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Skima AI

Fast PDF resume parsing with dependable core fields and experience-year calculation, but weak structured output and supplemental coverage.

3 resume inputsExperience total extractedSkills formatting issuesSupplemental fields missing
TL;DR — our verdictUpdated August 2026 · 16 test artifacts

Reliable on core fields, but weak on structured output fidelity

Where it wins
  • You need fast parsing for uploaded PDF resumes and can work with a fixed schema.
  • You mainly need name, contact info, work history, education, and a computed experience total.
  • You can normalize skills, bullets, and supplemental sections downstream before using the data.
Main limitation
  • You need reliable machine-readable skills lists or clean bullet points out of the box.
Pricing (verified plans)
Free FreePremium $79/user/monthEnterprise Custom pricing
Strongest test artifacts
OUTPUT →Skills Extraction and Normalization →Responsibility Bullet Extraction →

Our take

Skima AI was dependable for the basics: it consistently parsed identity/contact data, work history, education, and a total-experience estimate across clean, multi-column, and messy resumes. The tradeoff is output quality: skills can collapse into unreadable strings, responsibility bullets can truncate or run on, and certifications, projects, languages, references, hobbies, and GPA are often missing, so it is useful for quick screening but not for clean downstream structured export without cleanup.

Download video: Demo video
Screen recording of the Skima AI resume parsing workflow.

In-Depth Review

Our detailed analysis of Skima AI — features, performance, and real-world testing.

AD
AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Automated Resume Parsing
Automatic
▾
Test Summary
Feature tested: Automated Resume Parsing
Result: Passed — Automatic

Feature tested: Automated Resume Parsing

Result: Passed

Verdict: Automatic

Expected behavior: Skima AI accepts uploaded resume files and parses them automatically without manual template setup or layout hints. The evidence exercised this on a clean resume, a multi-column/sidebar resume, a messy resume, and direct PDF uploads.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The upload modal accepts drag-and-drop or click-to-upload and lists PDF, doc, docx, or Word files, with parsing proceeding automatically after upload. — 01_input_handling.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The upload modal accepts drag-and-drop or click-to-upload and lists PDF, doc, docx, or Word files, with parsing proceeding automatically after upload. — 01_input_handling.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Parsing completed successfully on a clean resume without manual configuration; the parser returned core fields and a 2.9-year experience total. — 02_experience_calc.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Parsing completed successfully on a clean resume without manual configuration; the parser returned core fields and a 2.9-year experience total. — 02_experience_calc.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The multi-column resume parsed successfully, showing that the tool can ingest sidebar layouts even though supplemental extraction is incomplete. — skima_task2_sidebar-skills-certs-languages-missing.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The multi-column resume parsed successfully, showing that the tool can ingest sidebar layouts even though supplemental extraction is incomplete. — skima_task2_sidebar-skills-certs-languages-missing.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The parser accepted the messy resume and returned a result page rather than failing, but the extracted responsibilities were badly formatted. — skima_task3_runon-string.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The parser accepted the messy resume and returned a result page rather than failing, but the extracted responsibilities were badly formatted. — skima_task3_runon-string.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Parsing is automatic and resilient to file/layout variety, but quality falls off once the layout gets complex.

Skima AI accepts uploaded resume files and parses them automatically without manual template setup or layout hints. The evidence exercised this on a clean resume, a multi-column/sidebar resume, a messy resume, and direct PDF uploads.

