| name | job-patterns |
| description | Read application-tracker.csv and outreach-log.csv, find non-obvious patterns in rejections / non-responses / interview rates / stage-of-loss, surface insights as markdown tables plus 3 concrete actionable takeaways. Proactively invoke this skill (do NOT answer conversationally) when the user asks "what's working", "what's not working", "analyze my rejections", "find patterns", "why am I getting rejected", "what's holding me back", "rejection analysis", "where am I losing", "analyze my pipeline", "why no responses", or invokes /job-patterns. Prefer this over `/job-track` or `/job-dashboard` when the user wants diagnosis, not status. |
| argument-hint | (no args — reads both CSVs and renders the analysis) |
| allowed-tools | ["Bash","Read","Write"] |
Job Patterns
Diagnostic companion to /job-track and /job-dashboard. Reads the two CSV sources of truth (application-tracker.csv + outreach-log.csv), slices them along the dimensions that actually predict outcomes (ATS, time-to-rejection, geography, role-type, day-of-week, outreach response), and renders 4-5 markdown tables plus 3 concrete actionable insights.
Where /job-track says what the pipeline looks like, /job-patterns says why it looks that way and what to change.
Repo location
$AI_JOB_AGENT_ROOT → ~/.claude/skills/ai-job-agent/ → REPO_PATH marker file → ~/ai-job-agent/.
Status emoji (consistent with /job-track)
📄 applied · 📬 submitted · 💼 interview · 🎯 offer · ❌ rejected · 🚫 blocked · 🚪 withdrawn
Workflow
Step 1 — Resolve repo + read both CSVs
-
Resolve $AI_JOB_AGENT_ROOT (env var → default skills path → REPO_PATH marker → ~/ai-job-agent/).
-
Read $AI_JOB_AGENT_ROOT/application-tracker.csv (or $LOCAL_TRACKER).
- Columns:
date, company, role, status, location, source, applied_by, url, notes, contact, compensation, days_since, key
-
Read $AI_JOB_AGENT_ROOT/outreach-log.csv (or $OUTREACH_LOG).
- Columns:
sent_at, company, role, to_name, to_email, to_title, to_linkedin, subject, body_file, message_id, status, replied_at, follow_up_count, last_follow_up_at, notes
-
If application-tracker.csv is missing or has 0 rows, tell the user to run /job-apply or bash setup.sh and stop.
-
If both files combined have fewer than 10 application rows, render this and stop:
Not enough data yet (N applications). Patterns surface around 20-30 apps. Keep going for a week or two and re-run /job-patterns.
Step 2 — Compute the six signals
For each signal below, compute the slice → render the table.
Signal A — Rejection rate by ATS source/platform
Group application-tracker.csv by source (or infer from url host if source is empty). Bucket into: linkedin, greenhouse, lever, jobvite, ashby, workday, direct-email, other.
For each platform: count total apps, count rejections, count interviews, compute rejection rate and interview rate.
| Platform | Apps | ❌ Rejected | 💼 Interview | Rejection % | Interview % |
|---|
| greenhouse | 18 | 14 | 1 | 78% | 6% |
| lever | 12 | 7 | 2 | 58% | 17% |
| linkedin | 22 | 10 | 4 | 45% | 18% |
| ashby | 4 | 1 | 1 | 25% | 25% |
| direct-email | 6 | 1 | 3 | 17% | 50% |
Sort by rejection rate descending. Skip platforms with apps < 3 (too noisy) or list them in a separate "low-volume" footnote.
Signal B — Time-to-rejection histogram
For every row where status == rejected, compute days between date (applied) and the rejection (use notes for the rejection date if logged; else fall back to days_since column or skip the row).
Bucket into:
- <24h = ATS keyword filter (resume didn't pass screen)
- 1-3 days = recruiter screen kill (recruiter looked, said no)
- 4-14 days = hiring manager kill (got past recruiter, lost to another candidate)
- >14 days = ghosted / silent (never decisioned)
| Bucket | Likely cause | Count | % of rejections |
|---|
| ❌ <24h | ATS keyword filter | 9 | 38% |
| ❌ 1-3 days | Recruiter screen kill | 5 | 21% |
| ❌ 4-14 days | Hiring manager kill | 4 | 17% |
| ❌ >14 days | Ghosted / silent | 6 | 25% |
The biggest bucket is the user's biggest leak. Call it out explicitly.
Signal C — Geography signal
Group apps by location (normalize: "Remote", "Bay Area", "NYC", "Texas", "Other US", "International"). For each: app count, interview count, interview rate.
| Geography | Apps | 💼 Interviews | Interview % |
|---|
| Bay Area | 14 | 0 | 0% |
| Remote | 18 | 3 | 17% |
| Midwest | 8 | 2 | 25% |
| NYC | 6 | 1 | 17% |
Sort by interview rate descending. Geographies with <3 apps go in a footnote.
