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Init Deep

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code-yeongyu
init-deep

Initializes a hierarchical AGENTS.md knowledge base for a project. Use when a repo needs its structure, commands, and conventions documented for agents.

Overview

Publishercode-yeongyu
Repositoryoh-my-openagent
Skill nameinit-deep
Stars
69.1K
Forks
5.7K
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by code-yeongyu on GitHub. Read the source before you install it.

Installation

Install the Init Deep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/code-yeongyu/oh-my-openagent.git /tmp/oh-my-openagent
mkdir -p .claude/skills
cp -r /tmp/oh-my-openagent/packages/omo-senpi/skills/init-deep .claude/skills/init-deep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Init Deep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Init Deep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Init Deep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/init-deep

Generate hierarchical AGENTS.md files: root + complexity-scored subdirectories, produced by a size-formula-driven dag map-reduce (quick scanners -> unspecified-high writers) so the main session's context stays flat at any repo size.

Usage

/init-deep                      # Update mode: modify existing + create new where warranted
/init-deep --create-new         # Read existing → remove all → regenerate from scratch
/init-deep --max-depth=2        # Limit directory depth (default: 3)

Workflow (High-Level)

  1. Size & route (main session) - ONE eval cell measures the repo and computes the node formula.
  2. Map (dag) - quick scanner nodes extract per-chunk facts into bounded file reports.
  3. Reduce (dag) - unspecified-high writer nodes own disjoint subtrees: score, write AGENTS.md files, emit digests.
  4. Root & verify (dag) - one node writes root AGENTS.md from digests only; one node verifies every file.
  5. Snapshot & mode (main session) - snapshot contract unchanged.

todo init the five phases; start/done each transition in real time.


Phase 1: Size & Route

Measure and compute in ONE eval cell - code, not mental arithmetic:

js
# Measure (tracked files minus vendored/generated: node_modules, .git, dist,
# build, out, vendor, target, coverage, lockfiles, minified and binary files)
S        = total source bytes after exclusions
per_dir  = source bytes per directory            # bin-packing input
depth    = max directory depth                   # respect --max-depth (default 3)
existing = every AGENTS.md / CLAUDE.md path      # read the ROOT one now

# Formula
CHUNK   = 400 * 1024          # ~100k tokens of source; a quick worker's usable window
                              # is ~150k over its fallback chain - leave room for its
                              # prompt and report
N_quick = ceil(S / CHUNK)     # bin-pack WHOLE directories into chunks; a directory
                              # larger than one chunk splits at its children
N_high  = ceil(N_quick / 12)  # one reducer absorbs ~12 reports (~60k tokens) and
                              # still has room to spot-check real code

Route:

  • N_quick < 4 -> inline path below; a dag costs more than it saves.
  • N_quick > task.dag.max_nodes_per_run (default 64) -> raise the knob in omo config, or run one chained dag per top-level directory (multi-run composition, mass-ulw skill).
  • Otherwise -> dag path: emit CHUNKS = [{id, dirs, bytes}], assign each writer a directory SUBTREE (disjoint - no two writers own the same directory), and mkdir -p .omo/init-deep/reports .omo/init-deep/digests.

--create-new: read every existing AGENTS.md FIRST (still-true facts survive as scanner input), then delete all, then regenerate.

Inline path (N_quick < 4)

Small repo - skip the dag. Fire 2-4 parallel explore agents (structure, entry points, conventions, anti-patterns), for example:

task(subagent_type="explore", run_in_background=true, prompt="Project structure: map real layout via ast-grep structural search (sg/ast_grep MCP) + rg --files -> REPORT deviations from standard patterns")

Run the LSP/ast-grep code map yourself (lsp_symbols outlines + workspace inventory, lsp_find_references on top exports, ast-grep import/call shapes; when neither resolves, mark centrality unmeasured). Then score with the matrix below and write every file per the templates yourself. Phase 5 applies unchanged.


Phase 2: Map Wave - quick Scanners (dag)

Build and start the run in one eval JS cell with the dag SDK (OMO_DAG_SDK_ROOT); wave doctrine, the node prompt contract, and the failure playbook come from the mass-ulw skill's references/planning.md. One scanner node per chunk, category: "quick", no load_skills - scanners stay lean and their prompt is a rigid numbered extraction template. Quick workers extract; they never judge and never write AGENTS.md:

TASK: Extract knowledge-base facts for chunk <id> (<dirs>) of <repo-root>.
Steps, in order:
1. Inventory each directory in scope: file count, LOC, languages, entry files.
2. Public exports/symbols other code imports - lsp_symbols and ast-grep
   import/call shapes, never file-name guesses.
3. Conventions that DEVIATE from stack defaults (configs, naming, layout).
4. Anti-patterns: DO NOT / NEVER / ALWAYS / DEPRECATED comments, forbidden patterns.
5. Hotspots: files >500 lines, high-reference symbols, complexity concentrations.
6. Build/test/dev commands touching these dirs.
DELIVERABLE: EXACTLY ONE file `.omo/init-deep/reports/<id>.md`, <=5k tokens, sections
`# CHUNK <id>` / `## INVENTORY` / `## EXPORTS` / `## CONVENTIONS` / `## ANTI-PATTERNS`
/ `## HOTSPOTS` / `## COMMANDS`; an empty section says `none`.
SCOPE: read only <dirs>; write only your report file. If an AGENTS.md exists in scope,
quote its still-true claims into the matching sections.
VERIFY: the report file exists and every section header is present.
STOP WHEN: the report is written and verified.

