Even on the messy resume, the parser still returned the core coverage set of name, email, phone, work experience, education, and skills.
What was measured
Field coverage
Are name, email, phone, experience, education, and skills extracted?
decisive for this rankingtransformation
A resume parser should extract the key canonical fields; missing them means it is not doing the main job well. (3 of 3 judges)
What was given, what came back
Test input: Messy real-world resume — John Kumar · pdf · group: resume-parsing
Input — what we sent
A poorly formatted, inconsistent real-world resume for John Kumar, used to test robustness against noisy structure, inconsistent dates, and mixed-content sections.
Why this input is hard
- · messy formatting robustness
- · section detection fallback
- · inconsistent date parsing
- · soft-skills extraction
- · noise and hallucination control
Output — unretouched
Also checked on this input — same tool, 3 other criteria
Messy resume handling⚠ StruggledThe tool accepted the messy file, but it degraded badly in structure by merging bullets into a run-on responsibility line instead of preserving clean formatting.Noise in output✗ FailedThe messy-resume skills block was collapsed into a single concatenated string with no separators, making the field hard to reuse downstream.Output quality⚠ StruggledThe messy-resume output degraded sharply: responsibilities became a run-on string, skills collapsed into one concatenated line, and references/certifications/hobbies were missing.
Provenance
- Observation
- 62a9c4d7-49ee-434c-8cd4-b2f01d5be6af
- Evidence run
- cbbef4db-964c-49fa-a57f-a2977822bdfc
- Study
- Parse resumes into structured data using an API
- Research task
- 86b9jm30n
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "skima-ai",
scenario: "resume-parsing"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 7 other tools
measured on Field coverage
Affinda◐ MixedIt extracted one certification but silently dropped the AWS Basics Coursera 2022 certificate, so the certification coverage was incomplete on the messy resume.Airparser✓ WorkedCovers the six core fields even on messy formatting, including contact info, work experience, education, and skills.Extracta.ai✓ WorkedStill covers the main resume fields on the messy input: name, email, phone, experience, education, skills, and certifications.Hireability✗ FailedOnly the B.E. education entry was extracted; the visible 12th and 10th entries were omitted from the parsed output.HrFlow✓ WorkedEven on the messy resume, it still returns the standard contact, experience, education, and technical-skills sections.LlamaParse✓ WorkedThe messy-resume output included the core fields and additional sections: name, contact information, objective, work experience, education, skills, certifications, hobbies, and references.Parseur⚠ StruggledOnly one education record was extracted, and the 12th Science and 10th std entries were omitted from the output.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com