Signal D — Role-type signal
Classify each row's role into a bucket via keyword match (case-insensitive):
- software —
software, swe, backend, frontend, full-stack, mobile, ml, ai, data, platform
- electrical —
electrical, ee, power, substation, circuits, hardware, firmware, embedded
- research —
research, scientist, phd, lab, grad
- product/design —
product, pm, design, ux
- other — anything else
| Role type | Apps | 💼 Interviews | ❌ Rejected | Interview % |
|---|
| software | 24 | 4 | 14 | 17% |
| electrical | 18 | 1 | 13 | 6% |
| research | 6 | 2 | 2 | 33% |
| product/design | 4 | 0 | 3 | 0% |
Sort by interview rate descending.
Signal E — Outreach response rate by company tier
Read outreach-log.csv. A row "replied" if replied_at is non-empty OR status ∈ {replied, interview_scheduled}.
If config/search-plan.md exists, read it and tag each company as Tier 1 / Tier 2 / Tier 3 based on the file's company list. If no search-plan, group by to_title instead (vp/director/manager/recruiter/engineer/other).
| Group | Sent | ✉️ Replied | Response % |
|---|
| Tier 1 (target) | 8 | 3 | 38% |
| Tier 2 (warm) | 11 | 2 | 18% |
| Tier 3 (cold) | 14 | 1 | 7% |
Or, when no search-plan exists:
| Recipient title | Sent | ✉️ Replied | Response % |
|---|
| VP / Director | 6 | 3 | 50% |
| Hiring Manager | 9 | 2 | 22% |
| Recruiter | 12 | 1 | 8% |
| Engineer (peer) | 5 | 2 | 40% |
Sort by response rate descending.
Signal F — Day-of-week signal
For each application row, parse date to weekday. For each weekday: count apps + count interviews + compute interview rate. Same for outreach: weekday of sent_at + reply rate.
| Weekday | Apps sent | 💼 Interview % | Outreach sent | ✉️ Reply % |
|---|
| Mon | 12 | 25% | 6 | 33% |
| Tue | 14 | 21% | 8 | 25% |
| Wed | 8 | 13% | 5 | 20% |
| Thu | 9 | 11% | 4 | 25% |
| Fri | 11 | 9% | 3 | 0% |
| Sat | 4 | 0% | 2 | 0% |
| Sun | 6 | 0% | 1 | 0% |
Skip weekdays with apps < 2 (too noisy).
Step 3 — Render the 3 actionable insights
After the tables, output a section titled "What to change this week" with exactly 3 bullets. Each bullet must:
- Cite a specific number from one of the tables above (e.g., "Greenhouse 78% vs LinkedIn 45%").
- State the likely cause in one phrase (e.g., "your Greenhouse-tailored resume is weaker than the LinkedIn one").
- Propose one concrete change the user can do this week (e.g., "tailor a Greenhouse-specific resume variant — keywords, format, length — and re-run 5 Greenhouse apps with it").
Pick the 3 strongest signals — biggest gap, smallest p-value if you can eyeball it, biggest volume × biggest delta. Don't pad. If only 2 signals are strong, output 2 bullets and say "only 2 strong signals at this volume, more to come."
Example output:
What to change this week
- Greenhouse rejection rate is 78% vs LinkedIn 45% — your Greenhouse-tailored resume is weaker than the LinkedIn one (or you're not tailoring per-platform). Build a Greenhouse-specific variant this week and re-run your last 5 Greenhouse rejections with it.
- 38% of rejections come within 24h — that's pure ATS keyword filter, not a recruiter looking. Run
/resume-ats-optimizer against your top 3 target JDs and rewrite the bullets that are missing keywords. Don't apply to anything else until that's done.
- Bay Area apps are 0/14 on interviews while Midwest is 2/8 — you're being filtered out as a non-local Mizzou student in the Bay Area pool. Either add a "willing to relocate, will pay own move" line to your Bay Area cover letter, or stop spending Bay Area applications and double down on Midwest + Remote where the conversion is happening.
Step 4 — Footer
End with a one-liner pointing to the next move:
Run /job-track to see the raw pipeline, /job-followup if you want to nudge the cold contacts, or paste a new job URL to apply with the lessons above.
Rules
- No narrative prose dumps. All findings go in tables. The only prose section is the 3-bullet "What to change this week."
- Show, don't editorialize. Don't say "Greenhouse is bad" — show 78% vs 45% and let the user conclude.
- Honest about volume. If a slice has <3 rows, footnote it as low-volume; don't draw conclusions from it.
- Cite specific numbers in every actionable insight. "Greenhouse 78%" beats "Greenhouse seems high."
- Re-runnable. Read-only on both CSVs. Never mutate
application-tracker.csv or outreach-log.csv.
- Sparse data fallback. <10 apps → tell the user to wait, don't fabricate patterns.
- Status mapping is case-insensitive. Handle
Rejected, rejected, REJECTED the same way.
Related
/job-track — flat view of the tracker, no analysis./job-dashboard — applications + outreach + follow-ups in one snapshot./job-followup — act on the cold-outreach signal once /job-patterns says response rates are low./resume-ats-optimizer — fix the ATS keyword filter problem when Signal B's <24h bucket is winning./tailor-resume — fix per-platform rejection deltas surfaced by Signal A.
