career-coach

name	career-coach
description	Produces a skill-gap analysis and a phased learning roadmap for a candidate targeting a specific role (using hybrid search on the career_paths and skills_taxonomy collections), AND retrieves compensation benchmarks — base/total/percentiles, benefits norms, and grounded negotiation ranges — for a role at a given seniority and location (using hybrid search on the compensation_data collection). Use this skill when a candidate asks what skills they need for a target role, how to transition between roles, how to reach a more senior level, wants a learning/development plan, OR asks what a role pays, what salary to expect or ask for, how pay varies by location or seniority, or how to frame a compensation negotiation.
metadata	{"author":"anujpanchal","version":"2.0","domain":"recruitment"}

Career Coach Skill

This skill covers two workflows. Pick the one(s) that match the candidate's request:

• Career development (Steps 1–4) — skill-gap analysis and a phased learning roadmap.
• Compensation (Steps 5–7) — salary/benefits benchmarks and a grounded negotiation range.

If the candidate asks about both (e.g. "what does this role pay and what skills do I need?"), run both workflows and present the answer in two clearly-labelled sections.

What You Can Do

Career development

1. Resolve the candidate's current skill set — from the skills stated in the message, or from a stored candidates profile when a candidateId is provided.
2. Retrieve the target role's requirements and progression path — run a hybrid vector + full-text search against the career_paths collection.
3. Compute the skill gap — diff the candidate's current skills against the target role's requiredSkills, classifying each as covered, partial, or missing.
4. Assemble a phased learning roadmap — enrich each missing/partial skill with learning resources from the skills_taxonomy collection and order them into time-boxed phases.

Compensation

5. Resolve the compensation query — extract the role title, seniority, and location the candidate is asking about.
6. Retrieve matching benchmarks — run a hybrid vector + full-text search against the compensation_data collection, pre-filtered by seniority and (when possible) region.
7. Present a grounded band and negotiation range — translate the retrieved percentiles into a plain-language pay band, benefits summary, and an "ask" range, all sourced from the data.

---

Step 1 — Resolve Current Skills and Target Role

When a candidate asks about skills, a transition, or a development plan:

1. Determine the target role from the conversation (e.g. "Machine Learning Engineer", "Staff Backend Engineer"). If it is not clear, ask one clarifying question and stop.

2. Determine the candidate's current skills:

   • If the message lists skills, seniority, or years of experience, use those directly.
   • If the candidate provides a candidateId, call mongodb_query → findOne on the candidates collection with { "candidateId": "<id>" } and "projection": { "embedding": 0 }. Use profile.skills, profile.seniority, and profile.yearsExperience.
   • If neither is available, ask the candidate to paste their key skills or their current role, then stop.

3. Build a queryText string in this format for the target-role search:

   <target role title>
   Current seniority: <candidate seniority or "unspecified">
   Current skills: <comma-separated current skills>
   Goal: transition to <target role title>
   

---

Step 2 — Hybrid Career-Path Search

Perform a hybrid search against the career_paths collection in a single tool call. The MCP runtime runs the vector and lexical legs in parallel and merges them with Reciprocal Rank Fusion server-side — do not run a separate $search aggregation or compute RRF in-band.

Single hybrid call

Call mongodb_vector_search with:

• collection: career_paths
• queryText: the string built in Step 1.3. The runtime embeds this server-side for the vector leg and uses it verbatim for the BM25 lexical leg. Do NOT call embed_multimodal_content — the search runtime embeds queryText internally.
• indexName: career_paths_vector_index
• hybrid: true
• lexicalIndex: career_paths_text_index
• lexicalPath: description — single text-indexed field the BM25 leg searches. It carries the densest mix of role title, required-skill, and progression signal. (Atlas Search hybrid mode is single-path; multi-field recall is recovered by the vector leg.)
• limit: 3 — final fused result count.
• fetchK: 8 — per-leg over-fetch before RRF merge.

Each result carries a _score (RRF score) and a _sources array (["vector"], ["lexical"], or both). Use the top-ranked path whose targetRole best matches the candidate's stated target. Do not re-rank or re-fuse yourself.

---

Step 3 — Compute the Skill Gap

Diff the candidate's current skills against the chosen path's requiredSkills array:

• Covered — the candidate already has this skill (exact or clear synonym match).
• Partial — the candidate has a related/foundational skill but not the specific one.
• Missing — the candidate has no evidence of this skill.

For every missing and partial skill, look up learning resources. In one mongodb_query → find call on skills_taxonomy, pass a filter matching all the gap skills at once, e.g.:

{
  "collection": "skills_taxonomy",
  "operation": "find",
  "filter": { "skillId": { "$in": ["<skillId1>", "<skillId2>"] } },
  "projection": { "embedding": 0 }
}

If the path stores requiredSkills as skill names rather than IDs, filter on { "name": { "$in": [ ... ] } } instead. Use references/collections-schema.md to confirm the exact field names before constructing the query.

---

Step 4 — Present the Skill Gap and Roadmap

Present the result in this structure:

1. Where you are — one sentence summarizing the candidate's current level and strongest relevant skills (the covered set).
2. The gap — a short list of missing and partial skills, each with a one-line note on why the target role needs it (from the path's requiredSkills notes when present).
3. Your roadmap — the path's phased plan. Order the phases using the path's milestones (each milestone has an order, title, skills, and estimatedWeeks). Under each milestone, list the concrete learning resources retrieved from skills_taxonomy (name, resourceType, and resources links/titles) verbatim.
4. Estimated timeline — sum the estimatedWeeks across milestones the candidate still needs, and state it as an approximate range.

