The parser preserved raw bullet/number formatting in responsibilities, leaking duplicated bullet characters into the extracted text.
What was measured
Noise in output
Does it add incorrect or hallucinated fields?
decisive for this rankingtransformation
Incorrect or hallucinated fields mean the resume parser is not producing reliable structured data, which is central to the job. (3 of 3 judges)
What was given, what came back
Test input: Clean single-column resume — Rugved Nichite · pdf · group: resume-parsing
Input — what we sent
Clean single-column resume — Rugved Nichite
A professionally structured single-column resume for Rugved Nichite, used as the baseline input for parser accuracy across standard resume fields.
Why this input is hard
- · baseline field extraction
- · contact info accuracy
- · work experience parsing
- · education and CGPA extraction
- · skills and certifications extraction
Output — unretouched
Also checked on this input — same tool, 3 other criteria
Accuracy✓ WorkedThe core extracted values were correct on the clean resume, including Rugved Nichite, rugved.nchite@email.com, 9876500000, Vasind, the work histories, the education details, and the 2.9-year total experience figure.Field coverage✓ WorkedOn the clean resume, the parser returned the six core fields expected for resume coverage: name, email, phone, work experience, education, and skills.Output quality◐ MixedThe clean-resume output was moderately usable: the basics were correct, but certifications and GPA were missing and the responsibilities/skills sections still showed formatting artifacts.
Provenance
- Observation
- 2d4131f4-f6fb-4d07-bf37-3d1a2135f950
- 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 — 4 other tools
measured on Noise in output
Affinda✗ FailedThe parser injects non-skill noise into the skills list, including certification-derived items such as AWS Certified Cloud Practitioner and IBM Mainframe.Extracta.ai⚠ StruggledPuts four programming languages — Python, JavaScript, SQL, and Bash — into the Languages field even though the resume does not contain a spoken-languages section.Hireability✗ FailedThe competency extractor introduced noise by labeling all 29 competencies as beginner and including non-skill terms such as Intern, Science, and Framework as competencies.HrFlow✗ FailedIt injects non-skill fragments into the skills list, including 'ml apis', 'lambda', 's3', 'ml', and 'rest apis'.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com