text
Resume file upload flow for a PDF/DOCX resume; no custom schema or manual field mapping.
↓
image
Output artifact for "Automated Resume Parsing" test: The upload modal accepts drag-and-drop or click-to-upload and lists PDF, doc, docx, or Word files, with parsing proceeding automatically after upload., 01_input_handling.png
The upload modal accepts drag-and-drop or click-to-upload and lists PDF, doc, docx, or Word files, with parsing proceeding automatically after upload.
text
Rugved Nchite clean PDF resume with no layout hints; the goal was to parse the file automatically.
↓
image
Output artifact for "Automated Resume Parsing" test: Parsing completed successfully on a clean resume without manual configuration; the parser returned core fields and a 2.9-year experience total., 02_experience_calc.png
Parsing completed successfully on a clean resume without manual configuration; the parser returned core fields and a 2.9-year experience total.
text
Priya Sharma multi-column PDF resume with a right-hand skills, certifications, and languages sidebar.
↓
image
Output artifact for "Automated Resume Parsing" test: The multi-column resume parsed successfully, showing that the tool can ingest sidebar layouts even though supplemental extraction is incomplete., skima_task2_sidebar-skills-certs-languages-missing.png
The multi-column resume parsed successfully, showing that the tool can ingest sidebar layouts even though supplemental extraction is incomplete.
text
JOHN KUMAR messy PDF resume with inconsistent formatting, merged bullets, and non-standard structure.
↓
image
Output artifact for "Automated Resume Parsing" test: The parser accepted the messy resume and returned a result page rather than failing, but the extracted responsibilities were badly formatted., skima_task3_runon-string.png
The parser accepted the messy resume and returned a result page rather than failing, but the extracted responsibilities were badly formatted.
Bottom Line
Parsing is automatic and resilient to file/layout variety, but quality falls off once the layout gets complex.
From our researchParse resumes into structured data using an APIearlier research
Core Profile Extraction
Reliable core identity capture across all three tested resumes.
▾
Test Summary
Feature tested: Core Profile Extraction
Result: Partial — Reliable core identity capture across all three tested resumes.

Feature tested: Core Profile Extraction

Result: Partial

Verdict: Reliable core identity capture across all three tested resumes.

Expected behavior: Skima AI extracts core identity and contact details such as name, email, phone, city, and LinkedIn when present. The evidence covered clean, multi-column, and messy resumes, with these fields staying the most stable across outputs.

Test case: Artifact → Image

Input type: Artifact

Input used: Input artifact (Artifact): Input 1: Rugved Nichite clean PDF resume with identity/contact fields, two jobs, education, skills, and certifications.

Observed output: Output artifact (Image): Rugved Nichite's parsed result includes name, email, phone, city, work history, and education; the report says GPA and certifications were not extracted as structured fields. — skima_task1_gpa-certs-missing.png

Input artifact: Input artifact (Artifact): Input 1: Rugved Nichite clean PDF resume with identity/contact fields, two jobs, education, skills, and certifications.

Output artifact: Output artifact (Image): Rugved Nichite's parsed result includes name, email, phone, city, work history, and education; the report says GPA and certifications were not extracted as structured fields. — skima_task1_gpa-certs-missing.png

What changed: Artifact transformed into Image

Test case: Artifact → Image

Input type: Artifact

Input used: Input artifact (Artifact): Input 2: Priya Sharma multi-column PDF resume with sidebar skills, certifications, languages, and key projects.

Observed output: Output artifact (Image): Priya Sharma's parsed result includes name, email, phone, LinkedIn, and city alongside the resume preview. — image-5.png

Input artifact: Input artifact (Artifact): Input 2: Priya Sharma multi-column PDF resume with sidebar skills, certifications, languages, and key projects.

Output artifact: Output artifact (Image): Priya Sharma's parsed result includes name, email, phone, LinkedIn, and city alongside the resume preview. — image-5.png

What changed: Artifact transformed into Image

Test case: Artifact → Image

Input type: Artifact

Input used: Input artifact (Artifact): Input 3: John Kumar messy PDF resume with run-on responsibilities, certifications, references, and hobbies.

Observed output: Output artifact (Image): John Kumar's parsed result still captures name, email, phone, city, and work history even though the responsibilities text is badly merged. — skima_task3_responsibilities-runon-string.png

Input artifact: Input artifact (Artifact): Input 3: John Kumar messy PDF resume with run-on responsibilities, certifications, references, and hobbies.

Output artifact: Output artifact (Image): John Kumar's parsed result still captures name, email, phone, city, and work history even though the responsibilities text is badly merged. — skima_task3_responsibilities-runon-string.png

What changed: Artifact transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The clean resume output shows the candidate identity and contact block, including name, email, phone, city, and the parsed profile section. — 02_experience_calc.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The clean resume output shows the candidate identity and contact block, including name, email, phone, city, and the parsed profile section. — 02_experience_calc.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Core identity fields are dependable across layout styles and were present in all three tested resumes.

