From octave
Creates comprehensive account plans for target companies by combining research, stakeholder mapping, persona matching, and coordinated outreach.
How this skill is triggered — by the user, by Claude, or both
Slash command
/octave:abmThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Create comprehensive account plans for target accounts by combining deep research, stakeholder mapping, persona matching, and coordinated outreach — all grounded in your library's Motion ICP cells and proof points.
Create comprehensive account plans for target accounts by combining deep research, stakeholder mapping, persona matching, and coordinated outreach — all grounded in your library's Motion ICP cells and proof points.
/octave:abm <company> [--stakeholders <N>] [--motion <name>] [--depth quick|full]
/octave:abm acme.com # Full account plan
/octave:abm acme.com --stakeholders 5 # Map top 5 stakeholders
/octave:abm acme.com --motion "Enterprise Expansion" # Scope to a specific Motion's ICP matrix
/octave:abm acme.com --depth quick # Quick assessment only
/octave:abm "Acme Corp" # Search by company name
When the user runs /octave:abm:
Parse input:
acme.com) → Use directlyIf company name provided without domain:
find_company({ name: "<company_name>" })
# Full company enrichment
enrich_company({ companyDomain: "<domain>" })
# Qualify against ICP
qualify_company({
companyDomain: "<domain>",
additionalContext: "Evaluate fit against all segments. Identify which segment, use cases, and Motion ICP cells are most relevant."
})
# Find decision makers and influencers
# Use persona titles from library to guide search
list_all_entities({ entityType: "persona" })
# Search for stakeholders matching each persona
find_person({
searchMode: "people",
companyDomain: "<domain>",
fuzzyTitles: ["<titles from persona 1>"],
limit: <stakeholders_param or 3>
})
# Repeat for other relevant personas if needed
find_person({
searchMode: "people",
companyDomain: "<domain>",
fuzzyTitles: ["<titles from persona 2>"],
limit: 3
})
For each key stakeholder found:
# Enrich top stakeholders
enrich_person({
person: {
firstName: "<first>",
lastName: "<last>",
companyDomain: "<domain>"
}
})
# Qualify against personas
qualify_person({
person: {
firstName: "<first>",
lastName: "<last>",
companyDomain: "<domain>",
jobTitle: "<title>"
},
additionalContext: "Match to our buyer personas. Identify their likely role in a buying decision (champion, evaluator, economic buyer, blocker)."
})
# Find the right Motion for this account (offering + motion type)
list_motions()
# See the persona × segment matrix for the matched Motion
list_motion_icps({ motionOId: "<motion_oId>" })
# Fetch the narrative for the target persona × segment cell
find_motion_icp({ motionIcpOId: "<motion_icp_oId>", includeLearnings: true })
# Find relevant proof points (industry, size match)
search_knowledge_base({
query: "<company industry> <company size> results case study",
entityTypes: ["proof_point", "reference"]
})
# Check for competitive context
search_knowledge_base({
query: "<company name> <any tech stack signals>",
entityTypes: ["competitor"]
})
# Check for any existing conversation history
list_events({
startDate: "<365 days ago>",
filters: {
companyDomains: ["<domain>"]
}
})
See account-plan-template.md for the full account plan output template.
For the recommended entry point stakeholder:
generate_email({
person: {
firstName: "<first>",
lastName: "<last>",
companyDomain: "<domain>",
jobTitle: "<title>"
},
numEmails: 4,
sequenceType: "COLD_OUTBOUND",
allEmailsContext: "Account plan context: [company signals, persona match, Motion ICP narrative, proof points]",
allEmailsInstructions: "Personalized to [company] specifically. Reference [relevant signals]. Use [proof points] progressively."
})
enrich_company - Deep company intelligenceenrich_person - Stakeholder backgroundqualify_company - ICP fit scoringqualify_person - Persona matchingfind_person - Stakeholder discoveryfind_company - Company lookup by namelist_all_entities (persona) - Get persona definitions for stakeholder matchinglist_motions - List all Motions in the workspacelist_motion_icps - List Motion ICP cells (persona × segment intersections) for a Motionfind_motion_icp - Full Motion ICP cell narrative (Target ICP overview, Operating landscape, Strategic narrative, Pains and consequences, Benefits and impacts, Methodology, References) plus Learning Loop learningssearch_knowledge_base - Proof points, references, competitive intellist_events - Existing conversation history with accountgenerate_email - Outreach sequencesgenerate_content - Account-specific contentgenerate_call_prep - Meeting preparationCompany Not Found:
Couldn't find "[input]".
Try:
- Provide the company's website domain (e.g., acme.com)
- Check spelling
- Search by name: I'll look it up
No Stakeholders Found:
No contacts found at [Company] matching your personas.
Options:
- Broaden the search (search all titles, not just persona matches)
- Search for specific titles you know
- Proceed with company-level plan only
Low ICP Score:
[Company] scored [X/100] against your ICP.
This is below typical qualification thresholds. Continue anyway? Or:
- Find similar companies with better fit
- See why the score is low and if any signals are missing
- Proceed with adjusted expectations
/octave:research - Deep-dive on a specific stakeholder/octave:campaign - Generate multi-channel campaign for this account/octave:battlecard - Competitive intel if competitor detected/octave:pipeline - Coach on deal progression after engagement starts/octave:prospector - Find more accounts like this onenpx claudepluginhub octavehq/lfgtm --plugin octaveGuides enterprise account planning and execution, including MEDDICC qualification, stakeholder management, mutual action plans (MAPs), and deal health tracking via 'stale MAP equals dead deal' rule. Use for complex sales cycles over 60 days.
Prepares for calls, meetings, demos, and outreach by researching companies and people. Adapts output based on occasion (discovery, demo, follow-up, outreach).
Builds targeted company and contact lists using Common Room Prospector for net-new prospects or existing accounts with signals. Clarifies object types and refines iteratively.