Global is an agent skill from minhnv0807/ai-business-skills. Use when paid ad AUDIENCES must be researched and defined before launch — target profile, interest and behavior mapping per platform, audience sizing, cold, warm, and hot tiering, lookalike seeds, exclusions, and ranked targeting hypotheses to test.
When should I use 51 Audience Research Global?
51 Audience Research Global fits situations like: paid ad AUDIENCES must be researched and defined before launch — target profile; interest and behavior mapping per platform; audience sizing; lookalike seeds.
How do I install 51 Audience Research Global in Claude Code?
Run `npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a claude-code`. Or copy the skill folder (skills/en/51-audience-research-global in minhnv0807/ai-business-skills) into .claude/skills/51-audience-research-global in your project. Claude Code loads it when a task matches its description.
How do I install 51 Audience Research Global in Codex?
Run `npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a codex`. Or copy the skill folder (skills/en/51-audience-research-global in minhnv0807/ai-business-skills) into .agents/skills/51-audience-research-global in your project. Codex loads it when a task matches its description.
Can I use 51 Audience Research Global in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add minhnv0807/ai-business-skills --skill 51-audience-research-global -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/51-audience-research-global, .gemini/skills/51-audience-research-global, .github/skills/51-audience-research-global and .opencode/skills/51-audience-research-global in your project.
What does 51 Audience Research Global need to run?
SKILL.md names no scripts, command-line tools or credentials: 51 Audience Research Global is instructions for the agent only.
Does 51 Audience Research Global access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is 51 Audience Research Global safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does 51 Audience Research Global use?
51 Audience Research Global is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does 51 Audience Research Global use?
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to 51 Audience Research Global?
Skills that share tags, products or a category with 51 Audience Research Global: Customer Research (Nexus-JPF/note-companion, 870 stars), Audience Research (ScrapeCreators/social-media-research-skills, 3.4k stars), Comment Mining (ScrapeCreators/social-media-research-skills, 3.4k stars) and Customer Research (unifapi-agent/agents, 589 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains 51 Audience Research Global?
minhnv0807 (a GitHub user) maintains it in minhnv0807/ai-business-skills, which has 609 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on September 12, 2026.
Source: minhnv0807/ai-business-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.