Agent skill

Agentifind

by AvivK5498 in AvivK5498/beads-web

Set up codebase intelligence for AI agents. An agent skill from AvivK5498/beads-web.

MITAuto-check passedDevOps & Cloud

Install Agentifind

skills CLI
$ npx skills add AvivK5498/beads-web --skill agentifind -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AvivK5498/beads-web agentifind --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AvivK5498/beads-web.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/agentifind .claude/skills/agentifind && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agentifind
GitHub stars
103
Token cost
~3.2k tokens
SKILL.md length
852 words
Files
23
Skills in repo
4
Repo updated
First seen
Licence
MIT

At a glance

Set up codebase intelligence for AI agents. An agent skill from AvivK5498/beads-web.

  • Works in 10 steps: Check for existing guide (Staleness… → Detect repo type and install LSP (if… → Run agentifind sync → …
  • Tasks that involve Infrastructure as code
  • SKILL.md covers Procedure, Output and Notes
  • Runs TypeScript scripts from its folder; calls brew, go and npx

What it does

Agentifind is an agent skill from AvivK5498/beads-web. Set up codebase intelligence for AI agents. Runs the agentifind CLI to extract code structure using LSP (pyright/tsserver) with tree-sitter fallback, then synthesizes a CODEBASE.md navigation guide. Run this skill to get a complete codebase map in .claude/ directory.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 29 other files (for example `.github/workflows/release.yml`, `package-lock.json` and `package.json`).

It sits in DevOps & Cloud, covering Infrastructure as code. It works with Terraform. The repository describes itself as: A visual Kanban UI for Beads CLI — built with Next.js + Rust. Real-time sync, epic tracking, Git Ops and multi-project dashboard. The licence is MIT.

When your agent uses it

  • Tasks that involve Infrastructure as code

Example prompts

  • “/agentifind”

Requirements

  • Node.js

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Check for existing guide (Staleness Detection)
  2. Detect repo type and install LSP (if needed)
  3. Run agentifind sync
  4. Update .gitignore
  5. Read and analyze extracted data
  6. Review analysis gaps
  7. Identify key components
  8. Write CODEBASE.md
  9. Confirm completion
  10. Offer to update agent instructions

What it can do on your machine

Read from SKILL.md and the folder at commit d76ada4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (TypeScript, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • brew
    • go
    • npx
    • git
    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, git and npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agentifind loads about 3.2k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 852 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from AvivK5498/beads-web at commit d76ada4, republished under its MIT licence (© AvivK5498). 852 words, ~3,235 tokens.

Download SKILL.mdSave it as .claude/skills/agentifind/SKILL.md (or your agent's skills folder). This skill also uses 22 other files; get the full folder from GitHub.
name
agentifind
description
Set up codebase intelligence for AI agents. Runs the agentifind CLI to extract code structure using LSP (pyright/tsserver) with tree-sitter fallback, then synthesizes a CODEBASE.md navigation guide. Run this skill to get a complete codebase map in .claude/ directory.

Agentifind: Codebase Intelligence Setup

This skill sets up codebase intelligence by:

  1. Running agentifind CLI to extract code structure
  2. Detecting dynamic patterns that static analysis can't fully trace
  3. Synthesizing a navigation guide with staleness metadata

Procedure

Step 1: Check for existing guide (Staleness Detection)

If .claude/CODEBASE.md already exists, check if it's stale:

  1. Read the metadata header from CODEBASE.md:

    Source-Hash: {sha256 of codebase.json when guide was generated}
    Commit: {git commit when generated}
    Stats: {file count, function count, class count}
  2. Compare against current state:

    • Run sha256sum .claude/codebase.json (or equivalent)
    • Run git rev-parse HEAD
    • Read current stats from codebase.json
  3. If metadata matches: Guide is fresh. Ask user if they want to regenerate anyway.

  4. If metadata differs or missing: Guide is stale. Proceed with regeneration.

  5. If no CODEBASE.md exists: Proceed with generation.

Step 2: Detect repo type and install LSP (if needed)

Check if this is a Terraform/IaC repository:

bash
# Check for .tf files
find . -name "*.tf" -type f | head -1

If Terraform files are found:

Check if terraform-ls is installed. If not, install it for better parsing accuracy:

bash
# Check if terraform-ls exists
which terraform-ls || echo "NOT_INSTALLED"

If NOT_INSTALLED, install terraform-ls:

bash
# macOS (Homebrew)
brew install hashicorp/tap/terraform-ls

# Or via Go (cross-platform)
go install github.com/hashicorp/terraform-ls@latest

Why terraform-ls matters:

  • Proper HCL parsing (not regex)
  • Accurate module resolution
  • Cross-file reference tracking
  • Provider schema awareness

If installation fails, agentifind will fall back to regex parsing (still functional but less accurate).

