Capture the skill
Save the technique, position, transition, finish, escape, or tactical pattern the grappler may want to work on.
Hone helps serious grapplers and ambitious hobbyists choose specific skills, train them with intention, and track the evidence that a technique is moving from interesting idea to live, reliable execution.
The app should not try to track all training. It should help grapplers choose specific skills, practice them deliberately, and see which ones are progressing from saved ideas to live execution.
Save the technique, position, transition, finish, escape, or tactical pattern the grappler may want to work on.
Record the training format: study, dialogue drilling, constraint games, or rolling.
Track time, successful executions, partner hit counts, footage, and insights so progress is visible.
Each stage describes where the skill is in the user's acquisition process, from a captured idea to a technique that works repeatedly against meaningful resistance.
I captured this skill because I may want to work on it later.
I am actively developing the basic mechanics and procedural understanding needed to perform it.
I can create or recognize the conditions for the skill and execute it against resistance.
I can hit it repeatedly on strong partners, including people who know my game or are hard to catch.
A specific thing the user wants to acquire.
A class, open mat, private, or competition training day.
A chunk of work inside a session tied to one skill.
A person the user has successfully executed a skill against.
Creating a skill should be fast: name the thing the user wants to work on, attach anything useful, and drop it into the pipeline.
Create a skill manually from an idea, coach suggestion, or training goal.
Save a video as a reference and attach notes, cues, or timestamps.
Capture a reel or post before it disappears into saved-content clutter.
Attach any video that helps the user understand what good execution looks like.
Save a thought after training and turn it into a skill later.
For each skill, users can log study, dialogue drilling, constraint games, and rolling. Each training type uses its own metrics: time, successful executions, partner hits, footage, and insights where they apply.
Watching, reading, reviewing footage, or thinking through the skill away from live practice.
Cooperative exploration and mechanical refinement.
Live situational work / task-based rounds / CLA games.
Open live training where the exchange can go anywhere.
This turns Hone from a passive tracker into a deliberate practice companion.
Before training, pick one to three focus skills for the day or week.
Use those skills in study, drilling, games, and rolling.
After training, record time, successful executions, footage, hit counts, and insights.
Update the stage, next cue, or next training intention based on evidence.
The app has to be useful enough that pros would use it in their own training. If that is true, their normal training process can become content, and that content can become a recurring income stream.
Hone should make deliberate practice easier for people who already care deeply about improving: choose the skill, log the work, preserve insights, review footage, and track whether the skill is becoming reliable.
For hobbyists, the usage risk is habit inertia: most people are used to showing up, taking class, and doing whatever training gives them that day. Borrowed status helps overcome that inertia because using the same tool as a pro makes deliberate practice feel like part of the pro identity they want to emulate.
The pro uses Hone for an activity they already do: deciding what to work on, training it, logging evidence, and reflecting afterward.
That use becomes easy content: what skill they are honing, how they are practicing it, what changed, and what they learned.
The pro already has an audience. Viewers see Hone in use, want to train more deliberately, and subscribe through the pro's affiliate link.
The pro gets a healthy percentage of monthly revenue from their users. A second level could pay them when athletes they recruit generate subscribers too.
These questions should test the learning model, the logging burden, and whether the app solves real problems for serious practitioners.
These are powerful directions, but each should wait until the core learning loop is working.
Convert voice notes into concise summaries: what worked, what failed, next cue, and next training intention.
A searchable library of constraint games connected to specific skills, positions, and learning goals.
Let coaches or training partners suggest skills, add notes, share games, or review progress.
Give athletes and coaches a way to assign skills, review student training footage, comment on executions, and monetize coaching relationships inside the same skill pipeline.
Potential integrations with resources like BJJ Mental Models DB or OutlierDB if they support the learning loop.