--- title: "Skima AI" type: "AI Tool" url: "https://aidemos.com/tools/skima-ai" description: "We tested Skima AI on clean, multi-column, and messy resumes; it extracted core fields and experience years, but skills and extras often broke." category: "developer-tools" published: "2026-07-11T16:01:21.143809+00:00" updated: "2026-08-07T14:17:44.384605+00:00" evidenceCount: 31 verifiedCount: 25 coverage: "dense" --- # Skima AI Fast PDF resume parsing with dependable core fields and experience-year calculation, but weak structured output and supplemental coverage. ## TL;DR Verdict **Reliable on core resume facts, weak on structured extraction** **Where it wins:** - You need fast resume intake for PDFs and want the parser to run without manual setup. - You mainly need name, email, phone, work history, education, and an experience-year estimate. - You can clean skills, bullets, and supplemental sections downstream before using the data. **Main limitation:** You need dependable structured JSON or custom field mapping. `PDF resumes` · `3 layouts tested` · `Experience-year calc` · `Skills formatting issues` ## Evidence (first-party, tested) *31 tested cells · 25/31 artifact-verified. Cite a cell by its Evidence ID, e.g. `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·accuracy`.* | Criterion | Scenario | Verdict | Proof | Evidence ID | | --- | --- | --- | --- | --- | | Accuracy | Multi-column sidebar resume — Priya Sharma | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-04-truncated-input2-2aee29b8947f.png) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·accuracy` | | Accuracy | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-02-experience-calc-56f127c576ab.png) | `ev:skima-ai·clean-single-column-resume-rugved-nichite·accuracy` | | Accuracy | cross-scenario | ◐ mixed | 👁 observed | `ev:skima-ai·cross·accuracy` | | Accuracy | Messy real-world resume — John Kumar | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-best-ai-resume-parser-cv-parser-ats-frie-c3ba6b6b2d3c.mp4) | `ev:skima-ai·messy-real-world-resume-john-kumar·accuracy` | | Automation level | cross-scenario | ✓ worked | 👁 observed | `ev:skima-ai·cross·automation-level` | | Contact info — name, email, phone, location: exact match | Multi-column sidebar resume — Priya Sharma | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-best-ai-resume-parser-cv-parser-ats-frie-c3ba6b6b2d3c.mp4) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·contact-info-exact-match` | | Custom field support | cross-scenario | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-best-ai-resume-parser-cv-parser-ats-frie-c3ba6b6b2d3c.mp4) | `ev:skima-ai·cross·custom-field-support` | | Export | cross-scenario | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-image-2-11be129e3f55.png) | `ev:skima-ai·cross·export` | | Export format | cross-scenario | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-image-2-11be129e3f55.png) | `ev:skima-ai·cross·export-format` | | Field coverage | Multi-column sidebar resume — Priya Sharma | ⚠ struggled | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-06-input2-sidebar-missing-07290c0a1129.png) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·field-coverage` | | Field coverage | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-01-input1-gpa-certs-missing-c59774f5e448.png) | `ev:skima-ai·clean-single-column-resume-rugved-nichite·field-coverage` | | Field coverage | cross-scenario | ◐ mixed | 👁 observed | `ev:skima-ai·cross·field-coverage` | | Field coverage | Messy real-world resume — John Kumar | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-05-runon-input3-2809394923e0.png) | `ev:skima-ai·messy-real-world-resume-john-kumar·field-coverage` | | Free tier viability | cross-scenario | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-image-2-11be129e3f55.png) | `ev:skima-ai·cross·free-tier-viability` | | Input handling | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-01-input1-gpa-certs-missing-c59774f5e448.png) | `ev:skima-ai·clean-single-column-resume-rugved-nichite·input-handling` | | Input handling | Messy real-world resume — John Kumar | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-05-runon-input3-2809394923e0.png) | `ev:skima-ai·messy-real-world-resume-john-kumar·input-handling` | | Input handling | Multi-column sidebar resume — Priya Sharma | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-06-input2-sidebar-missing-07290c0a1129.png) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·input-handling` | | Input handling | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-01-input-handling-d64b34a20313.png) | `ev:skima-ai·cross·input-handling` | | Messy resume handling | Messy real-world resume — John Kumar | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-05-runon-input3-2809394923e0.png) | `ev:skima-ai·messy-real-world-resume-john-kumar·messy-resume-handling` | | Multi-column handling | Multi-column sidebar resume — Priya Sharma | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-06-input2-sidebar-missing-07290c0a1129.png) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·multi-column-handling` | | Noise in output | Messy real-world resume — John Kumar | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-09-input3-skills-concatenated-mock-d25303702406.png) | `ev:skima-ai·messy-real-world-resume-john-kumar·noise-in-output` | | Noise in output | Clean single-column resume — Rugved Nichite | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-02-input1-skills-concatenated-mock-96c354edff51.png) | `ev:skima-ai·clean-single-column-resume-rugved-nichite·noise-in-output` | | Noise in output | cross-scenario | ✗ failed | 👁 observed | `ev:skima-ai·cross·noise-in-output` | | Output