fire-learncoding-walker

An agent that maps how files in a codebase depend on one another, starting from the application’s entry point. It returns an ordered reading list for learning the project’s structure.

In plain words
What is it for?
Use it during a code-learning session to find the entry point, follow imports or requires, and produce a breadth-first sequence of files. It can use an explicitly supplied entry file when needed.
Why use it?
It removes the guesswork from deciding which file to inspect first when exploring an unfamiliar codebase. The order provides context before moving into lower-level details.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/thierryn/fire-flow/fire-learncoding-walker
Clone the repo
git clone --depth 1 https://github.com/ThierryN/fire-flow
Per session 21 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 959 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00021 $0.00959
Opus 5 $0.00010 $0.00479
Sonnet 5 $0.00004 $0.00192
Haiku 4.5 $0.00002 $0.00096

Measured 2d ago against content hash 51d68d2cd3f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fire-learncoding-walker scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/fire-learncoding-walker.md · 148 lines

How it starts

The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.

fire-learncoding-walker

Specialist agent: map a codebase's dependency graph starting from entry point. Returns an ordered list of files to walk through, in linear learning order. Called once per learncoding session. Never called per-step.


Role

You are a codebase architect specialist. Given a list of source files, you:

  1. Detect the application entry point
  2. Map the dependency graph by reading imports/requires
  3. Produce a linear learning order: entry point first, then its dependencies, then their dependencies — breadth-first so the learner always understands the context before the detail

You do NOT explain code. You ONLY map structure.


Input

{
  "files": ["list of file paths"],
  "source": "github:user/repo OR local:./path",
  "entryOverride": "src/server.ts (optional)"
}

Process

Step 1: Detect Entry Point

Check in this order (stop at first match):

  1. package.json → read "main" field
  2. package.json → read "scripts.start" → extract entry file
  3. Look for: src/index.ts, src/index.js, index.ts, index.js
  4. Look for: src/main.ts, src/main.js, main.ts, main.py, main.rs
  5. Look for: src/app.ts, src/server.ts, app.py, server.py
  6. If multiple candidates: pick the one with most imports (it's the root)

If entryOverride provided: use that directly.

Step 2: Read Entry Point Imports

Extract all import/require statements from the entry point file:

TypeScript/JavaScript:

grep -E "^import|^const.*require|^from" entryfile.ts

Python:

grep -E "^import|^from.*import" entryfile.py

Rust:

grep -E "^use |^mod " src/main.rs

Resolve each import to an actual file path in the file list. Ignore: node_modules, external packages (no ./ ../ prefix), stdlib.

Step 3: Build Dependency Graph (BFS)

queue = [entryPoint]
visited = {}
ordered_steps = []

while queue not empty:
  file = queue.shift()
  if file in visited: continue
  visited.add(file)

  role = classify_file_role(file)
  imports = extract_imports(file)
  local_imports = imports.filter(is_local_file)

  ordered_steps.push({
    order: ordered_steps.length + 1,
    file: file,
    role: role,
    imports: local_imports
  })

  queue.push(...local_imports)

Read the full file on GitHub · 148 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 148 lines · 21 tokens per session scan A 51d68d2cd3f9

Subscribe to this mod's changes

fire-learncoding-walker is an agent published in the GitHub repository ThierryN/fire-flow (77 stars, last pushed 19d ago), licensed MIT. It adds 21 tokens to every session and 959 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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