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Bear Hunter System

7 min read

Part of Deep Processing

Bear Hunter System

Important study material needs to become a structure that can later be retrieved, explained, applied, and adapted. Use Bear Hunter System as the default encoding workflow for that job.

The practical version:

  1. Prestudy: create the rough frame before the main learning event.
  2. Aim: create the right questions before learning.
  3. Shoot: use sources to answer those questions and build the map.
  4. Skin: clean the map into a final structure you can retrieve from memory.

BHS replaces passive reading, linear notes, and “I’ll organize it later.” The organization happens while learning.

How To Use It

Before The Learning Event

Before running BHS on any material, retrieve what you already know. Spend a few minutes writing down what you already think is true about the topic — no sources, no notes. This activates existing schema so BHS builds on it rather than starting from zero. Skipping this step and going straight to technique is a common failure mode: the learner applies encoding structure to material they already partially understand, which inflates apparent effort and misses the opportunity to identify real gaps before the source even opens.

Do Prestudy and Aim as early as possible.

If you have a lecture, class, chapter, paper, or video coming up, do a short Aim pass before it. Even 5-10 minutes is useful if that is all you have.

Minimum Aim pass:

  • skim headings, objectives, summaries, diagrams, or keyword lists;
  • list the main concepts;
  • ask “Why is this important?”;
  • ask “How is this related to the other concepts?”;
  • draft 3-7 rough chunks or question clusters.

If you have more time, do a deeper Aim and start a light Shoot before the main learning event.

During The Learning Event

Use Shoot.

Do not copy the lecture or text in order. Instead:

  • answer your Aim questions;
  • add useful details under the chunk they support;
  • change the chunk structure when the old one no longer fits;
  • draw relationships as they become meaningful;
  • mark unresolved questions instead of pretending you understand everything.

Your notes should look like a working map, not a transcript.

After The Learning Event

Use Skin.

This is where the rough working map becomes a useful study artifact.

Skin checklist:

  • remove clutter;
  • rename vague chunks;
  • merge single-node chains;
  • split overloaded chunks;
  • make the main flow obvious;
  • keep only important arrows;
  • convert the map into retrieval prompts.

Do this the same day if possible. If the week is busy, schedule one weekly Skin pass for the whole week’s material.

Default Weekly Implementation

Use this as the user’s practical baseline:

  1. End of previous week: Aim one week’s worth of upcoming material.
  2. Before each class or source session: quick Shoot for the highest-level concepts if time allows.
  3. During class/source session: continue Shoot while learning.
  4. End of each day or week: Skin the map.
  5. After Skin: turn the map into SIR prompts.

This is the standard version. It is the best default while the skill is still becoming automatic.

Accelerated Implementation

Use this when the standard version feels stable.

Weekend:

  • Aim an entire topic.
  • Shoot roughly 60-70% of the topic using self-study material.
  • Build the backbone before classes reach it.

During the week:

  • finish the remaining Shoot before, during, and after learning events;
  • attend lectures/classes as a recheck, gap-finder, or retrieval pass;
  • Skin the whole topic at the end of the week.

This creates independence from curriculum pacing. The class stops being the first exposure and becomes a verification layer.

Minimum Viable BHS

Use this when time is tight.

  1. Write the 5-10 most important concepts.
  2. Ask “Why is each one important?”
  3. Group them into 3-5 chunks.
  4. Add only the relationships needed to explain the topic.
  5. After learning, clean one thing: overloaded chunks, missing links, or weak labels.

This is not ideal, but it preserves the core of BHS: importance, relationships, chunks, and refinement.

What The Artifact Should Look Like

A good BHS artifact should have:

  • a visible backbone;
  • a few major chunks;
  • sub-chunks where detail starts to overload;
  • relationships that explain why chunks connect;
  • minimal isolated facts;
  • few decorative arrows;
  • enough clarity that you can reconstruct it closed book.

If the map is beautiful but hard to retrieve, it is not done.

Practical Quality Checks

Ask these after each pass:

  • Can I explain the topic from the map without reading the source?
  • Do the chunks reflect importance, or did I copy the source order?
  • Are there more than four branches coming from one node?
  • Are there single-node chains that could be merged?
  • Are arrows showing important relationships or just visual noise?
  • Can Aim questions become retrieval prompts?
  • Did Shoot answer the important questions or turn into copying?
  • Did Skin make the topic simpler?

Common Failure Modes

ProblemWhat It Looks LikeFix
Technique-firstBHS starts before the learner has retrieved what they already know.Do a retrieval pass — write what you already think is true — before opening any source.
Passive AimQuestions are mostly “what is…”Rewrite as why/how/relationship questions.
Copying during ShootNotes follow source order exactly.Return to Aim questions and map answers under chunks.
SpiderwebbingToo many chaotic arrows.Re-chunk concepts that share important relationships.
Overloaded chunkOne node has 5+ branches.Split into sub-chunks.
Single-node chainA node only connects one thing to one thing.Merge, rename, or move it.
Pretty mapLooks clean but cannot be reconstructed.Brain dump it closed book and repair gaps.
Skipped SkinRough notes never become a study artifact.Schedule a short Skin pass before retrieval.

BHS And Retrieval

BHS is encoding. SIR tests whether the encoding works.

Handoff:

  • Aim questions become recall prompts.
  • Shoot map becomes the answer key.
  • Skin map becomes the brain-dump target.
  • Retrieval failures tell you whether to re-Aim, re-Shoot, or re-Skin.

If retrieval fails because details are missing, add detail. If retrieval fails because the structure collapses, repair the map.

User Perspective

The user should treat BHS as the core study engine:

  • Aim protects curiosity and direction.
  • Shoot protects active processing.
  • Skin protects consolidation.
  • SIR protects long-term access.

The goal is to make knowledge easier to reconstruct, use, and improve.

LLM Use

LLMs can help, but should not replace the user’s processing.

Good LLM use:

  • challenge the chunk structure;
  • generate alternate Aim questions after the user tries first;
  • create retrieval prompts from the finished map;
  • ask for missing relationships;
  • produce examples or counterexamples.

Bad LLM use:

  • asking the model to chunk the source before the user has tried;
  • accepting the model’s structure without evaluating it;
  • using the model to avoid the hard thinking.

How It Should Feel

BHS should feel like the topic is becoming more organized while you learn it. The main signal is that the map is making your thinking more structured.

Good signs:

  • Aim creates useful curiosity before the source gives answers;
  • Shoot feels like answering, sorting, and revising rather than copying;
  • Skin makes the map simpler and easier to reconstruct;
  • relationships feel earned, not decorative;
  • and retrieval from the final structure feels possible.

Warning sign: BHS has become surface activity when the artifact is clean but you cannot explain why the chunks and arrows exist.

Open Questions

  • What is the user’s default BHS artifact: paper, Obsidian canvas, Excalidraw, markdown outline, or mixed?
  • Which current topic should become the first worked BHS example?
  • What should the user’s minimum viable BHS template look like?

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