--- 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 reliably 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: "2026-08-08T18:06:52.745429+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 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:** Free Free · Premium $79/user/month (estimate range $49–$79/user/month) · Enterprise Custom pricing `3 resume inputs` · `Experience total extracted` · `Skills formatting issues` · `Supplemental fields missing` ## Evidence (first-party, tested) *31 tested cells · 25/31 artifact-verified. Cite a cell by its Evidence ID, e.g. `ev:skima-ai·clean-single-column-resume-rugved-nichite·accuracy`.* | Criterion | Scenario | Verdict | Proof | Evidence ID | | --- | --- | --- | --- | --- | | Accuracy | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-image-2-11be129e3f55.png) | `ev:skima-ai·clean-single-column-resume-rugved-nichite·accuracy` | | Accuracy | Messy real-world resume — John Kumar | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-skima-task3-certs-refs-hobbies-missing-277792520292.png) | `ev:skima-ai·messy-real-world-resume-john-kumar·accuracy` | | Accuracy | Multi-column sidebar resume — Priya Sharma | ◐ mixed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-04-truncated-input2-2aee29b8947f.png) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·accuracy` | | Accuracy | cross-scenario | ◐ mixed | 👁 observed | `ev:skima-ai·cross·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-01-input-handling-d64b34a20313.png) | `ev:skima-ai·cross·export-format` | | Field coverage | Multi-column sidebar resume — Priya Sharma | ◐ mixed | 🧾 [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 | 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` | | Field coverage | Clean single-column resume — Rugved Nichite | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-image-4-d3d76b30dad1.png) | `ev:skima-ai·clean-single-column-resume-rugved-nichite·field-coverage` | | Field coverage | cross-scenario | ◐ mixed | 👁 observed | `ev:skima-ai·cross·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 | cross-scenario | ✓ worked | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-01-input-handling-d64b34a20313.png) | `ev:skima-ai·cross·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 | 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` | | Messy resume handling | Messy real-world resume — John Kumar | ✗ failed | 🧾 [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-03-duplicate-bullets-693039b12af9.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-01-input-handling-d64b34a20313.png) | `ev:skima-ai·cross·output-format` | | Output quality | cross-scenario | ✗ failed | 👁 observed | `ev:skima-ai·cross·output-quality` | | Output quality | Multi-column sidebar resume — Priya Sharma | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-07-input2-projects-missing-ec02733eb1a4.png) | `ev:skima-ai·multi-column-sidebar-resume-priya-sharma·output-quality` | | Output quality | Messy real-world resume — John Kumar | ✗ failed | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/skima-ai-05-runon-input3-2809394923e0.png) | `ev:skima-ai·messy-real-world-resume-john-kumar·output-quality` | | 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` | | 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 | 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 | 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 fields, but weak on structured output fidelity** > > 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. ## 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 workflow.* ## Feature-by-Feature Breakdown ### Automated Resume Parsing **Verdict:** Automatic 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. **Input:** ``` Resume file upload flow for a PDF/DOCX resume; no custom schema or manual field mapping. ``` **Output:** > **Image** **Input:** ``` Rugved Nchite clean PDF resume with no layout hints; the goal was to parse the file automatically. ``` **Output:** > **Image** **Input:** ``` Priya Sharma multi-column PDF resume with a right-hand skills, certifications, and languages sidebar. ``` **Output:** > **Image** **Input:** ``` JOHN KUMAR messy PDF resume with inconsistent formatting, merged bullets, and non-standard structure. ``` **Output:** > **Image** **Bottom line:** Parsing is automatic and resilient to file/layout variety, but quality falls off once the layout gets complex. ### Core Profile Extraction **Verdict:** Reliable core identity capture across 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. **Input:** 1: Rugved Nichite clean PDF resume with identity/contact fields, two jobs, education, skills, and certifications. > **Image** — 1: Rugved Nichite clean PDF resume with identity/contact fields, two jobs, education, skills, and