funcfinder AGENTS.md

A set of instructions for using funcfinder, a command-line toolkit that maps functions, types, dependencies, call relationships, usage hotspots, and code complexity. It includes different investigation steps for small and large codebases.

In plain words
What is it for?
It helps locate functions, inspect their implementations, find imports, trace callers and callees, identify frequently used code, and find complex areas.
Why use it?
It helps an agent understand an unfamiliar codebase without reading every file in full. The resulting maps and targeted source extraction reduce the amount of code that must be inspected.

Instructions file for CodexOpenCode

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 instructions/ruslano69/funcfinder/agents-md
Clone the repo
git clone --depth 1 https://github.com/ruslano69/funcfinder

Made for: Codex, OpenCode.

Per session 3,493 This file is loaded in full into every session.
When invoked 3,493 The same file — it is already loaded in full.
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 $0.03493 $0.03493
Opus 5 $0.01747 $0.01747
Sonnet 5 $0.00699 $0.00699
Haiku 4.5 $0.00349 $0.00349

Measured 2d ago against content hash 050b85949369, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

funcfinder 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 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.md · 420 lines

How it starts

The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.

funcfinder for AI Agents

Quick Reference: Map codebases, extract functions, trace calls, save 99% tokens.


Build First!

./build.sh   # Required before first use (~5 sec)

Builds 5 binaries: funcfinder, stat, deps, complexity, callgraph.


The Toolkit at a Glance

Tool Purpose Input
funcfinder Map functions & types, extract bodies file or dir
deps Import dependencies + shard graph dir
callgraph Who calls whom file or dir
stat Call frequency & hotspots file
complexity Cognitive complexity per function file

Investigation Workflow

Small project (< 50 files)

# 1. Orient — full map in one shot (~30ms)
funcfinder --dir . --all --json > map.json

# 2. Find the target
grep -i "auth" map.json
# → auth/handler.go:42: AuthenticateUser

# 3. Extract the body
funcfinder --inp auth/handler.go --source go --func AuthenticateUser --extract

# 4. Trace who it calls
callgraph --inp auth/handler.go -l go --func AuthenticateUser

# 5. Trace who calls it (impact)
callgraph --dir . -l go --reverse --func AuthenticateUser

Large project (50+ files)

# 1. Split into shards (one-time, ~100ms)
funcfinder --dir . --all --json --split

# 2. Read manifest — 2KB overview of entire codebase
cat .codemap/manifest.json
# → see shards, function counts, depends_on links

# 3. Load the relevant shard
cat .codemap/internal_auth.json

# 4. Extract the function
funcfinder --inp internal/auth.go --source go --func Authenticate --extract

# 5. Check call graph for impact
callgraph --dir . -l go --reverse --func Authenticate

Incremental update (repeat sessions)

funcfinder --dir . --all --json --split --inc
# INFO: Incremental: 1 shards changed, 32 unchanged

Full architecture index (once per project)

# Build shard map
funcfinder --dir . --all --json --split --no-gitignore

# Add inter-shard dependency graph to manifest
deps . -l go --shards --no-gitignore --update-manifest .codemap/manifest.json

# manifest.json now contains:
# {"path": "cmd_funcfinder.json", "depends_on": ["internal.json"], ...}

Read the full file on GitHub · 420 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 · 420 lines · 3,493 tokens per session scan A 050b85949369

Subscribe to this mod's changes

funcfinder AGENTS.md is an instructions file published in the GitHub repository ruslano69/funcfinder (2 stars, last pushed 20d ago), licensed MIT. It adds 3,493 tokens to every session, about $0.0175 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 2026-08-31.

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