Step 3: Run agentifind sync

Execute the CLI to extract code structure:

bash
npx agentifind@latest sync

Extraction Method:

  • LSP first (if available): Uses language servers for accurate cross-file resolution
    • Python: pyright-langserver (install: npm i -g pyright)
    • TypeScript: tsserver (bundled with TypeScript)
    • Terraform: terraform-ls (install: brew install hashicorp/tap/terraform-ls)
    • Note: LSP extraction can take 5-15 minutes on large codebases (building reference graph)
  • Regex/Tree-sitter fallback: Fast parsing when LSP unavailable (~30 seconds)

This creates .claude/codebase.json with:

  • Module imports/exports
  • Function and class definitions
  • Call graph relationships (more accurate with LSP)
  • Import dependencies

Options:

  • --skip-validate: Skip linting/type checks (faster)
  • --verbose: Show extraction method and progress
  • --if-stale: Only sync if source files changed
Step 4: Update .gitignore

Add the generated files to .gitignore (if not already present):

# Agentifind generated files
.claude/codebase.json
.claude/CODEBASE.md
.claude/.agentifind-checksum

These files are:

  • Regeneratable from source code
  • Large (codebase.json can be several MB)
  • Machine-specific (paths may differ)
Step 5: Read and analyze extracted data

Read .claude/codebase.json and analyze:

  • stats: File/function/class counts
  • modules: Per-file structure (imports, exports, classes, functions)
  • call_graph: What functions call what
  • import_graph: Module dependencies
  • analysis_gaps: Gaps in call graph (see Step 6)
  • validation: Lint/type issues (if present)
Step 6: Review analysis gaps

The CLI automatically detects gaps in the call graph that may indicate dynamic patterns. Read analysis_gaps from codebase.json:

json
{
  "analysis_gaps": {
    "uncalled_exports": [...],  // Exported functions with no callers
    "unused_imports": [...],    // Imports never referenced
    "orphan_modules": [...]     // Files never imported
  }
}

How to interpret gaps:

Gap TypeWhat It MeansLikely Cause
uncalled_exportsExported function has no detected callersEntry point, CLI command, API handler, test fixture, plugin hook, signal receiver, decorator-invoked
unused_importsImport never referenced in codeSide-effect import, re-export, type-only import, dynamically accessed
orphan_modulesFile never imported by anythingEntry point, script, config file, dynamically loaded plugin

Key insight: If something is exported but never called, or imported but never used, static analysis cannot trace it. These are the areas where the call graph is incomplete.

No manual scanning required - the CLI does this automatically by analyzing the call graph structure.

Show full SKILL.md (365 more words)Show less
Step 7: Identify key components

From the data, determine:

  • Entry points: Files with many importers (check import_graph reverse)
  • Core modules: High export count, central in import graph
  • Utilities: Imported by many, import few themselves
  • Request flow: Trace call_graph from entry to output
Step 8: Write CODEBASE.md

First, check repo_type in codebase.json:

  • If repo_type is "terraform" → Use the Infrastructure Template below
  • If repo_type is missing or other → Use the Application Template below

Application Template (default)

Create .claude/CODEBASE.md with this structure:

markdown
# Codebase Guide

<!-- STALENESS METADATA - DO NOT EDIT -->
<!--
Generated: {ISO 8601 timestamp}
Source-Hash: {sha256 of codebase.json}
Commit: {git commit hash}
Stats: {files} files, {functions} functions, {classes} classes
-->

## ⚠️ Usage Instructions

This guide provides STARTING POINTS, not absolute truth.