Skima AI extracts core identity and contact details such as name, email, phone, city, and LinkedIn when present. The evidence covered clean, multi-column, and messy resumes, with these fields staying the most stable across outputs.

image
Input 1: Rugved Nichite clean PDF resume with identity/contact fields, two jobs, education, skills, and certifications.
↓
image
Output artifact for "Core Profile Extraction" test: Rugved Nichite's parsed result includes name, email, phone, city, work history, and education; the report says GPA and certifications were not extracted as structured fields., skima_task1_gpa-certs-missing.png
Rugved Nichite's parsed result includes name, email, phone, city, work history, and education; the report says GPA and certifications were not extracted as structured fields.
image
Input 2: Priya Sharma multi-column PDF resume with sidebar skills, certifications, languages, and key projects.
↓
image
Output artifact for "Core Profile Extraction" test: Priya Sharma's parsed result includes name, email, phone, LinkedIn, and city alongside the resume preview., image-5.png
Priya Sharma's parsed result includes name, email, phone, LinkedIn, and city alongside the resume preview.
image
Input 3: John Kumar messy PDF resume with run-on responsibilities, certifications, references, and hobbies.
↓
image
Output artifact for "Core Profile Extraction" test: John Kumar's parsed result still captures name, email, phone, city, and work history even though the responsibilities text is badly merged., skima_task3_responsibilities-runon-string.png
John Kumar's parsed result still captures name, email, phone, city, and work history even though the responsibilities text is badly merged.
text
Rugved Nchite clean resume with name, email, phone, city, and LinkedIn.
↓
image
Output artifact for "Core Profile Extraction" test: The clean resume output shows the candidate identity and contact block, including name, email, phone, city, and the parsed profile section., 02_experience_calc.png
The clean resume output shows the candidate identity and contact block, including name, email, phone, city, and the parsed profile section.
Bottom Line
Core identity fields are dependable across layout styles and were present in all three tested resumes.
From our researchParse resumes into structured data using an APIearlier research
Skills Extraction and Normalization
Unreliable
▾
Test Summary
Feature tested: Skills Extraction and Normalization
Result: Failed — Unreliable

Feature tested: Skills Extraction and Normalization

Result: Failed

Verdict: Unreliable

Expected behavior: Skima AI extracts skills from the SKILLS section and attempts to normalize them into readable output. The evidence showed mixed results across resumes, including readable lists, collapsed strings, and missing sidebar skills.

Test case: Artifact → Image

Input type: Artifact

Input used: Input artifact (Artifact): The skills field collapsed

Observed output: Output artifact (Image): The skills field collapsed into a single concatenated string with no separators or chips. — 02_input1_skills_concatenated_mock-2.png

Input artifact: Input artifact (Artifact): The skills field collapsed

Output artifact: Output artifact (Image): The skills field collapsed into a single concatenated string with no separators or chips. — 02_input1_skills_concatenated_mock-2.png

What changed: Artifact transformed into Image

Test case: Artifact → Image

Input type: Artifact

Input used: Input artifact (Artifact): John Kumar messy resume

Observed output: Output artifact (Image): A second messy resume produced another long run-on skills string with no readable tokenization. — 09_input3_skills_concatenated_mock-2.png

Input artifact: Input artifact (Artifact): John Kumar messy resume

Output artifact: Output artifact (Image): A second messy resume produced another long run-on skills string with no readable tokenization. — 09_input3_skills_concatenated_mock-2.png

What changed: Artifact transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The sidebar skills block is not extracted cleanly in this multi-column layout, so the skills section is effectively missing from the parsed result. — skima_task2_sidebar-skills-certs-languages-missing.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The sidebar skills block is not extracted cleanly in this multi-column layout, so the skills section is effectively missing from the parsed result. — skima_task2_sidebar-skills-certs-languages-missing.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Skills handling is inconsistent and often unusable downstream without post-processing.

