Skills-first capability

A Blackkite company

Skillify turns expert judgment into reusable AI capability.

For design partners who need protected, permissioned workflows shaped by production AI skills and reviewable decision overlays, not generic model output.

Selective design-partner engagement. Conversations stay relevant to your workflow and trust boundary.

Exploratory intro only. Booking does not create a contract, commitment, or commercial obligation.

What design partners get

A skills-first capability layer, not another prompt pack.

Skillify packages expert tacit judgment as reviewable, reusable skills that can be inserted into a real partner workflow, with ownership, permissioning, and trust boundaries kept explicit.

Expert-built decision layer

Capability starts from expert decision logic captured in interviews and tightened through review, not from generic model summarisation.

Production AI skills

Judgment is packaged into triggers, decision logic, and workflow-ready behavior that can travel with the partner’s operating context.

Protected deployment

Expert IP and partner data stay permissioned. Nothing is publicly disclosed without approval, and use remains governed by agreement.

How Skillify works

One connected loop from expert interview to design-partner workflow.

Skillify starts by capturing expert judgment, validates that reasoning with the expert, then packages it as production AI skills and decision overlays for an agreed design-partner workflow.

Expert cycle

Interview → validated skill

Real decision moments are compressed into reviewable skills the expert can check and keep attribution over.

01

Expert interview

We capture real cases, judgment calls, and the reasoning behind them.

02

Skill extraction

Skillify pulls out reusable decision logic, triggers, and edge-case handling.

03

Expert review

The strongest skills are tightened, clarified, and checked with the expert.

04

Validated skill

A private skill artifact is returned for expert review, with participation when approved skills are deployed in live Skillify workflows.

Validated judgment becomes reusable Skillify capability
Design partner cycle

Skill package → deployed workflow

The same skills move from expert-validated artifact into an agreed workflow used by a design partner.

05

Capability packaging

Skills are structured into logic, overlays, and workflow-ready system behavior.

06

Design partner workflow

The capability is inserted into a real operating context rather than left as a memo.

07

Structured output

The partner gets AI workflow output shaped by an expert-built decision layer, not generic AI summarisation.

08

Feedback loop

New partner cases and expert review feed back into the system to improve the capability over time.

Expert-built skills Decision overlays Design partner deployment Continuous learning loop

Why this matters

Generic AI can summarise. It does not reliably know what matters in edge cases.

Tacit judgment

The most valuable part of expert performance is rarely written down. It sits in pattern recognition, exception handling, and what gets weighted when conditions change.

What generic AI misses

Foundation models can restate the obvious. They are less reliable when the job is to notice which assumption just broke and how that should update the decision.

Why partners feel it

If expert judgment is not captured, validated, and kept usable as a skill, partner workflows stay fragile, expensive, and hard to trust where edge cases matter most.

Design partner engagement

A selective working relationship with clear expectations.

01

Intro call

5–15 minutes to confirm fit, workflow relevance, and whether a design-partner track is worth starting. Exploratory only: no contract or commitment implied.

02

Workflow scoping

Identify one concrete operating context where skills-first capability would change output quality.

03

Capability pilot

Deploy a permissioned skill package into that workflow and review outputs against expert standards.

04

Feedback loop

Partner cases and expert review tighten the capability. Deeper involvement is possible, not assumed.

What partners influence

Real say over how capability is packaged and evaluated.

Shape the first skill packages

Help define how expert reasoning is structured for live partner workflows.

Set the evaluation bar

Push the system toward what matters in operating judgment, not generic model theatre.

Early access to capability

See how validated skills translate into decision overlays and deployable workflow tools.

Protected collaboration

Work inside clear permissioning so expert IP and partner context stay governed.

“The days of people making decisions in their own heads are ending.”

Ray Dalio

Founder, Bridgewater Associates

“The vast majority of human knowledge is not expressed in text; it’s in the subconscious part of your mind.”

Yann LeCun

Chief AI Scientist, Meta

Together these capture Skillify’s thesis: more decisions are moving into systems, but the knowledge those systems need often lives in expert judgment rather than text alone.

Knowledge handling and trust

How expert knowledge and partner context are handled matters from the start.

Confidential by default

Interviews, case walkthroughs, partner context, and draft artifacts are treated as private unless a different agreement is made explicitly.

Provenance matters

We care about where a skill came from, how it was derived, and what confidence belongs around it.

Permissioned deployment

The goal is not extraction theatre. It is to structure expert judgment so it remains useful, reviewable, and only used where partners have agreed.

Trust boundary

Skillify does not provide investment advice, trading signals, order execution, broker routing, client-asset management, or guaranteed outcomes. Public examples are illustrative, not live signals or automated trade execution. Experts should not share employer-confidential or client-confidential material. Interviews, partner data, and draft artifacts remain private by default and are only used where permissioned. Nothing is disclosed publicly without explicit agreement. See Disclaimer, Privacy, and Terms.

Who is behind this

Built inside Blackkite Ventures.

Skillify is being developed within Blackkite Ventures as a focused effort to convert tacit expert judgment into validated AI capability.

Gary Pratt

Gary Pratt

Founder

Former macro PM with 10 years of experience building and evaluating macro research processes, including at BFAM Partners and ExodusPoint.

Why this exists

The best workflows often depend on judgment that is real, valuable, and largely undocumented. Skillify is an attempt to preserve and operationalise that layer without pretending generic AI already solved it.

FAQ

Who is this site for?

Design partners evaluating skills-first capability, and experts whose judgment is difficult to replicate with generic AI. We keep outreach selective.

What does skills-first capability mean?

Expert judgment is packaged as production AI skills with triggers, decision logic, and workflow behavior, including reviewable decision overlays, not as a loose prompt library or generic chatbot wrapper.

How long is the intro?

A standard flow starts with a 5–15 minute intro call, then moves into workflow scoping or an expert session only if there is clear mutual fit. The intro is exploratory. Booking does not create a contract or commitment.

Does booking an intro create a contract?

No. Intro calls are exploratory conversations to check fit. No paid engagement, exclusivity, or commercial obligation is implied unless a separate written agreement is made later.

How will information be handled?

Confidential by default. Expert IP and partner data stay permissioned. Nothing is published or shared publicly without agreement. See our Privacy page for more detail.

Is this trading advice or trade execution?

No. Skillify does not provide investment advice, trading signals, order execution, broker routing, client-asset management, or guaranteed alpha. Illustrative examples on this site are not live signals. See the Disclaimer.

Will names or partner details be public?

Not by default. Attribution and public reference only happen with explicit agreement. Raw expert logic and private partner context are not exposed on the public site.

We are speaking with a small number of design partners now.

If your workflow depends on judgment that generic systems do not reliably capture, we would value the conversation.

Exploratory intro only: no contract, commitment, or commercial obligation is implied by booking.