**Before acting on any location:**
1. Verify the file exists with a quick Read
2. Confirm the symbol/function is still there
3. If something seems wrong, the guide may be stale - regenerate with `/agentifind`

**This guide CANNOT see:**
- Runtime behavior (dynamic imports, plugins, DI)
- Configuration-driven logic
- Database queries and their relationships
- External API integrations

## Quick Reference

| Component | Location |
|-----------|----------|
| {name} | `{path}` → `{symbol}` |

## Architecture

### Module Dependencies
{Key relationships from import_graph - focus on core modules}

### Data Flow
{Trace from call_graph if clear pattern exists}

## Analysis Gaps (Potential Dynamic Patterns)

{If analysis_gaps has items, list them here grouped by type}

### Uncalled Exports
{List from analysis_gaps.uncalled_exports - these are likely entry points, API handlers, or dynamically invoked}

| Symbol | File | Reason |
|--------|------|--------|
| {name} | `{file}:{line}` | {reason} |

### Orphan Modules
{List from analysis_gaps.orphan_modules - these are likely entry points or dynamically loaded}

| File | Reason |
|------|--------|
| `{file}` | {reason} |

**What this means:**
- Call graph is incomplete for these symbols/files
- They may be invoked via plugins, signals, decorators, CLI, or configuration
- Always trace execution manually when working in these areas
- Don't assume the call graph shows all callers

{If no gaps found, write: "No analysis gaps detected. Call graph appears complete."}

## Conventions
{Infer from naming patterns, file organization, directory structure}

## Impact Map

| If you change... | Also update... |
|------------------|----------------|
| `{high-dependency file}` | {N} dependent files |

## Known Issues
{From validation.linting/formatting/types if present, otherwise omit section}

Infrastructure Template (for Terraform/IaC repos)

When repo_type is "terraform", create .claude/CODEBASE.md with this structure:

markdown
# Infrastructure Guide

<!-- STALENESS METADATA - DO NOT EDIT -->
<!--
Generated: {ISO 8601 timestamp}
Source-Hash: {sha256 of codebase.json}
Commit: {git commit hash}
Stats: {files} files, {resources} resources, {modules} modules
-->

## ⚠️ Usage Instructions

This guide provides STARTING POINTS for infrastructure navigation.

**Before making changes:**
1. Verify the resource/module exists
2. Check the blast radius (what depends on this?)
3. Review variable dependencies
4. Consider state implications

**This guide CANNOT see:**
- Remote state data
- Dynamic values from data sources
- Provider-specific behaviors
- Secrets in tfvars files

## Infrastructure Overview

| Provider | Resources | Modules |
|----------|-----------|---------|
{For each provider in stats.providers, count resources}

## Module Structure

{List from modules array, show source and dependencies}

modules/ ├── {module.name}/ → {module.source} │ └── inputs: {list key variables}


## Resource Inventory

{Group resources by type from resources object}

### {Provider} Resources
| Type | Name | File | Dependencies |
|------|------|------|--------------|
| {type} | {name} | `{file}:{line}` | {dependencies.length} deps |

## Variable Flow

{List from variables array}

| Variable | Type | Used By | Default |
|----------|------|---------|---------|
| {name} | {type} | {used_by.length} resources | {default or "required"} |

## Blast Radius (High Risk)

{List from blast_radius where severity is "high" or "medium"}

⚠️ **Changing these resources affects many dependents:**

| Resource | Affected | Severity |
|----------|----------|----------|
| {target} | {affected_resources.length} resources | {severity} |

**Before modifying high-risk resources:**
- Run `terraform plan` to preview changes
- Consider using `terraform state mv` for refactoring
- Check if changes will force recreation

## Outputs

{List from outputs array}

| Output | Value | Referenced |
|--------|-------|------------|
| {name} | {value} | {references} |

## Dependency Graph

{Describe key relationships from dependency_graph}

Key dependencies:
- `{resource A}` → depends on → `{resource B}`
Step 9: Confirm completion

For application repos, report:

  • Files analyzed (from stats.files)
  • Symbols extracted (from stats.functions + stats.classes)
  • Extraction method used (LSP or tree-sitter)
  • Key entry points identified
  • Analysis gaps detected (count of uncalled_exports, orphan_modules)
  • Any validation issues found
  • Guide staleness metadata recorded

For Terraform/IaC repos, report:

  • Files analyzed (from stats.files)
  • Resources extracted (from stats.resources)
  • Modules detected (from stats.modules)
  • Providers used (from stats.providers)
  • High-risk resources (count from blast_radius with severity "high")
  • Variables defined vs used
  • Guide staleness metadata recorded
Step 10: Offer to update agent instructions

Check if CLAUDE.md or AGENTS.md exists in the project root.

Ask the user:

"Would you like me to add an instruction to your {CLAUDE.md/AGENTS.md} file so the agent automatically uses the CODEBASE.md for navigation?"

