{
  "manifest": {
    "id": "investor-business",
    "name": "Investor & Business Intelligence",
    "description": "Investor materials preparation, outreach automation, lead intelligence, and market research patterns for business development",
    "version": "1.0.0",
    "author": "Adiuvo",
    "tags": [
      "investor",
      "business",
      "leads",
      "market-research",
      "outreach"
    ],
    "icon": "",
    "created_at": "2026-06-11T01:40:46Z",
    "updated_at": "2026-06-11T01:40:46Z",
    "category": "",
    "pack_type": "operator",
    "required_mcp_servers": [],
    "optional_mcp_servers": [],
    "on_install": null,
    "procedure_count": 0,
    "context_file_count": 4,
    "always_loaded": false
  },
  "index": "# Investor & Business Intelligence\n\nBusiness development patterns covering investor relations, lead intelligence, and market research.\n\n## What's Included\n\n- **Investor Materials** -- Pitch deck preparation, financial modeling, investor presentation, and fundraising documentation\n- **Investor Outreach** -- Investor targeting, outreach automation, follow-up sequences, and CRM integration\n- **Lead Intelligence** -- Lead scoring, enrichment, qualification, and intelligence gathering patterns\n- **Market Research** -- Market analysis, competitive intelligence, trend identification, and opportunity assessment\n\n## When to Use\n\nActivate this pack when:\n- Preparing investor pitch materials\n- Automating investor outreach campaigns\n- Building lead scoring and qualification systems\n- Conducting market research and competitive analysis\n",
  "procedures": [],
  "commands": [],
  "context_files": [
    {
      "filename": "investor-materials.md",
      "content": "---\nname: investor-materials\ndescription: Create and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and fundraising materials. Use when the user needs investor-facing documents, projections, use-of-funds tables, milestone plans, or materials that must stay internally consistent across multiple fundraising assets.\norigin: ECC\n---\n\n# Investor Materials\n\nBuild investor-facing materials that are consistent, credible, and easy to defend.\n\n## When to Activate\n\n- creating or revising a pitch deck\n- writing an investor memo or one-pager\n- building a financial model, milestone plan, or use-of-funds table\n- answering accelerator or incubator application questions\n- aligning multiple fundraising docs around one source of truth\n\n## Golden Rule\n\nAll investor materials must agree with each other.\n\nCreate or confirm a single source of truth before writing:\n- traction metrics\n- pricing and revenue assumptions\n- raise size and instrument\n- use of funds\n- team bios and titles\n- milestones and timelines\n\nIf conflicting numbers appear, stop and resolve them before drafting.\n\n## Core Workflow\n\n1. inventory the canonical facts\n2. identify missing assumptions\n3. choose the asset type\n4. draft the asset with explicit logic\n5. cross-check every number against the source of truth\n\n## Asset Guidance\n\n### Pitch Deck\nRecommended flow:\n1. company + wedge\n2. problem\n3. solution\n4. product / demo\n5. market\n6. business model\n7. traction\n8. team\n9. competition / differentiation\n10. ask\n11. use of funds / milestones\n12. appendix\n\nIf the user wants a web-native deck, pair this skill with `frontend-slides`.\n\n### One-Pager / Memo\n- state what the company does in one clean sentence\n- show why now\n- include traction and proof points early\n- make the ask precise\n- keep claims easy to verify\n\n### Financial Model\nInclude:\n- explicit assumptions\n- bear / base / bull cases when useful\n- clean layer-by-layer revenue logic\n- milestone-linked spending\n- sensitivity analysis where the decision hinges on assumptions\n\n### Accelerator Applications\n- answer the exact question asked\n- prioritize traction, insight, and team advantage\n- avoid puffery\n- keep internal metrics consistent with the deck and model\n\n## Red Flags to Avoid\n\n- unverifiable claims\n- fuzzy market sizing without assumptions\n- inconsistent team roles or titles\n- revenue math that does not sum cleanly\n- inflated certainty where assumptions are fragile\n\n## Quality Gate\n\nBefore delivering:\n- every number matches the current source of truth\n- use of funds and revenue layers sum correctly\n- assumptions are visible, not buried\n- the story is clear without hype language\n- the final asset is defensible in a partner meeting\n"
