
A candidate listing 'Python' tells you nothing. Used it at BP for 4 years building REST APIs? That tells you everything. How to read CVs for evidence, not keywords.
Two candidates apply for the same role. Both list Python on their CV.
One used Python for four years at BP, building REST APIs for an internal platform. The other completed a Python course last month and added it to their skills section.
Same word. Different candidates. If you match on the word, you can't tell them apart. If you read for evidence, the difference is obvious in thirty seconds โ and it's the thirty seconds that decide whether your client interviews a developer or a hopeful.
A CV's skills section is the candidate's marketing department. It's written to be scanned, to tick boxes, to survive a keyword search. Every word in it is a claim: "I can do this."
There's nothing dishonest about claims. A candidate who's used Python once, on a course, in a lab, isn't lying when they list it โ they just mean something different by it than your client does.
The problem is what happens in your head when you scan: a listed skill feels like a verified skill. Your brain does the keyword tool's job โ word seen, box ticked, next CV. The evidence check takes seconds. The shortcut costs interviews.
The rule
A skill only counts when you can point to where they did it, for how long, and what happened as a result. If you can't point, it's a claim โ fine as a bonus, meaningless as a reason to shortlist.
Every real skill leaves a trail across a CV: an employer, a date range, a description of work, and โ in the best cases โ an outcome. Reading for evidence means following that trail for the three or four skills the role actually depends on.

Four checkpoints. For each one, weak and strong answers look like this:
| Checkpoint | Weak | Adequate | Strong |
|---|---|---|---|
| Where | No employer attached | A company, any company | A name you recognise, in a sector like your client's |
| How long | Appears once, in the summary | Months at one employer | Years, across multiple roles |
| At what scale | No context at all | Some numbers ("several services") | Concrete scale ("40 services", "2m requests/month") |
| With what outcome | Nothing happened | A deliverable ("built the API") | A result ("cut costs 38%", "led the migration") |

The grading isn't binary. "Adequate" is often fine โ for a nice-to-have skill. The question is whether the trail matches the weight the role places on the skill.
Some CVs can be graded in ten seconds with these flags:
โ ๏ธThe cost of skipping this
Every false positive costs you a 45-minute interview slot, the prep time before it, and a notch of credibility with your client when the candidate clearly can't do the job. Ten false positives a month is the difference between a recruiter who's busy and one who's winning business.
Our running example: Senior Cloud Security Engineer, energy-sector client. The ad's three non-negotiables: Azure security, SIEM experience, regulated-environment experience. One CV, run through the trail:
Azure security โ listed prominently. Where? A fintech, 2021โ2024. How long? Three years. Scale? "Managed Azure infrastructure across 30+ services." Outcome? "Cut infrastructure cost 38% during migration." Every checkpoint hit, at a recognisable name. Strong โ shortlist on this alone.
SIEM โ listed in the skills section. Where? Nowhere in any role description. The word appears once, in the summary. Claim without evidence. The keyword matcher's favourite. This CV would have ranked highly on a "SIEM" search โ and the client would have found out in the interview.
Regulated environment โ not listed at all. But read the roles: "4 years at a UK energy trading firm." The candidate never wrote "regulated" โ they lived it. Evidence without the keyword. This is the inverse case: the trail exists, the label doesn't.
The audit takes two minutes. The result is a shortlist decision the recruiter can defend: "Strong on Azure, genuinely regulated-sector, but no SIEM evidence โ worth an interview to probe it, or pass depending on how hard a requirement SIEM is."
That last clause is the point. Evidence reading isn't about accepting or rejecting. It's about knowing exactly what you're shortlisting on.
Not every skill needs a full trail. The bar comes from the job ad, not from habit:
Ten minutes before you touch the CV pile: write down the three skills that decide this role, and what each one's trail has to show. Then read. The bar keeps you fast and keeps you honest.
Claim-checking takes seconds per CV and is the highest-leverage habit in screening. The recruiters who do it aren't working harder than the ones who don't โ they're just spending the same minutes on the decision that matters: is this real experience? Because that question, answered honestly, is what separates a shortlist from a pile.
AI reads CVs like a recruiter: skills with evidence, career trajectory, employer context. Three-stage matching from code gates to full evaluation.

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