Cite pathId when you reference a specific progression path.

---

Step 5 — Resolve Role, Seniority, and Location (compensation)

When a candidate asks about pay, expectations, or negotiation:

1. Extract from the conversation:

   • Role title (e.g. "Machine Learning Engineer", "Staff Backend Engineer").
   • Seniority — one of junior | mid | senior | staff. If not stated, infer from the role title or the candidate's stated years of experience; otherwise leave unset.
   • Location — a city/region or "remote". Map it to a region value where possible (see references/collections-schema.md for the region enum). If not stated, leave unset and note that the benchmark will be nationwide.

2. If the role is missing, ask one clarifying question and stop. Seniority and location are optional — proceed without them if not provided.

3. Build a queryText string in this format:

   <role title>
   Seniority: <seniority or "unspecified">
   Location: <location or "any / remote">
   

---

Step 6 — Filtered Hybrid Compensation Search

Perform a hybrid search against the compensation_data collection in a single tool call. The MCP runtime runs the vector and lexical legs in parallel and merges them with Reciprocal Rank Fusion server-side — do not run a separate $search aggregation or compute RRF in-band.

Single hybrid call

Call mongodb_vector_search with:

• collection: compensation_data
• queryText: the string built in Step 5.3. The runtime embeds this server-side for the vector leg and uses it verbatim for the BM25 lexical leg. Do NOT call embed_multimodal_content — the search runtime embeds queryText internally.
• indexName: compensation_vector_index
• hybrid: true
• lexicalIndex: compensation_text_index
• lexicalPath: role — single text-indexed field the BM25 leg searches. It carries the densest role/title signal. (Atlas Search hybrid mode is single-path; multi-field recall is recovered by the vector leg.)
• limit: 3 — final fused result count.
• fetchK: 8 — per-leg over-fetch before RRF merge.
• filter: when seniority and/or region are known, pass them as pre-filter fields, e.g. { "seniority": "senior", "region": "us-south" }. Only include keys you actually resolved in Step 5. seniority and region are declared as filter fields on the vector index.

Each result carries a _score (RRF score) and a _sources array. Use the top-ranked record whose role, seniority, and region best match the query. Do not re-rank or re-fuse yourself.

---

Step 7 — Present the Band and Negotiation Range

From the chosen benchmark document, present:

1. Coverage line — the exact role, seniority, region, and asOf date the benchmark applies to, so the candidate knows the basis.
2. Pay band — from baseSalary and totalComp, show:
   • Base: p25–p75 as the typical range, and note the p50 (median).
   • Total comp: p25–p75, and note the p50.
   • State the currency and whether figures are annual.
3. Benefits & equity — summarize benefits (array of strings) and equityNote verbatim.
4. Suggested ask range — frame a realistic ask: for a strong candidate, anchor near the p50–p75 of total comp; for a stretch, mention p90 if present. Present this as a range and clearly label it as guidance derived from the retrieved percentiles.

Cite benchmarkId when you reference a specific record.

---

References

References (on demand): the API only serves files for skills that are allowed for this agent and already activated (activate_skill, or pre-activation for specialists). Call read_skill_resource with skillName career-coach and path (relative to that skill folder), e.g.:

• references/collections-schema.md — Full document schemas, index definitions, and field notes for the career_paths, skills_taxonomy, and compensation_data collections (including the region enum and percentile fields). Load this when you need to verify field names, the region mapping, filter parameters, or construct precise queries.

For the career-coach specialist agent, this skill is pre-activated at turn start, so read_skill_resource works immediately. If you see skill_not_activated, run activate_skill with career-coach first.

---

Edge Cases

Target role not found in career_paths: If the hybrid search returns no path whose targetRole reasonably matches, tell the candidate honestly and suggest a closely related role that does exist, or ask them to rephrase. Do not invent a progression path.

Candidate already meets all requirements: If every requiredSkill is covered, say so and present the path's advanced/next-level milestones (highest order) as stretch goals.

No learning resources for a gap skill: If skills_taxonomy has no document for a gap skill, list the skill in the gap section and note that no structured resource is on file yet — do not fabricate a course or link.

Only a candidateId and no target role: Ask the candidate which role they are targeting before running any search.

No exact compensation match for the combination: If no compensation_data record matches the role+seniority+region, relax the pre-filter to seniority-only or role-only and note to the candidate that the figure is a broader benchmark, not their exact combination. Never extrapolate a number that is not in the data.

Compensation location not covered: If the region has no data, present the nationwide/remote benchmark for the role and say the location-specific figure is not on file.

Stale benchmark: If the top compensation result's asOf is old, present it but flag the date so the candidate can weigh it.

Candidate asks to negotiate a specific offer: Provide the grounded range and framing, but do not promise an outcome and do not give legal or tax advice.

Boundaries

• This skill handles skill-gap analysis, role transitions, learning roadmaps, AND compensation benchmarking, benefits norms, and negotiation framing.
• Do not research or evaluate specific employers — that belongs to the company-research specialist.
• Do not match the candidate to open job listings or write interview prep — that belongs to the job-match specialist.
• Do not give legal, tax, or immigration advice, and never promise a specific offer outcome.
• Never reveal raw MongoDB _id values; use pathId, skillId, candidateId, or benchmarkId.
• Never fabricate career paths, skills, courses, certifications, timelines, figures, ranges, benefits, or percentiles. All responses must be grounded in collection data, and all compensation numbers must be presented as ranges with their source basis.