Skima AI extracts skills from the SKILLS section and attempts to normalize them into readable output. The evidence showed mixed results across resumes, including readable lists, collapsed strings, and missing sidebar skills.

image
The skills field collapsed
↓
image
Output artifact for "Skills Extraction and Normalization" test: The skills field collapsed into a single concatenated string with no separators or chips., 02_input1_skills_concatenated_mock-2.png
The skills field collapsed into a single concatenated string with no separators or chips.
image
John Kumar messy resume
↓
image
Output artifact for "Skills Extraction and Normalization" test: A second messy resume produced another long run-on skills string with no readable tokenization., 09_input3_skills_concatenated_mock-2.png
A second messy resume produced another long run-on skills string with no readable tokenization.
text
Priya Sharma multi-column resume with skills in the right-hand sidebar.
↓
image
Output artifact for "Skills Extraction and Normalization" test: The sidebar skills block is not extracted cleanly in this multi-column layout, so the skills section is effectively missing from the parsed result., skima_task2_sidebar-skills-certs-languages-missing.png
The sidebar skills block is not extracted cleanly in this multi-column layout, so the skills section is effectively missing from the parsed result.
Bottom Line
Skills handling is inconsistent and often unusable downstream without post-processing.
From our researchParse resumes into structured data using an APIearlier research
Responsibility Bullet Extraction
Weak
▾
Test Summary
Feature tested: Responsibility Bullet Extraction
Result: Failed — Weak

Feature tested: Responsibility Bullet Extraction

Result: Failed

Verdict: Weak

Expected behavior: Skima AI attempts to preserve bullet-point responsibilities from work history entries on bullet-heavy resumes. The tested outputs showed truncation, run-on merges, duplicated markers, and other formatting degradation.

Test case: Artifact → Image

Input type: Artifact

Input used: Input artifact (Artifact): Clean resume responsibilities section with multiple bullets under the Software Developer Intern role.

Observed output: Output artifact (Image): Two responsibility bullets showed duplicate numbering and awkward wrapping, indicating the parser preserved raw bullet artifacts. — skima_task1_duplicate-bullet-formatting.png

Input artifact: Input artifact (Artifact): Clean resume responsibilities section with multiple bullets under the Software Developer Intern role.

Output artifact: Output artifact (Image): Two responsibility bullets showed duplicate numbering and awkward wrapping, indicating the parser preserved raw bullet artifacts. — skima_task1_duplicate-bullet-formatting.png

What changed: Artifact transformed into Image

Test case: Artifact → Image

Input type: Artifact

Input used: Input artifact (Artifact): Source resume snippet where the second responsibility bullet ends mid-sentence after 'cutting costs by.'

Observed output: Output artifact (Image): The second responsibility bullet is cut off after 'cutting costs by.', losing the quantified result and leaving the line incomplete. — 04_truncated_input2-2.png

Input artifact: Input artifact (Artifact): Source resume snippet where the second responsibility bullet ends mid-sentence after 'cutting costs by.'

Output artifact: Output artifact (Image): The second responsibility bullet is cut off after 'cutting costs by.', losing the quantified result and leaving the line incomplete. — 04_truncated_input2-2.png

What changed: Artifact transformed into Image

Why it matters / Conclusion: Do not use responsibility text verbatim; important metrics can be cut off or merged away.

Skima AI attempts to preserve bullet-point responsibilities from work history entries on bullet-heavy resumes. The tested outputs showed truncation, run-on merges, duplicated markers, and other formatting degradation.

image
Clean resume responsibilities section with multiple bullets under the Software Developer Intern role.
↓
image
Output artifact for "Responsibility Bullet Extraction" test: Two responsibility bullets showed duplicate numbering and awkward wrapping, indicating the parser preserved raw bullet artifacts., skima_task1_duplicate-bullet-formatting.png
Two responsibility bullets showed duplicate numbering and awkward wrapping, indicating the parser preserved raw bullet artifacts.
image
Source resume snippet where the second responsibility bullet ends mid-sentence after 'cutting costs by.'
↓
image
Output artifact for "Responsibility Bullet Extraction" test: The second responsibility bullet is cut off after 'cutting costs by.', losing the quantified result and leaving the line incomplete., 04_truncated_input2-2.png
The second responsibility bullet is cut off after 'cutting costs by.', losing the quantified result and leaving the line incomplete.
Bottom Line
Do not use responsibility text verbatim; important metrics can be cut off or merged away.
From our researchParse resumes into structured data using an API
Supplemental Section Extraction
Incomplete
▾
Test Summary
Feature tested: Supplemental Section Extraction
Result: Failed — Incomplete