format | cross-scenario | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-best-ai-resume-parser-cv-parser-ats-frie-c3ba6b6b2d3c.mp4) | `ev:skima-ai·cross·output-format` | | Output quality | Clean single-column resume — Rugved Nichite | ⚠ struggled | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-03-duplicate-bullets-693039b12af9.png) | `ev:skima-ai·clean-single-column-resume-rugved-nichite·output-quality` | | Output quality | Messy real-world resume — John Kumar | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-09-input3-skills-concatenated-mock-d25303702406.png) | `ev:skima-ai·messy-real-world-resume-john-kumar·output-quality` | | Output quality | Multi-column sidebar resume — Priya Sharma | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-04-truncated-input2-2aee29b8947f.png) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·output-quality` | | Output quality | cross-scenario | ✗ failed | 👁 observed | `ev:skima-ai·cross·output-quality` | | Work experience — companies, titles, dates, task completeness | Messy real-world resume — John Kumar | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-best-ai-resume-parser-cv-parser-ats-frie-c3ba6b6b2d3c.mp4) | `ev:skima-ai·messy-real-world-resume-john-kumar·work-experience-completeness` | | Work experience — companies, titles, dates, task completeness | Multi-column sidebar resume — Priya Sharma | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-best-ai-resume-parser-cv-parser-ats-frie-c3ba6b6b2d3c.mp4) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·work-experience-completeness` | | Work experience — companies, titles, dates, task completeness | cross-scenario | ◐ mixed | 👁 observed | `ev:skima-ai·cross·work-experience-completeness` | > 🧾 = artifact-verified (proof captured) · 👁 = observed (noted, no artifact) · verdicts: worked / mixed / struggled / failed. > **Reliable on core resume facts, weak on structured extraction** > > Skima AI consistently extracted 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 or incomplete, so it works better for quick screening than downstream structured parsing. ## Demo Recording [Video: Skima AI demo recording](https://d3epheqghktydj.cloudfront.net/skima-ai-skima-ai-tool-demo-video-3d9fc2931f27.mp4) *Video — Screen recording of the Skima AI resume parsing flow.* ## Feature-by-Feature Breakdown ### Automated Resume Intake and Parsing **Verdict:** Works Skima AI accepts uploaded resume files and parses them automatically from formats like PDF, DOC, DOCX, and Word without per-file schema mapping or layout hints. The tested inputs show direct upload intake and hands-off parsing once a file is provided. **Input:** ``` Resume file upload flow for a PDF/DOCX resume; no custom schema or manual field mapping. ``` **Output:** > **Image** **Input:** 1: Rugved Nichite clean PDF resume > **Image** — 1: Rugved Nichite clean PDF resume **Output:** > **Image** **Input:** 1: Rugved Nichite clean PDF resume **Input:** 2 Priya Sharma multi column PDF resume **Input:** 3: Messy resume for JOHN KUMAR **Bottom line:** Intake is straightforward and fully automated once a file is uploaded. ### Core Candidate Profile Extraction **Verdict:** Reliable core identity capture across all three tested resumes. Skima AI extracts the foundational identity and contact fields from resumes, including name, email, phone, city, and LinkedIn when present. The evidence covers clean, multi-column, and messy resumes, showing this is the most dependable structured output. **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Input:** 1: Rugved Nichite clean PDF resume > **Image** — 1: Rugved Nichite clean PDF resume **Output:** > **Image** **Input:** 2: Priya Sharma multi-column PDF resume > **Image** — 2: Priya Sharma multi-column PDF resume **Output:** > **Image** **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Bottom line:** Core identity fields are dependable across layout styles and were present in all three tested resumes. ### Work and Education History Extraction **Verdict:** Mixed Skima AI pulls structured employment history and education entries from resumes, including employer/title/date blocks, academic history, and some handling of non-standard date phrasing. The tested resumes include cleaner and messier layouts, showing the section extraction works even when formatting quality varies. **Input:** 1: Rugved Nichite clean PDF resume > **Image** — 1: Rugved Nichite clean PDF resume **Output:** > **Image** **Input:** 2: Priya Sharma multi-column PDF resume > **Image** — 2: Priya Sharma multi-column PDF resume **Output:** > **Image** **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Bottom line:** Work history and education are broadly captured, but the fidelity drops as the resume becomes messier. ### Total Experience Calculation **Verdict:** Works Skima AI derives a total professional-experience value from the resume timeline and displays it as a computed output. In the tests, the value stayed consistent across all three resumes. **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Input:** 1: Rugved Nichite clean PDF resume > **Image** — 1: Rugved Nichite clean PDF resume **Output:** > **Image** **Input:** 2: Priya Sharma multi-column PDF resume > **Image** — 2: Priya Sharma multi-column PDF resume **Output:** > **Image** **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Bottom line:** This was the clearest standout behavior and stayed consistent across the three tests. ### Skills Extraction and Normalization **Verdict:** Weak Skima AI extracts skills from resumes and attempts to normalize them into structured output. The observed outputs were inconsistent, ranging from raw strings to collapsed sidebar content and merged