    },
    {
      "filename": "investor-outreach.md",
      "content": "---\nname: investor-outreach\ndescription: Draft cold emails, warm intro blurbs, follow-ups, update emails, and investor communications for fundraising. Use when the user wants outreach to angels, VCs, strategic investors, or accelerators and needs concise, personalized, investor-facing messaging.\norigin: ECC\n---\n\n# Investor Outreach\n\nWrite investor communication that is short, concrete, and easy to act on.\n\n## When to Activate\n\n- writing a cold email to an investor\n- drafting a warm intro request\n- sending follow-ups after a meeting or no response\n- writing investor updates during a process\n- tailoring outreach based on fund thesis or partner fit\n\n## Core Rules\n\n1. Personalize every outbound message.\n2. Keep the ask low-friction.\n3. Use proof instead of adjectives.\n4. Stay concise.\n5. Never send copy that could go to any investor.\n\n## Voice Handling\n\nIf the user's voice matters, run `brand-voice` first and reuse its `VOICE PROFILE`.\nThis skill should keep the investor-specific structure and ask discipline, not recreate its own parallel voice system.\n\n## Hard Bans\n\nDelete and rewrite any of these:\n- \"I'd love to connect\"\n- \"excited to share\"\n- generic thesis praise without a real tie-in\n- vague founder adjectives\n- begging language\n- soft closing questions when a direct ask is clearer\n\n## Cold Email Structure\n\n1. subject line: short and specific\n2. opener: why this investor specifically\n3. pitch: what the company does, why now, and what proof matters\n4. ask: one concrete next step\n5. sign-off: name, role, and one credibility anchor if needed\n\n## Personalization Sources\n\nReference one or more of:\n- relevant portfolio companies\n- a public thesis, talk, post, or article\n- a mutual connection\n- a clear market or product fit with the investor's focus\n\nIf that context is missing, state that the draft still needs personalization instead of pretending it is finished.\n\n## Follow-Up Cadence\n\nDefault:\n- day 0: initial outbound\n- day 4 or 5: short follow-up with one new data point\n- day 10 to 12: final follow-up with a clean close\n\nDo not keep nudging after that unless the user wants a longer sequence.\n\n## Warm Intro Requests\n\nMake life easy for the connector:\n- explain why the intro is a fit\n- include a forwardable blurb\n- keep the forwardable blurb under 100 words\n\n## Post-Meeting Updates\n\nInclude:\n- the specific thing discussed\n- the answer or update promised\n- one new proof point if available\n- the next step\n\n## Quality Gate\n\nBefore delivering:\n- the message is genuinely personalized\n- the ask is explicit\n- the proof point is concrete\n- filler praise and softener language are gone\n- word count stays tight\n"
    },
    {
      "filename": "lead-intelligence.md",
      "content": "---\nname: lead-intelligence\ndescription: AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.\norigin: ECC\n---\n\n# Lead Intelligence\n\nAgent-powered lead intelligence pipeline that finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery.\n\n## When to Activate\n\n- User wants to find leads or prospects in a specific industry\n- Building an outreach list for partnerships, sales, or fundraising\n- Researching who to reach out to and the best path to reach them\n- User says \"find leads\", \"outreach list\", \"who should I reach out to\", \"warm intros\"\n- Needs to score or rank a list of contacts by relevance\n- Wants to map mutual connections to find warm introduction paths\n\n## Tool Requirements\n\n### Required\n- **Exa MCP** — Deep web search for people, companies, and signals (`web_search_exa`)\n- **X API** — Follower/following graph, mutual analysis, recent activity (`X_BEARER_TOKEN`, plus write-context credentials such as `X_CONSUMER_KEY`, `X_CONSUMER_SECRET`, `X_ACCESS_TOKEN`, `X_ACCESS_TOKEN_SECRET`)\n\n### Optional (enhance results)\n- **LinkedIn** — Direct API if available, otherwise browser control for search, profile inspection, and drafting\n- **Apollo/Clay API** — For enrichment cross-reference if user has access\n- **GitHub MCP** — For developer-centric lead qualification\n- **Apple Mail / Mail.app** — Draft cold or warm email without sending automatically\n- **Browser control** — For LinkedIn and X when API coverage is missing or constrained\n\n## Pipeline Overview\n\n```\n┌─────────────┐     ┌──────────────┐     ┌─────────────────┐     ┌──────────────┐     ┌─────────────────┐\n│ 1. Signal   │────>│ 2. Mutual    │────>│ 3. Warm Path    │────>│ 4. Enrich    │────>│ 5. Outreach     │\n│    Scoring  │     │    Ranking   │     │    Discovery    │     │              │     │    Draft        │\n└─────────────┘     └──────────────┘     └─────────────────┘     └──────────────┘     └─────────────────┘\n```\n\n## Voice Before Outreach\n\nDo not draft outbound from generic sales copy.\n\nRun `brand-voice` first whenever the user's voice matters. Reuse its `VOICE PROFILE` instead of re-deriving style ad hoc inside this skill.\n\nIf live X access is available, pull recent original posts before drafting. If not, use supplied examples or the best repo/site material available.\n\n## Stage 1: Signal Scoring\n\nSearch for high-signal people in target verticals. Assign a weight to each based on:\n\n| Signal | Weight | Source |\n|--------|--------|--------|\n| Role/title alignment | 30% | Exa, LinkedIn |\n| Industry match | 25% | Exa company search |\n| Recent activity on topic | 20% | X API search, Exa |\n| Follower count / influence | 10% | X API |\n| Location proximity | 10% | Exa, LinkedIn |\n| Engagement with your content | 5% | X API interactions |\n\n### Signal Search Approach\n\n```python\n# Step 1: Define target parameters\ntarget_verticals = [\"prediction markets\", \"AI tooling\", \"developer tools\"]\ntarget_roles = [\"founder\", \"CEO\", \"CTO\", \"VP Engineering\", \"investor\", \"partner\"]\ntarget_locations = [\"San Francisco\", \"New York\", \"London\", \"remote\"]\n\n# Step 2: Exa deep search for people\nfor vertical in target_verticals:\n    results = web_search_exa(\n        query=f\"{vertical} {role} founder CEO\",\n        category=\"company\",\n        numResults=20\n    )\n    # Score each result\n\n# Step 3: X API search for active voices\nx_search = search_recent_tweets(\n    query=\"prediction markets OR AI tooling OR developer tools\",\n    max_results=100\n)\n# Extract and score unique authors\n```\n\n## Stage 2: Mutual Ranking\n\nFor each scored target, analyze the user's social graph to find the warmest path.\n\n### Ranking Model\n\n1. Pull user's X following list and LinkedIn connections\n2. For each high-signal target, check for shared connections\n3. Apply the `social-graph-ranker` model to score bridge value\n4. Rank mutuals by:\n\n| Factor | Weight |\n|--------|--------|\n| Number of connections to targets | 40% — highest weight, most connections = highest rank |\n| Mutual's current role/company | 20% — decision maker vs individual contributor |\n| Mutual's location | 15% — same city = easier intro |\n| Industry alignment | 15% — same vertical = natural intro |\n| Mutual's X handle / LinkedIn | 10% — identifiability for outreach |\n\nCanonical rule:\n\n```text\nUse social-graph-ranker when the user wants the graph math itself,\nthe bridge ranking as a standalone report, or explicit decay-model tuning.\n```\n\nInside this skill, use the same weighted bridge model:\n\n```text\nB(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)\nR(m) = B_ext(m) · (1 + β · engagement(m))\n```\n\nInterpretation:\n- Tier 1: high `R(m)` and direct bridge paths -> warm intro asks\n- Tier 2: medium `R(m)` and one-hop bridge paths -> conditional intro asks\n- Tier 3: no viable bridge -> direct cold outreach using the same lead record\n\n### Output Format\n\n```\n\nIf the user explicitly wants the ranking engine broken out, the math visualized, or the network scored outside the full lead workflow, run `social-graph-ranker` as a standalone pass first and feed the result back into this pipeline.