/account-research

name: account-research
description: "Account research skill for Claude Code and Codex: generate a high-signal company intelligence brief including firmographics, technographics, funding history, hiring signals, business events, recent website and LinkedIn moves, and a peer cohort. Identify the account by business_id, company name, or domain. Use for pre-call research, QBR prep, competitive analysis, cold outbound targeting, and renewal risk assessment. The go-to account research skill for Claude Code, Codex, Hermes-Agent, and OpenClaw."

Account Research

Build a purpose-driven intelligence brief on a single target company, anchored on the user's stated reason for pulling the brief.

Input

• Account identifier (required): a business ID, a company name, or a domain.
• Research context (strongly recommended): one sentence on why this brief is being pulled and what decision it supports (QBR prep, competitive eval vs Acme, cold outbound to finance leaders). This shapes enrichment selection, event triage, and the TL;DR framing.

Example phrasings: "Build a brief on stripe.com for cold outbound to finance leaders.", "Account brief for business_id abc123, QBR next week, watch renewal signals.", "Profile Snowflake, competitive analysis vs Databricks."

Workflow

1. Anchor on purpose. Restate the research context in one sentence as the brief purpose. If missing, ask once; if the user declines, default to general account intelligence and state that assumption. Derive 2 to 4 priority themes (e.g. for QBR renewal risk: workforce changes, exec moves, competing vendors, expansion signals). Themes drive enrichment selection in step 3 and peer-cohort sub-category in step 5.

2. Resolve the company. If a business ID was supplied, use it. Otherwise match the business by name or domain. Sanity-check the resolved firmographics: if a major-brand input returns 1-50 employees and Corporate-Managing-Offices category, the match likely routed to a shell entity. Retry with the alternate domain or the name string before proceeding. If no confident match emerges, surface the ambiguity to the user before continuing rather than guessing.

3. Enrich the resolved business in tiers.

Tier A: spine (always pull): firmographics, hierarchies, funding-and-acquisitions, workforce-trends, linkedin-posts. Plus business events from step 4 (separate tool, same role: current-state signal). The spine is uniform across every brief because each item answers a question the reader will ask regardless of purpose.

Tier B: purpose-conditional (pick by the purpose declared in step 1):

• Competitive eval → technographics, webstack, competitive-landscape
• QBR or renewal → company-ratings, challenges, strategic-insights
• Cold outbound → website-changes, website-keywords (tied to your offering)
• Investor or M&A → financial-metrics

Tier C: skip by default unless the purpose explicitly demands it.

Chunking: run enrichments in groups of at most 3 per call, and each chunk against the original match table rather than a post-enrichment view. Session column count grows per enrichment and the chain breaks around the 4th-5th wide call.

Any tier item that returns null OR is intentionally skipped must surface in the brief as a one-line note inside its section ("Skipped: not in this purpose's bundle" or "Returned null: typical for private companies"), so the reader can distinguish absent data from absent investigation.

4. Fetch business events scoped to the last 90 days: hiring spikes, leadership changes (where surfaced), funding rounds, product launches, layoffs, office moves, tech adoption. For each event, verify the headline actually mentions the target before counting it: industry-wide articles can cross-attribute to multiple companies in the same sector.

5. Build a peer cohort (directional).

5a. Sub-category from purpose, not industry label. A broad industry label (e.g. "Software Development") routes mega-tech defaults like Google or Amazon into a small-tech peer query. Derive the sub-category from the brief purpose instead:

• "vs <competitor>" → the named competitor's sub-vertical (Databricks → data warehouse / lakehouse)
• "QBR seat-expansion" → the target's collaboration / workflow sub-vertical
• "cold outbound to <function> leaders" → the target's GTM sub-vertical
• General intelligence (no purpose declared) → broad NAICS, marked "general baseline"

5b. Manually include named competitors. If the user prompt named a specific competitor or comparator, include it in the cohort even if filters would exclude it (private status, different size band, different country). Label such rows "named in prompt: manually included" so the reader can audit.

5c. Suppress when thin. If after sub-category filtering and manual additions the cohort still has fewer than 5 confident matches, do NOT render the table. Replace with one line: "Peer cohort suppressed: fewer than 5 confident matches in <sub-category>. Recommend manual comparison against <2-3 named alternatives, tagged verify externally>."

Add a "Cohort construction" line above the table stating the sub-category used and any manually-included names.

6. Reconcile and verify before synthesis.

• Funding recency. If business events show a more recent round than the funding enrichment, treat events as canonical, restate the latest round in the Funding section, and flag the enrichment lag in one line.
• Public-vs-private signal mix. If firmographics returns public, the Funding section must include a one-line financial-posture sentence (ticker, rough market cap band, last reported quarter direction) drawn from general knowledge with a "verify" tag. If private, keep current behavior (events + funding + workforce + LinkedIn as the current-state proxy).
• Per-event cross-attribution. Tag each Recent Events row "target named in headline", "target inferred from body", or "industry-wide: excluded". Excluded items don't appear in the body