Borrowing it
Nothing to install: this file belongs to ThinkyMiner/codeTree. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ThinkyMiner/codeTree/main/AGENTS.mdgit clone --depth 1 https://github.com/ThinkyMiner/codeTreeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/thinkyminer/codetree/agents-md)<a href="https://agentmods.dev/instructions/thinkyminer/codetree/agents-md"><img src="https://agentmods.dev/badge/instructions/thinkyminer/codetree/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/thinkyminer/codetree/agents-md"><img src="https://agentmods.dev/badge/instructions/thinkyminer/codetree/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04091 | $0.04091 |
| Opus 5.5 | $0.01636 | $0.01636 |
| Sonnet 5.5 | $0.00818 | $0.00818 |
| Haiku 4.5 | $0.00409 | $0.00409 |
Grade A, and why
codeTree AGENTS.md 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides guidance to Codex (Codex.ai/code) when working with code in this repository.
What This Is
codetree is a Python MCP (Model Context Protocol) server that gives coding agents structured code understanding via tree-sitter. Instead of reading entire files, an agent can ask "what classes are in this file?" or "what does this function call?" and get precise, structured answers.
It exposes 23 tools over MCP:
| Tool | Purpose | Returns |
|---|---|---|
get_file_skeleton(file_path, format?) |
Classes, functions, methods with line numbers + doc comments; format="compact" omits doc lines |
class Foo → line 5, "A calculator.", def bar(x, y) (in Foo) → line 7 |
get_symbol(file_path, symbol_name) |
Full source of a function/class | # path:line\n<source code> |
find_references(symbol_name) |
All usages across the repo | file.py:12, other.py:34 |
get_call_graph(file_path, function_name) |
What it calls + what calls it | → callee, ← file.py:20 |
get_imports(file_path) |
Import/use statements with line numbers | 1: import os, 2: from pathlib import Path |
get_skeletons(file_paths, format?) |
Skeletons for multiple files in one call; format="compact" omits doc lines |
=== calc.py ===\nclass Foo → line 1 |
get_symbols(symbols) |
Full source of multiple symbols | # calc.py:1\nclass Foo: |
get_complexity(file_path, function_name) |
Cyclomatic complexity of a function | Complexity of foo() in calc.py: 5\n if: 2, for: 1 |
find_dead_code(file_path?) |
Symbols defined but never referenced | Dead code in calc.py:\n function unused() → line 15 |
get_blast_radius(file_path, symbol_name) |
Transitive impact analysis | Direct callers:\n main.py: run() → line 4 |
detect_clones(file_path?, min_lines?) |
Duplicate/near-duplicate functions | Clone group 1 (2 functions, 12 lines each): |
search_symbols(query?, type?, parent?, ..., format?) |
Flexible symbol search; format="compact" omits doc lines |
calc.py: class Calculator → line 1 |
find_tests(file_path, symbol_name) |
Find test functions for a symbol | test_calc.py: test_add() → line 3 (name match) |
index_status() |
Graph index freshness and stats | {files: 42, symbols: 315, edges: 580} |
get_repository_map(max_items?) |
Compact repo overview for onboarding | {languages: {py: 20}, hotspots: [...], start_here: [...]} |
resolve_symbol(query, kind?, path_hint?) |
Disambiguate short name into qualified matches | calc.py::Calculator.add → line 11 |
search_graph(query?, kind?, file_pattern?) |
Graph search with degree filters and pagination | {total: 5, results: [...]} |
get_change_impact(symbol_query?, diff_scope?) |
Impact analysis via symbol or git diff | {impact: {CRITICAL: [...], HIGH: [...]}} |
analyze_dataflow(file_path, function_name, mode?, depth?) |
Variable dataflow (mode="flow"), taint analysis ("taint"), or cross-function taint ("cross_taint") |
{variables, sinks} or {paths: [{verdict, risk}]} |
find_hot_paths(top_n?) |
High-complexity × high-call-count optimization targets | file:line — name (complexity=N, callers=M) |
get_dependency_graph(file_path?, format?) |
File-level dependency graph as Mermaid or list | graph LR\n main.py --> calc.py |
git_history(mode?, file_path?, top_n?, since?, min_commits?) |
Git blame (mode="blame"), file churn ("churn"), or change coupling ("coupling") |
Author summary, churn list, or coupled file pairs |
suggest_docs(file_path?, symbol_name?) |
Find undocumented functions with context for doc generation | file:line — name(params), calls: [...] |
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.
- 2mo ago First seen · 209 lines · 4,091 tokens per session scan A 3c7878c9693a
codeTree AGENTS.md is an instructions file published in the GitHub repository ThinkyMiner/codeTree (30 stars, last pushed 4mo ago), licensed MIT. It adds 4,091 tokens to every session, about $0.0164 per session on Opus 5.5. 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.
codeTree AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| codeTree AGENTS.mdThinkyMiner/codeTree | Instructions file | 30 | 4mo ago | MIT | A | 4,091 | Codex, OpenCode |
| plan-forge database.instructions.mdsrnichols/plan-forge | Instructions file | 6 | 3d ago | MIT | A | 1,890 | GitHub Copilot |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 5,516 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,093 | GitHub Copilot |