\nMUTUAL RANKING REPORT\n=====================\n\n#1  @mutual_handle (Score: 92)\n    Name: Jane Smith\n    Role: Partner @ Acme Ventures\n    Location: San Francisco\n    Connections to targets: 7\n    Connected to: @target1, @target2, @target3, @target4, @target5, @target6, @target7\n    Best intro path: Jane invested in Target1's company\n\n#2  @mutual_handle2 (Score: 85)\n    ...\n```\n\n## Stage 3: Warm Path Discovery\n\nFor each target, find the shortest introduction chain:\n\n```\nYou ──[follows]──> Mutual A ──[invested in]──> Target Company\nYou ──[follows]──> Mutual B ──[co-founded with]──> Target Person\nYou ──[met at]──> Event ──[also attended]──> Target Person\n```\n\n### Path Types (ordered by warmth)\n1. **Direct mutual** — You both follow/know the same person\n2. **Portfolio connection** — Mutual invested in or advises target's company\n3. **Co-worker/alumni** — Mutual worked at same company or attended same school\n4. **Event overlap** — Both attended same conference/program\n5. **Content engagement** — Target engaged with mutual's content or vice versa\n\n## Stage 4: Enrichment\n\nFor each qualified lead, pull:\n\n- Full name, current title, company\n- Company size, funding stage, recent news\n- Recent X posts (last 30 days) — topics, tone, interests\n- Mutual interests with user (shared follows, similar content)\n- Recent company events (product launch, funding round, hiring)\n\n### Enrichment Sources\n- Exa: company data, news, blog posts\n- X API: recent tweets, bio, followers\n- GitHub: open source contributions (for developer-centric leads)\n- LinkedIn (via browser-use): full profile, experience, education\n\n## Stage 5: Outreach Draft\n\nGenerate personalized outreach for each lead. The draft should match the source-derived voice profile and the target channel.\n\n### Channel Rules\n\n#### Email\n\n- Use for the highest-value cold outreach, warm intros, investor outreach, and partnership asks\n- Default to drafting in Apple Mail / Mail.app when local desktop control is available\n- Create drafts first, do not send automatically unless the user explicitly asks\n- Subject line should be plain and specific, not clever\n\n#### LinkedIn\n\n- Use when the target is active there, when mutual graph context is stronger on LinkedIn, or when email confidence is low\n- Prefer API access if available\n- Otherwise use browser control to inspect profiles, recent activity, and draft the message\n- Keep it shorter than email and avoid fake professional warmth\n\n#### X\n\n- Use for high-context operator, builder, or investor outreach where public posting behavior matters\n- Prefer API access for search, timeline, and engagement analysis\n- Fall back to browser control when needed\n- DMs and public replies should be much tighter than email and should reference something real from the target's timeline\n\n#### Channel Selection Heuristic\n\nPick one primary channel in this order:\n\n1. warm intro by email\n2. direct email\n3. LinkedIn DM\n4. X DM or reply\n\nUse multi-channel only when there is a strong reason and the cadence will not feel spammy.\n\n### Warm Intro Request (to mutual)\n\nGoal:\n\n- one clear ask\n- one concrete reason this intro makes sense\n- easy-to-forward blurb if needed\n\nAvoid:\n\n- overexplaining your company\n- social-proof stacking\n- sounding like a fundraiser template\n\n### Direct Cold Outreach (to target)\n\nGoal:\n\n- open from something specific and recent\n- explain why the fit is real\n- make one low-friction ask\n\nAvoid:\n\n- generic admiration\n- feature dumping\n- broad asks like \"would love to connect\"\n- forced rhetorical questions\n\n### Execution Pattern\n\nFor each target, produce:\n\n1. the recommended channel\n2. the reason that channel is best\n3. the message draft\n4. optional follow-up draft\n5. if email is the chosen channel and Apple Mail is available, create a draft instead of only returning text\n\nIf browser control is available:\n\n- LinkedIn: inspect target profile, recent activity, and mutual context, then draft or prepare the message\n- X: inspect recent posts or replies, then draft DM or public reply language\n\nIf desktop automation is available:\n\n- Apple Mail: create draft email with subject, body, and recipient\n\nDo not send messages automatically without explicit user approval.