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Agent skill

Account Research

by w95 in w95/awesome-claude-corporate-skills

Research a company using Common Room data. An agent skill from w95/awesome-claude-corporate-skills.

MITAuto-check passedSales & Support

Install Account Research

skills CLI
$ npx skills add w95/awesome-claude-corporate-skills --skill account-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install w95/awesome-claude-corporate-skills account-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/05-sales/account-research-common-room .claude/skills/account-research && rm -rf skills-src

Use ~/.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/

Facts

Skill name
account-research
GitHub stars
244
Token cost
~1.5k tokens
SKILL.md length
683 words
Files
2 (incl. references)
Skills in repo
42
Repo updated
First seen
Licence
MIT

At a glance

Research a company using Common Room data. An agent skill from w95/awesome-claude-corporate-skills.

  • Works in 7 steps: Load User Context (Me) → Identify the Interaction Pattern → Look Up the Account → …
  • Research [company]
  • SKILL.md covers Step 0: Load User Context (Me), Step 1: Identify the…, Step 2: Look Up the Account and Step 3: Fetch the Right Fields, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Account Research is an agent skill from w95/awesome-claude-corporate-skills. Research a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/signals-guide.md`).

It sits in Sales & Support, covering Sales call preparation. The repository describes itself as: 166 production-ready Claude AI skills organized by corporate role — executive leadership, finance, HR, marketing, sales, legal, operations, engineering, product, data, customer…. The licence is MIT.

When your agent uses it

  • Research [company]
  • Tell me about [domain]
  • Pull up signals for [account]
  • Whats going on with [company]

Example prompts

  • “research [company]”
  • “tell me about [domain]”
  • “pull up signals for [account]”
  • “/account-research”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Load User Context (Me)
  2. Identify the Interaction Pattern
  3. Look Up the Account
  4. Fetch the Right Fields
  5. Web Search (Sparse Data Only)
  6. Apply Reasoning (Pattern 4)
  7. Produce Output

What it can do on your machine

Read from SKILL.md and the folder at commit 78dbc7c. 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.

    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

Account Research loads about 1.5k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 683 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.2k

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 w95/awesome-claude-corporate-skills at commit 78dbc7c, republished under its MIT licence (© w95). 683 words, ~1,513 tokens.

Download SKILL.mdSave it as .claude/skills/account-research/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
account-research
description
Research a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.

Account Research

Retrieve and synthesize account information from Common Room. Handles four interaction patterns: full overviews, targeted field questions, sparse data situations, and combined MCP data + LLM reasoning.

Step 0: Load User Context (Me)

Before researching any account, fetch the Me object from Common Room. This provides:

  • The user's profile, title, role, and Persona in CR
  • The user's segments ("My Segments")

Default all queries to the user's own segments unless the user explicitly asks for a broader view. This keeps results scoped to their territory.

Step 1: Identify the Interaction Pattern

Determine what the user actually needs before deciding how much data to fetch:

Pattern 1 — Full Overview: "Tell me about Datadog" / "Summarize cloudflare.com" → Fetch the full field set and produce a structured briefing.

Pattern 2 — Targeted Question: "Who owns the Snowflake account?" / "Is acme.io showing buying signals?" / "What's the employee count for notion.so?" → Fetch only the relevant field(s). Return a direct, concise answer — do not produce a full brief for a simple question.

Pattern 3 — Sparse Data: "Tell me about tiny-startup.io" → If Common Room has limited data for an account, say so honestly: "There is limited information available for this account." Never speculate or fill gaps with generic statements.

Pattern 4 — Combined Reasoning: Fetch structured MCP data, then layer in LLM analysis — e.g., "Stripe has 8,000 employees and is hiring heavily for AI roles. Based on your ICP of 1k–10k fintech companies, this is a strong fit."

Step 2: Look Up the Account

Search Common Room for the account by domain or company name. Exact match first; if no result, try partial match and confirm with the user before proceeding.

Step 3: Fetch the Right Fields

Use the Common Room object catalog to see available field groups and their contents. For full overviews, request all field groups. For targeted questions, request only what's relevant.

Key field groups to know about:

  • Scores — always return as raw values or percentiles, never labels
  • Summary research — RoomieAI output; often the richest qualitative signal
  • Top contacts — sorted by score desc; use communityMemberID for full lookups

Choosing what to fetch:

User query typeFields to request
Full account overviewAll field groups
"Who owns this account?"Company profiles & links, CRM fields
"Is this company a good fit?"Key fields, scores, about
"What signals is this account showing?"Scores, summary research, CRM fields
"Who are the top contacts?"Top contacts
"What does RoomieAI say about them?"Summary research, all research
"Find engineers at this account"Prospects (with title filter)
Show full SKILL.md (270 more words)Show less

Step 4: Web Search (Sparse Data Only)

Common Room is the primary data source. Do not run web search when CR returns rich data.

When CR data is sparse (Pattern 3 — few fields returned, no activity, no scores), run a targeted web search to fill gaps:

  • "[company name]" news — scoped to the last 30 days
  • Look for: funding rounds, acquisitions, product launches, executive changes, press coverage

If the user explicitly asks for external context or recent news, run web search regardless of data richness.