certifications. **Output:** > **Image** **Input:** 2: Priya Sharma multi-column PDF resume with sidebar skills, certifications, languages, and key projects. > **Image** — 2: Priya Sharma multi-column PDF resume with sidebar skills, certifications, languages, and key projects. **Output:** > **Image** **Input:** 3: John Kumar messy PDF resume with run-on responsibilities, certifications, references, and hobbies. > **Image** — 3: John Kumar messy PDF resume with run-on responsibilities, certifications, references, and hobbies. **Output:** > **Image** **Input:** ``` Rugved Nchite clean resume with name, email, phone, city, and LinkedIn. ``` **Output:** > **Image** **Bottom line:** Core identity fields are dependable across layout styles and were present in all three tested resumes. ### Skills Extraction and Normalization **Verdict:** Unreliable 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. **Input:** The skills field collapsed > **Image** — The skills field collapsed **Output:** > **Image** **Input:** John Kumar messy resume > **Image** — John Kumar messy resume **Output:** > **Image** **Input:** ``` Priya Sharma multi-column resume with skills in the right-hand sidebar. ``` **Output:** > **Image** **Bottom line:** Skills handling is inconsistent and often unusable downstream without post-processing. ### Responsibility Bullet Extraction **Verdict:** Weak 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. **Input:** Clean resume responsibilities section with multiple bullets under the Software Developer Intern role. > **Image** — Clean resume responsibilities section with multiple bullets under the Software Developer Intern role. **Output:** > **Image** **Input:** Source resume snippet where the second responsibility bullet ends mid-sentence after 'cutting costs by.' > **Image** — Source resume snippet where the second responsibility bullet ends mid-sentence after 'cutting costs by.' **Output:** > **Image** **Bottom line:** Do not use responsibility text verbatim; important metrics can be cut off or merged away. ### Supplemental Section Extraction **Verdict:** Incomplete 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. **Input:** ``` Rugved Nchite clean resume with CGPA 8.2/10 and two certifications. ``` **Output:** > **Image** **Input:** ``` Priya Sharma multi-column resume with projects and a right-hand skills, certifications, and languages sidebar. ``` **Output:** > **Image** **Input:** ``` JOHN KUMAR resume with certifications, references, and hobbies. ``` **Output:** > **Image** **Bottom line:** Supplemental fields are the weakest part of the parser and were the most likely to disappear. ## Pricing & access verified August 2026 Free plan tested; exact free-tier caps weren't published. | Plan | Price | Notes | | --- | --- | --- | | Free (tested) | 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.* ## Is It Right For You? **Use it 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 it 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. ## Classification - **Category:** developer-tools - **Subcategory:** apis - **Type:** text - **Built for:** Other ## Frequently Asked Questions **Q: Does Skima AI accept PDF resumes directly?** Yes. The report shows all three tested resumes were parsed successfully after upload, without manual setup or layout hints. **Q: What fields does Skima AI extract most reliably?** Name, email, phone, city, work history, education, and an overall experience total were the most consistent fields in the tests. **Q: Does Skima AI calculate total years of experience?** Yes. The three test resumes showed 2.9 years, 6.8 years, and 7.3 years. **Q: How does Skima AI handle skills?** 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. **Q: Does Skima AI preserve responsibility bullets cleanly?** No. One test truncated a bullet mid-sentence, another merged multiple bullets into a run-on string, and another showed duplicate bullet formatting. **Q: Are certifications, projects, languages, references, hobbies, and GPA extracted?** 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. **Q: Does the research show structured JSON export or custom schema mapping?** No. The report says output was displayed on screen, and it did not show structured JSON export or custom field mapping. **Q: Was pricing or trial access verified?** 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. ## 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) - [OpenResume](https://aidemos.com/tools/openresume) — Free browser resume parser that is handy for quick manual review on clean single-column PDFs, but brittle field mapping and no JSON export make it unsuitable for API pipelines. - [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, experience extraction, or candidate data extraction 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).