Feature tested: Supplemental Section Extraction

Result: Failed

Verdict: Incomplete

Expected behavior: Skima AI tries to capture non-core resume sections such as certifications, projects, languages, references, hobbies, and GPA. The evidence shows these supplemental fields were inconsistent, incomplete, or often missing altogether.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): GPA and certifications are absent from the parsed output even though they are present in the source resume. — 01_input1_gpa_certs_missing.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): GPA and certifications are absent from the parsed output even though they are present in the source resume. — 01_input1_gpa_certs_missing.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The projects section is not carried through cleanly, and the sidebar supplemental fields are missing or incomplete in the parsed output. — skima_task2_projects-missing.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The projects section is not carried through cleanly, and the sidebar supplemental fields are missing or incomplete in the parsed output. — skima_task2_projects-missing.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Certifications, references, and hobbies are absent from the parsed output. — skima_task3_certs-refs-hobbies-missing.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Certifications, references, and hobbies are absent from the parsed output. — skima_task3_certs-refs-hobbies-missing.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Supplemental fields are the weakest part of the parser and were the most likely to disappear.

Skima AI tries to capture non-core resume sections such as certifications, projects, languages, references, hobbies, and GPA. The evidence shows these supplemental fields were inconsistent, incomplete, or often missing altogether.

text
Rugved Nchite clean resume with CGPA 8.2/10 and two certifications.
↓
image
Output artifact for "Supplemental Section Extraction" test: GPA and certifications are absent from the parsed output even though they are present in the source resume., 01_input1_gpa_certs_missing.png
GPA and certifications are absent from the parsed output even though they are present in the source resume.
text
Priya Sharma multi-column resume with projects and a right-hand skills, certifications, and languages sidebar.
↓
image
Output artifact for "Supplemental Section Extraction" test: The projects section is not carried through cleanly, and the sidebar supplemental fields are missing or incomplete in the parsed output., skima_task2_projects-missing.png
The projects section is not carried through cleanly, and the sidebar supplemental fields are missing or incomplete in the parsed output.
text
JOHN KUMAR resume with certifications, references, and hobbies.
↓
image
Output artifact for "Supplemental Section Extraction" test: Certifications, references, and hobbies are absent from the parsed output., skima_task3_certs-refs-hobbies-missing.png
Certifications, references, and hobbies are absent from the parsed output.
Bottom Line
Supplemental fields are the weakest part of the parser and were the most likely to disappear.
From our researchParse resumes into structured data using an API

Pricing & access verified August 2026

Free plan tested; exact free-tier caps weren't published.

TESTED
Free
Free
Test resume parsing capabilities before upgrading; 14-day free trial
Premium
$79/user/month (estimate range $49–$79/user/month)
AI Resume Parser extracting 200+ data points; AI Matching Score for candidate ranking
Enterprise
Custom pricing
Customizable tier; contact sales for details

Pricing checked August 2026. The pricing page was JS-rendered, and the report notes that exact free-tier limits were not published on-site; the quoted paid-tier figures were corroborated via third-party sources.

✓ Use This If
●You need fast parsing for uploaded PDF resumes and can work with a fixed schema.
●You mainly need name, contact info, work history, education, and a computed experience total.
●You can normalize skills, bullets, and supplemental sections downstream before using the data.
✕ Skip This If
●You need reliable machine-readable skills lists or clean bullet points out of the box.
●You rely on certifications, projects, languages, references, hobbies, or GPA being captured consistently.
●You need structured JSON export or custom field mapping.
developer-toolsapistextOther
Yes. The report shows all three tested resumes were parsed successfully after upload, without manual setup or layout hints.
Name, email, phone, city, work history, education, and an overall experience total were the most consistent fields in the tests.
Yes. The three test resumes showed 2.9 years, 6.8 years, and 7.3 years.
Poorly in the tested cases. One clean resume and one messy resume collapsed skills into a single unspaced string, and the multi-column resume lost the skills sidebar altogether.
No. One test truncated a bullet mid-sentence, another merged multiple bullets into a run-on string, and another showed duplicate bullet formatting.
Not reliably. GPA and certifications were missing on the clean resume; projects and sidebar supplemental fields were missing on the multi-column resume; certifications, references, and hobbies were missing on the messy resume.
No. The report says output was displayed on screen, and it did not show structured JSON export or custom field mapping.
Yes. Pricing was checked in August 2026, the free plan was tested, and the report lists Free, Premium, and Enterprise. The report also notes that exact free-tier limits were not published on-site.

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