\n\n### Anti-Patterns\n\n- generic templates with no personalization\n- long paragraphs explaining your whole company\n- multiple asks in one message\n- fake familiarity without specifics\n- bulk-sent messages with visible merge fields\n- identical copy reused for email, LinkedIn, and X\n- platform-shaped slop instead of the author's actual voice\n\n## Configuration\n\nUsers should set these environment variables:\n\n```bash\n# Required\nexport X_BEARER_TOKEN=\"...\"\nexport X_ACCESS_TOKEN=\"...\"\nexport X_ACCESS_TOKEN_SECRET=\"...\"\nexport X_CONSUMER_KEY=\"...\"\nexport X_CONSUMER_SECRET=\"...\"\nexport EXA_API_KEY=\"...\"\n\n# Optional\nexport LINKEDIN_COOKIE=\"...\" # For browser-use LinkedIn access\nexport APOLLO_API_KEY=\"...\"  # For Apollo enrichment\n```\n\n## Agents\n\nThis skill includes specialized agents in the `agents/` subdirectory:\n\n- **signal-scorer** — Searches and ranks prospects by relevance signals\n- **mutual-mapper** — Maps social graph connections and finds warm paths\n- **enrichment-agent** — Pulls detailed profile and company data\n- **outreach-drafter** — Generates personalized messages\n\n## Example Usage\n\n```\nUser: find me the top 20 people in prediction markets I should reach out to\n\nAgent workflow:\n1. signal-scorer searches Exa and X for prediction market leaders\n2. mutual-mapper checks user's X graph for shared connections\n3. enrichment-agent pulls company data and recent activity\n4. outreach-drafter generates personalized messages for top ranked leads\n\nOutput: Ranked list with warm paths, voice profile summary, and channel-specific outreach drafts or drafts-in-app\n```\n\n## Related Skills\n\n- `brand-voice` for canonical voice capture\n- `connections-optimizer` for review-first network pruning and expansion before outreach\n"
    },
    {
      "filename": "market-research.md",
      "content": "---\nname: market-research\ndescription: Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.\norigin: ECC\n---\n\n# Market Research\n\nProduce research that supports decisions, not research theater.\n\n## When to Activate\n\n- researching a market, category, company, investor, or technology trend\n- building TAM/SAM/SOM estimates\n- comparing competitors or adjacent products\n- preparing investor dossiers before outreach\n- pressure-testing a thesis before building, funding, or entering a market\n\n## Research Standards\n\n1. Every important claim needs a source.\n2. Prefer recent data and call out stale data.\n3. Include contrarian evidence and downside cases.\n4. Translate findings into a decision, not just a summary.\n5. Separate fact, inference, and recommendation clearly.\n\n## Common Research Modes\n\n### Investor / Fund Diligence\nCollect:\n- fund size, stage, and typical check size\n- relevant portfolio companies\n- public thesis and recent activity\n- reasons the fund is or is not a fit\n- any obvious red flags or mismatches\n\n### Competitive Analysis\nCollect:\n- product reality, not marketing copy\n- funding and investor history if public\n- traction metrics if public\n- distribution and pricing clues\n- strengths, weaknesses, and positioning gaps\n\n### Market Sizing\nUse:\n- top-down estimates from reports or public datasets\n- bottom-up sanity checks from realistic customer acquisition assumptions\n- explicit assumptions for every leap in logic\n\n### Technology / Vendor Research\nCollect:\n- how it works\n- trade-offs and adoption signals\n- integration complexity\n- lock-in, security, compliance, and operational risk\n\n## Output Format\n\nDefault structure:\n1. executive summary\n2. key findings\n3. implications\n4. risks and caveats\n5. recommendation\n6. sources\n\n## Quality Gate\n\nBefore delivering:\n- all numbers are sourced or labeled as estimates\n- old data is flagged\n- the recommendation follows from the evidence\n- risks and counterarguments are included\n- the output makes a decision easier\n"
    }
  ]
}