lines. **Input:** 1: Rugved Nichite clean PDF resume > **Image** — 1: Rugved Nichite clean PDF resume **Output:** > **Image** **Input:** 2: Priya Sharma multi-column PDF resume > **Image** — 2: Priya Sharma multi-column PDF resume **Output:** > **Image** **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Bottom line:** Skills extraction is present at a surface level, but the output is not machine-friendly without heavy cleanup. ### Supplemental Resume-Section Extraction **Verdict:** Weak Beyond the core profile and main history sections, Skima AI tries to capture additional resume content such as certifications, projects, languages, references, hobbies, and GPA/grade details. The tests show this broader extraction is available but often incomplete, especially on harder layouts. **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Input:** 1: Rugved Nichite clean PDF resume > **Image** — 1: Rugved Nichite clean PDF resume **Output:** > **Image** **Input:** 2: Priya Sharma multi-column PDF resume > **Image** — 2: Priya Sharma multi-column PDF resume **Output:** > **Image** **Bottom line:** Anything beyond the core profile is unreliable, especially in multi-column or messy layouts. ### Responsibility Bullet Cleanup **Verdict:** Weak Skima AI tries to preserve bullet boundaries and sentence endings in responsibility text inside work-history entries. The test outputs still showed duplicate bullets, truncation, and merged run-on strings, so the cleanup is only partially reliable. **Input:** 1: Rugved Nichite clean PDF resume > **Image** — 1: Rugved Nichite clean PDF resume **Output:** > **Image** **Input:** 2: Priya Sharma multi-column PDF resume > **Image** — 2: Priya Sharma multi-column PDF resume **Output:** > **Image** **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Input:** 3: Messy resume for JOHN KUMAR > **Image** — 3: Messy resume for JOHN KUMAR **Output:** > **Image** **Bottom line:** This is not safe to use verbatim; it needs downstream cleanup before ATS ingestion or analytics. ## Is It Right For You? **Use it if** - You need fast resume intake for PDFs and want the parser to run without manual setup. - You mainly need name, email, phone, work history, education, and an experience-year estimate. - You can clean skills, bullets, and supplemental sections downstream before using the data. **Skip it if** - You need dependable structured JSON or custom field mapping. - You rely on certifications, projects, languages, references, hobbies, or GPA being extracted reliably. - You need clean skills lists and bullet-preserving output on multi-column or messy resumes. ## Classification - **Category:** developer-tools - **Subcategory:** apis - **Type:** text ## Frequently Asked Questions **Q: Does Skima AI accept PDF resumes directly?** Yes. The research says it accepted PDF resumes directly and parsed them without per-file configuration or layout hints. **Q: What does Skima AI extract most reliably?** Identity and contact fields, work history, education, and the total-experience estimate were the most consistent results in the research. **Q: Does Skima AI calculate total years of experience?** Yes. The report shows 2.9 years for Rugved Nchite, 6.8 years for Priya Sharma, and 7.3 years for John Kumar. **Q: How does Skima AI handle skills?** Poorly in this research. Skills sometimes collapsed into a single concatenated string, and on the multi-column resume the sidebar skills were not surfaced as a clean list. **Q: Does Skima AI extract certifications, projects, languages, references, or hobbies?** Not reliably. The report says certifications were missed on all three inputs, projects were absent on the multi-column input, and references and hobbies were not captured on the messy input. **Q: Does Skima AI preserve responsibility bullets cleanly?** No. The research found duplicate bullet formatting, truncated lines, and run-on merged responsibilities instead of clean, separate bullets. **Q: Does the research show structured JSON export or custom schema mapping?** No. The report says output was shown on screen, with a fixed predefined schema and no clear structured JSON export or custom field-mapping feature observed. **Q: Was pricing or trial access verified in this research?** No pricing or trial terms were stated in the report, so pricing should be treated as unverified from this research alone. ## Similar Tools AI tools similar to Skima AI: - [Affinda](https://aidemos.com/tools/affinda) — Affinda Review: AI Resume Parser Tested Across Resume Formats (2026) - [Airparser](https://aidemos.com/tools/airparser) — Structured resume parsing across clean, multi-column, and messy PDFs, with fast JSON output and a few field-quality caveats. - [LlamaParse](https://aidemos.com/tools/llamaparse) — Reliable PDF-to-Markdown conversion for hybrid reports, with strong hierarchy and table capture but weaker preservation of complex table semantics and embedded visuals. - [Parseur](https://aidemos.com/tools/parseur) — Template-driven resume parsing that returns clean JSON after one-time schema setup. - [HrFlow](https://aidemos.com/tools/hrflow) — HrFlow Review: AI Resume Parsing API Tested (2026) - [Hireability](https://aidemos.com/tools/hireability) — HireAbility Review: Resume Parsing API for Structured Data Extraction Tested (2026) ## Need a custom AI solution for this use case? If you are looking to build a custom resume parsing, resume extraction, or candidate profile system for your business or internal workflow, email us at [contact@futuresmart.ai](mailto:contact@futuresmart.ai). ### Found something inaccurate or missing? We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at [collaborate@aidemos.com](mailto:collaborate@aidemos.com).