OpenClaw Job Search Matching Agent: Personalized Opportunity Filtering
Search listings, match opportunities to profile keywords, and return curated job recommendations.
0) TL;DR (3-minute launch)
- Job search gets inefficient when listings are spread across many sources and manual filtering eats time.
- Workflow in short: Daily job fetch from approved sources → normalize role/location/compensation metadata → score each listing against your profile constraints → generate ranked shortlist with fit rationale → flag missing info for manual review → track applied/saved/ignored outcomes to tune ranking
- Start fast: Define hard filters first (role type, location, seniority, salary floor).
- Guardrail: Do not auto-apply to jobs without explicit approval.
1) What problem this solves
Job search gets inefficient when listings are spread across many sources and manual filtering eats time. This workflow centralizes discovery, profile matching, and shortlist delivery so you focus on high-fit opportunities.
2) Who this is for
- Operators responsible for career automation decisions
- Builders who need repeatable search workflows
- Teams that want automation with explicit human checkpoints
3) Workflow map
Daily job fetch from approved sources
-> normalize role/location/compensation metadata
-> score each listing against your profile constraints
-> generate ranked shortlist with fit rationale
-> flag missing info for manual review
-> track applied/saved/ignored outcomes to tune ranking4) MVP setup
- Define hard filters first (role type, location, seniority, salary floor)
- Use one profile rubric with weighted skills and must-have criteria
- Limit daily output to top 10 opportunities with clear reasons
- Add one-click status labels: apply, maybe, skip, duplicate
- Review weekly acceptance rate and adjust scoring weights
5) Prompt template
You are my job matching assistant. For today's listings: 1) Remove obvious mismatches using hard filters. 2) Score remaining roles against my profile rubric. 3) Provide top opportunities with concise fit rationale. 4) Highlight red flags (visa mismatch, unclear compensation, suspicious posting). Output: - Top matches - Why matched - Risks/unknowns - Suggested next actions
6) Cost and payoff
Cost
Primary costs are model calls, integration maintenance, and periodic prompt tuning.
Payoff
Faster execution cycles, fewer context switches, and clearer decision quality over time.
Scale
Add role-specific subagents, stronger evaluation metrics, and staged automation permissions.
7) Risk boundaries
- Do not auto-apply to jobs without explicit approval
- Clearly separate verified facts from inferred fit judgment
- Handle personal profile data with minimum necessary exposure
- Preserve source links for every recommendation so users can verify
9) FAQ
How quickly can this workflow deliver value?
Most teams see meaningful results within 1-2 weeks when they keep the initial scope narrow and measurable.
What should stay manual at the beginning?
Keep ambiguous, high-risk, or customer-impacting actions behind explicit human approval until quality is proven.
How do we prevent automation drift over time?
Review logs weekly, sample outputs, and tune prompts/rules as data patterns and business goals change.
What KPI should we track first?
Track one leading metric (speed or coverage) plus one quality metric (accuracy, escalation rate, or user satisfaction).