Phase 3: Reduce Wave - unspecified-high Writers (same dag)

One writer node per subtree, dependsOn its chunks' scanner ids, load_skills: ["init-deep"] - every writer carries this file, so the scoring matrix and templates below ARE its instructions:

TASK: Own subtree <path>: produce its AGENTS.md files for the repo knowledge base.
Steps, in order:
1. Read your chunk reports: .omo/init-deep/reports/<ids>.md. Reports are claims,
   not truth - spot-check real code wherever they conflict or look thin.
2. Score each directory with the init-deep Scoring Matrix; pick locations with the
   Decision Rules (both are in the init-deep skill content loaded with this task).
3. Write each AGENTS.md per the templates and the File Writing Rule. 30-80 lines,
   never repeating parent content.
4. Write .omo/init-deep/digests/<subtree-slug>.md, <=2k tokens: every location
   written (score, one-line role) plus cross-subtree facts the root file must know.
SCOPE: write only inside <path> plus your digest file. Root AGENTS.md is OUT of scope.
VERIFY: every location chosen in step 2 exists on disk within line limits; digest exists.
STOP WHEN: files and digest are written and verified.

Phase 4: Root & Verify (same dag)

  • root-writer - category: "unspecified-high", load_skills: ["init-deep"], dependsOn every writer. Reads ONLY .omo/init-deep/digests/* plus the existing root AGENTS.md; writes the root file per the template below. Never reads chunk reports.
  • verify - category: "quick", dependsOn root-writer. Checks: every digest-declared path exists; root is 50-150 lines; subdirectory files 30-80; no child repeats a parent section block. DELIVERABLE: one PASS / FAIL <path>: <reason> line per file.

The main session reads the verify node's output and nothing else. Each FAIL line -> dag send the owning writer with the named defect (or retry it), then re-run verify. Loop until all PASS. Fixing files yourself by reading reports is a defect - repair flows through the dag.


Scoring & Location (each writer applies this to its subtree; the inline path applies it repo-wide)

Scoring Matrix

FactorWeightHigh ThresholdSource
File count3x>20bash
Subdir count2x>5bash
Code ratio2x>70%bash
Unique patterns1xHas own configexplore
Module boundary2xHas index.ts/init.pybash
Symbol density2x>30 symbolsLSP/sg
Export count2x>10 exportsLSP/sg
Reference centrality3x>20 refsLSP/sg

Decision Rules

ScoreAction
Root (.)ALWAYS create
>15Create AGENTS.md
8-15Create if distinct domain
<8Skip (parent covers)

Output

AGENTS_LOCATIONS = [
  { path: ".", type: "root" },
  { path: "src/hooks", score: 18, reason: "high complexity" },
  { path: "src/api", score: 12, reason: "distinct domain" }
]

Templates & File Writing Rule

Root AGENTS.md (Full Treatment)

markdown
# PROJECT KNOWLEDGE BASE

**Generated:** {TIMESTAMP}
**Commit:** {SHORT_SHA}
**Branch:** {BRANCH}

## OVERVIEW
{1-2 sentences: what + core stack}

## STRUCTURE

{root}/ ├── {dir}/ # {non-obvious purpose only} └── {entry}


## WHERE TO LOOK
| Task | Location | Notes |
|------|----------|-------|

## CODE MAP
{From LSP/ast-grep - skip only if neither exists or project <10 files}

| Symbol | Type | Location | Refs | Role |
|--------|------|----------|------|------|

## CONVENTIONS
{ONLY deviations from standard}

## ANTI-PATTERNS (THIS PROJECT)
{Explicitly forbidden here}

## UNIQUE STYLES
{Project-specific}

## COMMANDS
```bash
{dev/test/build}

NOTES

{Gotchas}


**Quality gates**: 50-150 lines, no generic advice, no obvious info.

### Subdirectory AGENTS.md

30-80 lines max. Sections: OVERVIEW (1 line), STRUCTURE (only if >5 subdirs), WHERE TO LOOK, CONVENTIONS (only if different from parent), ANTI-PATTERNS. NEVER repeat parent content; note why the directory earned its file (score, distinct domain).

---

## Phase 5: Snapshot & Mode

Ask the user: **Local or committed?**

Capture the answer as `USER_MODE_CHOICE`. The explicit answer is authoritative:
- `local` keeps the generated guidance personal to this checkout.
- `committed` reruns the change through the `work-with-pr` skill so the generated guidance lands through a reviewed PR.
- If no explicit answer is available, tracked `AGENTS.md` status is the fallback.

Run these commands after the review is complete:

