report

Invoke the analyst agent to run all three operations and deliver the integrated insight report.

Workflow

1. Confirm data exists

Read {user_dir}/CareerNavigator/tracker.json, {user_dir}/CareerNavigator/ExperienceLibrary.json, {user_dir}/CareerNavigator/profile.md, and {user_dir}/CareerNavigator/artifacts-index.json. If the ExperienceLibrary is empty:

"Your ExperienceLibrary is empty. Run /career-navigator:add-source to add a resume first."

If the tracker has no applications, note this inline — pattern analysis will be limited but the other two operations can still run.

1.5 Ensure ATS scores exist (resume artifacts)

Read {user_dir}/CareerNavigator/artifacts-index.json.

If any artifact with type: "resume" is missing ats_score (or has ats_score: null):

• Invoke resume-score for that resume artifact (reference its filename so the skill can look it up and run the ATS check pass).
• If keyword coverage can’t be computed due to missing JD context, still proceed with formatting/narrative ATS checks and write whatever ats_score the ATS pass can compute.
• Re-check {user_dir}/CareerNavigator/artifacts-index.json after the ATS pass(es). If scores are still missing, note the remaining ATS gaps in the report highlights.

2. Invoke analyst — all four operations

Hand off to the analyst agent with:

• The full CareerNavigator/tracker.json
• The full CareerNavigator/ExperienceLibrary.json
• The full artifacts-index.json
• The full CareerNavigator/profile.md
• Instruction to read references/AI_Job_Report-Anthropic-2026-03.pdf before the displacement assessment
• Instruction to run all four operations: outcome patterns (Op 1), transferable strengths (Op 2), AI displacement (Op 3), market benchmark (Op 4)
• Instruction: return graph-ready data in addition to the narrative, suitable for writing to {user_dir}/CareerNavigator/analyst-graph-data.json, using this schema:
  • ai_displacement_outlook: {overall_risk, exposure_min_pct, exposure_max_pct, durable_min_pct, durable_max_pct, durable_differentiators[], narrative_reframe}
  • transferable_strengths: array of {name, rating, score_0_100, evidence, destinations[]}

3. Present the integrated report

In the text report, include only HIGHLIGHTS, PART 1: WHAT'S WORKING IN YOUR SEARCH, and RECOMMENDED NEXT ACTIONS. Do not output PART 2/PART 3/PART 4 sections; instead direct the user to the pipeline dashboard for the AI displacement + transferable strengths graphs and for pipeline/benchmark details.

**Analyst Report** — {today's date}

HIGHLIGHTS
- Search:      {1-sentence verdict on what's working or not}
- Strengths:   {1-sentence on the most transferable capability}
- AI outlook:  {1-sentence on overall displacement risk posture}
- vs. Market:  {1-sentence on how pipeline metrics compare to norm for this level and market}
- ATS status:  {1-liner on whether `ats_score` exists for your resume artifacts (and remaining gaps)}

---

PART 1: WHAT'S WORKING IN YOUR SEARCH
{1–2 sentence highlight for the single most actionable outcome-pattern finding}
{Short detail (3–6 bullets) capturing: what the data shows, what it doesn't show yet, and the 1–2 biggest pipeline bottlenecks}

ExperienceLibrary weights updated: {n} changed ({n} up, {n} down)
Data confidence: {Preliminary / Directional / Moderate / High}

Note: The AI displacement + transferable strengths graphs (and pipeline funnel/benchmark + ExperienceLibrary weights) are interactive in your pipeline dashboard, so I’m not duplicating those charts/tables in the text report.

---

---

PART 3: AI DISPLACEMENT OUTLOOK
{1–2 sentence section highlight before detail}
{Risk profile for current and target roles, durable differentiators, narrative reframe}

---

PART 4: BENCHMARK vs. INDUSTRY NORMS
{1–2 sentence section highlight — are metrics above or below norm for this level and market?}

  Pipeline ({level} · {primary company size} · {geography})
    App → Response:     {user%}  norm {low–high%}  {▲ above / ▼ below / — at norm}
    Response → Screen:  {user%}  norm {low–high%}  {▲/▼/—}
    Screen → Interview: {user%}  norm {low–high%}  {▲/▼/—}
    Interview → Offer:  {user%}  norm {low–high%}  {▲/▼/—}
    Avg days to response: {n}d   norm {low–high}d  {▲/▼/—}
    Ghosting rate:      {user%}  norm {low–high%}  {▲/▼/—}

  ATS Scores
    Avg: {n}/100  ·  threshold: 70+  ·  {above/below}
    Lowest: {score} — {filename}

  {1–2 sentences on compensation positioning vs. level and company size norms, or note to run salary-research if no data}

---

RECOMMENDED NEXT ACTIONS
1. {Most impactful action, drawing on all four analyses}
2. ...

4. Write dashboard graph data (AI displacement + transferable strengths)

Write {user_dir}/CareerNavigator/analyst-graph-data.json (create {user_dir}/CareerNavigator/ if needed) using the graph-ready data returned by the analyst agent in step 2.

Required JSON keys:

• ai_displacement_outlook
• transferable_strengths

If any field is missing due to insufficient data, set it to null (or []) so the dashboard shows placeholders instead of failing.

Dashboard subkeys (when available):

• ai_displacement_outlook: overall_risk, exposure_min_pct, exposure_max_pct, durable_min_pct, durable_max_pct
• transferable_strengths[]: name, rating, score_0_100 (plus optional evidence and destinations[])

5. Generate the pipeline dashboard

After presenting the text report, invoke the pipeline-dashboard skill. It will read the same data files, build the visualization, write {user_dir}/CareerNavigator/pipeline-dashboard.html, and open it in the browser automatically.

Do not wait for the user to ask — generate it every time the report runs.

6. Suggest next step

Based on the most prominent finding across all four analyses:

• If any ATS scores are weak: > "Run /career-navigator:ats-optimization on specific resumes that could be improved."
• If search patterns are weak: > "Run /career-navigator:tailor-resume using the updated ExperienceLibrary weights."
• If transferable strengths suggest a pivot: > "Run /career-navigator:search-jobs targeting {role type}."
• If AI risk is high in current positioning: > "Run /career-navigator:cover-letter to try a reframed narrative."
• If pipeline metrics are below norm: > "Run /career-navigator:benchmark for the full breakdown with targeted fixes."