robotaitai/project-bedrock/project-ontology-bootstrap

Claude Code and agent skill by robotaitai/project-bedrock

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Bootstrap an adaptive project knowledge tree. Use when agent-knowledge/Memory/MEMORY.md is missing, broken, or too generic.

Install

npx vskill@latest install robotaitai/project-bedrock/project-ontology-bootstrap

SKILL.md

Project Ontology Bootstrap

Creates a minimal knowledge scaffold, then the agent infers the ontology from the real project. Run this once -- then maintain with the writeback rule.

When to use

  • agent-knowledge/Memory/MEMORY.md does not exist or is empty
  • Memory/MEMORY.md exists but is still generic bootstrap content
  • User says "initialize memory", "bootstrap memory", or "set up project memory"
  • Starting work on an inherited repo with no prior agent memory

Step 0 -- Verify the local pointer

The project entrypoint is ./agent-knowledge.

  • ./agent-knowledge must be a symlink or junction to the real dedicated knowledge folder
  • The external folder is the source of truth
  • Manual and scripted bootstrap still write through ./agent-knowledge as the local handle

If the pointer does not exist yet, create it first with:

bash
scripts/install-project-links.sh --slug <project-slug> --repo /path/to/project

Step 1 -- Run the bootstrap script

bash
scripts/bootstrap-memory-tree.sh /path/to/project

This creates a minimal scaffold:

text
agent-knowledge/
Memory/
MEMORY.md <- root durable memory note
decisions/
decisions.md <- decision log (same-name convention)
Evidence/
raw/
imports/
Sessions/
Outputs/
Dashboards/
Templates/
.obsidian/
STATUS.md

The script detects a profile hint (web-app, robotics, ml-platform, or hybrid) and stores it in STATUS.md. The hint does NOT create branch notes -- that is the agent's job.


Step 2 -- Inspect the repo and infer the ontology

After the scaffold exists, inspect:

  • Manifests and lockfiles
  • Directory structure
  • Docs (README.md, AGENTS.md, CLAUDE.md, docs/)
  • Config files
  • Test directories
  • Workflow files
  • Any evidence already present under Evidence/

From these signals, determine the project's natural decomposition. Use the project's own terminology, not generic template names.


Step 3 -- Create initial branch notes

Create one branch per functional domain in the project. Use the same-name convention:

  • Small topic with no subtopics: Memory/<topic>.md
  • Bigger topic with subtopics: Memory/<topic>/<topic>.md

Each branch note must stay under ~150 lines. If a topic is too big for one note, split it into a folder with subtopic notes.

Do NOT put the whole system description in a single file. Split by functional domain.

Example for a robotics project:

text
Memory/
MEMORY.md
stack.md # build system, deps, hardware platform
perception/
perception.md # vision pipeline overview
object-detection.md # YOLO, TensorRT, tracking
lane-detection.md # lane following
navigation/
navigation.md # path planning, row driving
path-following.md # pure pursuit, drive controller
localization/
localization.md # EKF, IMU, GNSS, LiDAR odometry
cloud-interface.md # STOMP, fleet management
vehicle-interface.md # CAN, LLC, hardware abstraction
safety.md # obstacle detection, fault monitoring
decisions/
decisions.md

Rules:

  • Use the project's own decomposition and terminology, not generic templates
  • Each branch = one focused functional domain. Do NOT use generic names like

"architecture" or "conventions" to lump unrelated subsystems together.

  • Keep each note under ~150 lines. Split if bigger.
  • Link related notes to each other with relative markdown links

(e.g., See [localization](localization/localization.md) for sensor fusion)

  • Do not create empty placeholder branches
  • Each branch entry note should have frontmatter, Purpose, Current State, Recent Changes,

Decisions, and Open Questions sections


Step 4 -- Seed from immediately available facts

For each branch you create, seed it with verified facts:

  • Read manifests, configs, docs, and directory structure
  • Write only confirmed facts to Current State
  • Do not invent facts or speculate
  • If a source is machine-generated, verify against the repo before writing to Memory/

Step 5 -- Update Memory/MEMORY.md

Add links to each branch note in the Branches section:

markdown
## Branches
-[Stack](stack.md) -- Python 3.10, ROS2 Humble, colcon build system
-[Perception](perception/perception.md) -- Camera pipeline, YOLO detection, depth fusion
-[Navigation](navigation/navigation.md) -- Nav2 stack, custom costmap layers

Step 6 -- Trigger history backfill

Bootstrap creates structure only. To fill it with real project history:

bash
scripts/import-agent-history.sh /path/to/project

Then follow the history-backfill skill to distill evidence into memory.


Output checklist

After bootstrap, verify:

  • [ ] agent-knowledge/Memory/MEMORY.md exists and uses YAML frontmatter
  • [ ] agent-knowledge/Memory/decisions/decisions.md exists
  • [ ] Each branch note has a Purpose and at least one entry in Current State
  • [ ] agent-knowledge/Evidence/raw/ and agent-knowledge/Evidence/imports/ exist
  • [ ] agent-knowledge/Sessions/ exists
  • [ ] Memory/MEMORY.md is still short enough to scan quickly
  • [ ] No INDEX.md files were created

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

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