```bash
# Snapshot — create complete JSON with all fields, milliseconds timestamp
mkdir -p .omo
SHA=$(git rev-parse HEAD)
# Count tracked files (NUL byte counting, chunk-boundary safe)
FILES=$(git ls-files -z | node -e 'let c=0;process.stdin.on("data",d=>{for(let i=0;i<d.length;i++)if(d[i]===0)c++});process.stdin.on("end",()=>process.stdout.write(String(c)))')
LOC=$(git ls-files -z -- '*.ts' '*.tsx' '*.js' '*.jsx' '*.py' '*.go' '*.rs' '*.java' '*.kt' '*.swift' '*.rb' '*.php' '*.c' '*.cpp' '*.cs' '*.scala' '*.lua' '*.ex' '*.exs' '*.zig' '*.dart' | xargs -0 wc -l 2>/dev/null | tail -1 | awk '{print $1}')
NOW=$(node -e 'console.log(Date.now())')
USER_MODE_CHOICE="${USER_MODE_CHOICE:-}" && if [ "$USER_MODE_CHOICE" = "committed" ]; then MODE=committed; elif [ "$USER_MODE_CHOICE" = "local" ]; then MODE=local; elif git ls-files --error-unmatch AGENTS.md >/dev/null 2>&1; then MODE=committed; else MODE=local; fi
cat > .omo/init-deep.json <<EOF
{"commitSha":"$SHA","fileCount":$FILES,"loc":${LOC:-0},"timestamp":$NOW,"mode":"$MODE"}
EOF
# Local mode exclude — managed block (idempotent, never clobbers user lines)
if [ "$MODE" = "local" ]; then
  EXCLUDE=$(git rev-parse --git-path info/exclude)
  mkdir -p "$(dirname "$EXCLUDE")"
  if ! grep -q '# >>> omo-senpi init-deep local (managed)' "$EXCLUDE" 2>/dev/null; then
    cat >> "$EXCLUDE" <<'BLOCK'
# >>> omo-senpi init-deep local (managed)
# Do not edit this block; rerun init-deep or switch modes.
/.omo/init-deep.json
AGENTS.md
# <<< omo-senpi init-deep local (managed)
BLOCK
  fi
fi
# Nested AGENTS.md discovery
find . -name AGENTS.md -not -path '*/node_modules/*' -not -path '*/.git/*' -not -path '*/dist/*' -not -path '*/build/*'

When switching from local mode to committed mode, remove the managed block before rerunning through work-with-pr:

bash
EXCLUDE=$(git rev-parse --git-path info/exclude) && sed -i.bak "/# >>> omo-senpi init-deep local (managed)/,/# <<< omo-senpi init-deep local (managed)/d" "$EXCLUDE" && rm -f "$EXCLUDE.bak"

USER_MODE_CHOICE=committed selects committed mode even when AGENTS.md is untracked. USER_MODE_CHOICE=local selects local mode even when AGENTS.md is tracked. .git/info/exclude cannot hide changes to an already tracked file, so explicit local mode on a tracked AGENTS.md is informational only and the file remains visible to git.


Cleanup

After the snapshot: rm -rf .omo/init-deep - reports and digests are ephemeral scaffolding; .omo/init-deep.json is the only artifact that stays. Record the removal in the final report.


Final Report

=== init-deep Complete ===

Mode: {update | create-new}
Sizing: S={MB} source -> {N_quick} scanners, {N_high} writers ({dag | inline} path)
Cleanup: .omo/init-deep removed

Files:
  [OK] ./AGENTS.md (root, {N} lines)
  [OK] ./src/hooks/AGENTS.md ({N} lines)

Dirs Analyzed: {N}
AGENTS.md Created: {N}
AGENTS.md Updated: {N}

Hierarchy:
  ./AGENTS.md
  └── src/hooks/AGENTS.md

Anti-Patterns

  • Main session reading reports or node outputs: always-reduce is structural - repair via dag send, never by pulling scan data into your own context
  • One node per file or per source: nodes own BATCHES; the formula sets N
  • Free-form scanner prompts: quick workers get numbered extraction steps only
  • Sequential execution: MUST parallel (map wave fans out; inline path runs explore + LSP + ast-grep concurrently)
  • Ignoring existing: ALWAYS read existing first, even with --create-new
  • Over-documenting: Not every dir needs AGENTS.md
  • Redundancy: Child never repeats parent
  • Generic content: Remove anything that applies to ALL projects
  • Verbose style: Telegraphic or die

Frequently asked questions

What does the Init Deep AI skill do?

Initializes a hierarchical AGENTS.md knowledge base for a project. Use when a repo needs its structure, commands, and conventions documented for agents.

Why use Init Deep on TypingMind?

Because you install it once and use it with any model. Init Deep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Init Deep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/code-yeongyu/oh-my-openagent/tree/dev/packages/omo-senpi/skills/init-deep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Init Deep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Init Deep?

As many as you like. As long as a model supports skills, you can use Init Deep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Init Deep AI skill free?

It is published on GitHub by code-yeongyu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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