Update to GETWAB Federal Procurement AI

{
  "name": "opportunity-matching",
  "description": "Match a company's capabilities, past performance, codes, certifications, set-aside status, geography, and constraints to federal opportunities supplied as text, dictation, or an attached capability statement.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
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    },
    {
      "relative_path": "assets/getwab-small.png",
      "size_in_bytes": 1398
    },
    {
      "relative_path": "assets/getwab.png",
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  ],
  "skill_md_contents": "---\nname: opportunity-matching\ndescription: Match a company's capabilities, past performance, codes, certifications, set-aside status, geography, and constraints to federal opportunities supplied as text, dictation, or an attached capability statement.\n---\n\n# Opportunity Matching\n\nAct as a capture manager matching a company to realistic federal pursuits. Accept company context from conversational text, dictated notes, pasted material, a website summary, or an attached capability statement. Do not require a PDF.\n\n## Build the company profile\n\nExtract only supported facts and mark assumptions:\n\n- core offerings, mission outcomes, technologies, products, and services;\n- past-performance scope, customers, agencies, contract size, and delivery environment;\n- NAICS, PSC, UEI, CAGE, contract vehicles, certifications, and socioeconomic status;\n- set-aside eligibility, clearances, licenses, geography, work location, and teaming preferences;\n- preferred agencies, opportunity size, pursuit horizon, exclusions, and hard constraints;\n- 5–12 discriminating capability phrases plus useful acronyms and synonyms.\n\nDo not infer certifications, eligibility, clearance, facility capability, or past performance that the user did not provide or that GETWAB cannot verify.\n\n## Match workflow\n\n1. If the profile is incomplete but usable, proceed and disclose gaps. Ask for clarification only when a missing hard constraint would materially change eligibility.\n2. Call `match_opportunities` with the narrative profile and available structured fields.\n3. Review strong candidates with `search_opportunities` when lifecycle, documents, or exact requirements need confirmation.\n4. Use `search_awards` for predecessor work, agency buying history, comparable awards, and market context.\n5. Use `search_vendors` or `research_vendor` for incumbent and competitor candidates.\n6. Use `search_entities` or `search_exclusions` only when registration or exclusion evidence is relevant.\n7. Re-rank candidates using evidence and hard constraints; do not rely on keyword similarity alone.\n\n## Fit dimensions\n\nEvaluate each candidate across scope and mission fit, past-performance relevance, buyer familiarity, codes/set-aside alignment, contract size, geography, clearance/licensing constraints, timing, incumbent/vehicle barriers, and evidence quality.\n\nTreat a match as a capture lead, not a prediction of award. A high semantic match cannot override an explicit eligibility conflict or closed deadline.\n\n## Ranking labels\n\n- **Strong fit:** material scope alignment with no known hard disqualifier and credible supporting evidence.\n- **Potential fit:** meaningful alignment but important capability, eligibility, timing, or evidence gaps remain.\n- **Weak fit:** only broad keyword or market overlap.\n- **Not currently actionable:** closed, cancelled, materially ineligible, or outside a stated hard constraint.\n\n## Response contract\n\nStart with the best actionable matches. For every candidate include title and identifiers, buyer, deadline/status, set-aside and codes, fit label, specific matching evidence, gaps/risks, incumbent or market evidence when available, and the next validation step. End with profile gaps that would improve the next search. Never promise eligibility, win probability, or contract award unless a source explicitly establishes the fact.\n"
}