Step 5: Apply Reasoning (Pattern 4)

When the user's question invites synthesis — not just data retrieval — layer in analysis:

  • Compare account data to known ICP criteria from session context
  • Identify fit signals (size, industry, tech stack, hiring patterns)
  • Note timing signals (funding, trial status, recent activity spike)
  • Frame insights as clearly derived from data, not assumed

When the user's company context is available (see references/my-company-context.md), position findings relative to the user's value proposition and ICP.

Step 6: Produce Output

Only include sections where Common Room returned actual data. Omit sections entirely rather than filling them with guesses.

Full overview (when data is rich):

## [Company Name] — Account Overview

**Snapshot**
[2–3 sentences: what they do, plan/stage, relationship status]

**Key Details**
[Employee count, industry, location, domain, funding — from key fields]

**CRM & Ownership** [If CRM fields returned]
[Owner, opp stage, ARR]

**Scores** [If scores returned]
[All available scores as raw values or percentiles]

**Signal Highlights** [If activity/signals exist]
[3–5 most important signals with dates]

**Top Contacts** [If contacts returned]
[Name | Title | Score — top 5 sorted by score desc]

**RoomieAI Research** [If summary research is non-null]
[Summary research output; list all available research topic names]

**Recommended Next Steps**
[2–3 specific, signal-backed actions]

Targeted question: 1–3 sentence direct answer. No full brief needed.

Sparse data (few fields returned, most sections would be empty):

## [Company Name] — Account Overview (Limited Data)

**Data available:** [List exactly what Common Room returned]

[Present only the returned fields]

**Web Search**
[Findings from web search — or "No significant recent news found"]

**Note:** Common Room has limited data on this account. The account may need enrichment in Common Room.

Quality Standards

  • Scores must always be raw values or percentiles — never categorical labels
  • For targeted questions, answer precisely and don't over-deliver
  • Be explicit when data is missing or stale — don't speculate
  • Keep full briefings readable in 2–3 minutes
  • Every fact must trace to a tool call — don't include data not returned by Common Room

Reference Files

  • references/signals-guide.md — signal type taxonomy and interpretation guide

© w95, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in 05-sales/account-research-common-room of w95/awesome-claude-corporate-skills.

  • SKILL.md
  • references/signals-guide.md

Open the folder on GitHubat commit 78dbc7c

Compare with similar skills

Account Research 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.

Account Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Account Research this skillw95/awesome-claude-corporate-skills244—~1.5kAutomated safety check: PassMIT
Luopan Company Researchzhangxiaoqiang1991/luopan389—~998Automated safety check: PassMIT
Company Researchstophobia/deerflow2.0-enhanced822—~845Automated safety check: PassMIT
Meeting Prep BriefBrianRWagner/ai-marketing-claude-code-skills441—~922Automated safety check: PassNone
SdtStopDisTrain/sdt-skills309—~535Automated safety check: PassMIT
Account Researchextruct-ai/gtm-skills109—~1.6kAutomated safety check: PassNone

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Categories

Questions about Account Research

What does Account Research do?

Research a company using Common Room data. An agent skill from w95/awesome-claude-corporate-skills. Account Research is an agent skill from w95/awesome-claude-corporate-skills. Research a company using Common Room data.

When should I use Account Research?

Account Research fits situations like: research [company]; tell me about [domain]; pull up signals for [account]; whats going on with [company].

How do I install Account Research in Claude Code?

Run `npx skills add w95/awesome-claude-corporate-skills --skill account-research -a claude-code`. Or copy the skill folder (05-sales/account-research-common-room in w95/awesome-claude-corporate-skills) into .claude/skills/account-research in your project. Claude Code loads it when a task matches its description.

How do I install Account Research in Codex?

Run `npx skills add w95/awesome-claude-corporate-skills --skill account-research -a codex`. Or copy the skill folder (05-sales/account-research-common-room in w95/awesome-claude-corporate-skills) into .agents/skills/account-research in your project. Codex loads it when a task matches its description.

Can I use Account Research 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 w95/awesome-claude-corporate-skills --skill account-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/account-research, .gemini/skills/account-research, .github/skills/account-research and .opencode/skills/account-research in your project.

What does Account Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Account Research is instructions for the agent only.

Does Account Research 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 Account Research 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 Account Research use?

Account Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Account Research use?

About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 644 tokens, read only when the agent opens those files.

What are the alternatives to Account Research?

Skills that share tags, products or a category with Account Research: Luopan Company Research (zhangxiaoqiang1991/luopan, 389 stars), Company Research (stophobia/deerflow2.0-enhanced, 822 stars), Meeting Prep Brief (BrianRWagner/ai-marketing-claude-code-skills, 441 stars) and Sdt (StopDisTrain/sdt-skills, 309 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Account Research?

w95 (a GitHub user) maintains it in w95/awesome-claude-corporate-skills, which has 244 GitHub stars. The repository holds 42 skills in this directory. The repository was last updated on February 26, 2026.

Source: w95/awesome-claude-corporate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.