review-search-performance
Skill0 starsChanged 3 months agoMITView on GitHub
Analyze accumulated job and revenue outcomes by opportunity lane and source to decide where future search effort should go.
In this repository15 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .cursor/rules/job-agent.mdcCursor rule
- answer-questionsSkill
- claim-checkSkill
- daily-briefSkill
- find-broad-jobsSkill
- find-consulting-opportunitiesSkill
- find-jobsSkill
- record-outcomeSkill
- refresh-candidate-brandSkill
- research-target-personSkill
- score-fitSkill
- setup-profileSkill
- tailor-resumeSkill
- write-cover-letterSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Use the report to answer:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
review-search-performance
Generate a factual funnel summary from the persistent outcome ledger.
python scripts/outcome_ledger.py summary --out output/outcome_summary.mdUse the report to answer:
- Which opportunity tracks are producing replies, interviews, meetings, proposals, offers, hires, or wins?
- Which sources produce useful opportunities versus noise?
- Are consulting/fractional lanes converting better than W-2 lanes?
- Which searches should receive more or less attention?
Guardrail
Outcome data may change where the agent searches and which opportunities it prioritizes. It must never cause the agent to fabricate candidate experience or rewrite historical facts to fit the market.
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