If user accepts:

Append this section to the file (or create AGENTS.md if neither exists):

markdown
## Codebase Navigation

Before exploring the codebase, read `.claude/CODEBASE.md` for architecture overview, key files, and conventions. This file is auto-generated by agentifind and provides:
- Quick reference to key components
- Module dependencies and data flow
- Dynamic patterns that static analysis can't trace
- Coding conventions
- Impact map for changes

**Important:** The guide provides starting points. Always verify locations before making changes.

If both CLAUDE.md and AGENTS.md exist, update CLAUDE.md (takes precedence).

If user declines:

Respond with:

"No problem! If you change your mind, add this to your CLAUDE.md or AGENTS.md file:"

markdown
## Codebase Navigation

Before exploring the codebase, read `.claude/CODEBASE.md` for architecture overview, key files, and conventions.

Output

.claude/
├── codebase.json         # Structured extraction (CLI output)
├── CODEBASE.md           # Navigation guide (this skill's output)
└── .agentifind-checksum  # Staleness detection

Notes

  • Ground ALL claims in the extracted data - do not hallucinate relationships
  • Keep the guide concise - focus on navigation over explanation
  • Prioritize "what" and "where" over "why" and "how"
  • If codebase.json already exists and is recent, skip Step 2
  • LSP extraction is slower but more accurate for cross-file references
  • Tree-sitter is faster but uses heuristic-based resolution
  • Always include the staleness metadata header - it enables future freshness checks
  • Always include the analysis gaps section - even if none found, document that the call graph is complete

© AvivK5498, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 22 other files in .claude/skills/agentifind of AvivK5498/beads-web.

  • SKILL.md
  • .github/workflows/release.yml
  • .gitignore
  • LICENSE
  • bin/agentifind
  • images/transparent.png
  • package-lock.json
  • package.json
  • skills
  • src/analyzers/.gitkeep
  • src/analyzers/anomaly-detector.ts
  • src/analyzers/structure.ts
  • src/index.ts
  • src/lsp/client.ts
  • … and 9 more

Open the folder on GitHubat commit d76ada4

Compare with similar skills

Agentifind next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agentifind compared with similar skills
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Terraform Skillantonbabenko/terraform-skill2.4k1 repos~5.1kAutomated safety check: PassApache-2.0
Review Docshashicorp/terraform-provider-aws11k—~1.3kAutomated safety check: PassMPL-2.0
Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit2606 repos~1.1kAutomated safety check: NotesCustom licence
Cloudflarehodgef/apiker1277 repos~2.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Agentifind

What does Agentifind do?

Set up codebase intelligence for AI agents. An agent skill from AvivK5498/beads-web. Agentifind is an agent skill from AvivK5498/beads-web. Set up codebase intelligence for AI agents.

When should I use Agentifind?

Agentifind fits situations like: tasks that involve Infrastructure as code.

How do I install Agentifind in Claude Code?

Run `npx skills add AvivK5498/beads-web --skill agentifind -a claude-code`. Or copy the skill folder (.claude/skills/agentifind in AvivK5498/beads-web) into .claude/skills/agentifind in your project. Claude Code loads it when a task matches its description.

How do I install Agentifind in Codex?

Run `npx skills add AvivK5498/beads-web --skill agentifind -a codex`. Or copy the skill folder (.claude/skills/agentifind in AvivK5498/beads-web) into .agents/skills/agentifind in your project. Codex loads it when a task matches its description.

Can I use Agentifind in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AvivK5498/beads-web --skill agentifind -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agentifind, .gemini/skills/agentifind, .github/skills/agentifind and .opencode/skills/agentifind in your project.

What does Agentifind need to run?

Going by SKILL.md and its folder, Agentifind needs TypeScript for the scripts in its folder and the command-line tools its instructions call (brew, go, npx, git and npm). Our summary lists: Node.js.

Does Agentifind access the network?

SKILL.md contains no URLs. Its commands use npx, git and npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Agentifind safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agentifind use?

Agentifind is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agentifind use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agentifind?

Skills that share tags, products or a category with Agentifind: Terraform and OpenTofu Guide (agentscope-ai/QwenPaw, 36k stars), Terraform Skill (antonbabenko/terraform-skill, 2.4k stars), Review Docs (hashicorp/terraform-provider-aws, 11k stars) and Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agentifind?

AvivK5498 (a GitHub user) maintains it in AvivK5498/beads-web, which has 103 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on May 19, 2026.

Source: AvivK5498/beads-web on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.