researching-code

researching-code is a skill for Claude Code from shopwareLabs/ai-coding-tools. It costs 121 tokens per session (3,460 once invoked), scanned A, original, MIT.

A research workflow for understanding an unfamiliar codebase. It uses a code search index to investigate architecture, component usage, data flow, and where features are handled.

In plain words
What is it for?
Use it to trace how a feature works, find code that uses a particular item, map dependencies, or explain a project's architecture.
Why use it?
It gives coding agents a structured way to answer codebase questions instead of relying on guesses or reading files at random.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the chunkhound-integration plugin — 1 skill, 1 agent, 1 hook, 1 MCP server shipped together

Good fit Use it to trace how a feature works, find code that uses a particular item, map dependencies, or explain a project's architecture.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shopwarelabs/ai-coding-tools/researching-code
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.

Any agent
npx skills add shopwareLabs/ai-coding-tools --skill researching-code
Clone the repo
git clone --depth 1 https://github.com/shopwareLabs/ai-coding-tools

Made for: Claude Code.

Or install chunkhound-integration, the plugin that ships this one along with the rest of its 1 skill, 1 agent, 1 hook, 1 MCP server.

Wrote 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.

agentmods badge for researching-code

README.md
[![agentmods](https://agentmods.dev/badge/skills/shopwarelabs/ai-coding-tools/researching-code/github.svg)](https://agentmods.dev/skills/shopwarelabs/ai-coding-tools/researching-code)
Your own site
<a href="https://agentmods.dev/skills/shopwarelabs/ai-coding-tools/researching-code"><img src="https://agentmods.dev/badge/skills/shopwarelabs/ai-coding-tools/researching-code/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.

agentmods 80×15 button for researching-code

Your own site · 80×15
<a href="https://agentmods.dev/skills/shopwarelabs/ai-coding-tools/researching-code"><img src="https://agentmods.dev/badge/skills/shopwarelabs/ai-coding-tools/researching-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,460 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00121 $0.03460
Opus 5.5 $0.00048 $0.01384
Sonnet 5.5 $0.00024 $0.00692
Haiku 4.5 $0.00012 $0.00346

Measured 1mo ago against content hash 90ef9a782c64, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

researching-code 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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/sweep.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/chunkhound-integration/skills/researching-code/SKILL.md · 167 lines

How it starts

The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Researching Code

Execute code research against the ChunkHound index and return synthesized findings. The skill picks the depth, sequences the queries, and returns the result.

Workflow

digraph researching_code {
    "Skill invoked" [shape=doublecircle];
    "Step 1: Detect depth and primitive directives" [shape=box];
    "Depth?" [shape=diamond];
    "Surface plan" [shape=box];
    "Broad plan" [shape=box];
    "Deep plan" [shape=box];
    "Plan uses ChunkHound?" [shape=diamond];
    "Step 2: Pre-flight (daemon_status)" [shape=box];
    "All hard gates pass?" [shape=diamond];
    "STOP — return structured failure" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];
    "Step 3: Execute plan" [shape=box];
    "Step 4: Synthesize findings" [shape=box];
    "Return result" [shape=doublecircle];

    "Skill invoked" -> "Step 1: Detect depth and primitive directives";
    "Step 1: Detect depth and primitive directives" -> "Depth?";
    "Depth?" -> "Surface plan" [label="surface"];
    "Depth?" -> "Broad plan" [label="broad"];
    "Depth?" -> "Deep plan" [label="deep"];
    "Surface plan" -> "Plan uses ChunkHound?";
    "Broad plan" -> "Plan uses ChunkHound?";
    "Deep plan" -> "Plan uses ChunkHound?";
    "Plan uses ChunkHound?" -> "Step 2: Pre-flight (daemon_status)" [label="yes"];
    "Plan uses ChunkHound?" -> "Step 3: Execute plan" [label="no — searches no code"];
    "Step 2: Pre-flight (daemon_status)" -> "All hard gates pass?";
    "All hard gates pass?" -> "STOP — return structured failure" [label="no"];
    "All hard gates pass?" -> "Step 3: Execute plan" [label="yes"];
    "Step 3: Execute plan" -> "Step 4: Synthesize findings";
    "Step 4: Synthesize findings" -> "Return result";
}

Step 1: Detect depth and primitive directives

Pick surface, broad, or deep in this priority:

  1. Explicit directive from the caller — phrases like "quick check", "surface", "deep dive", "full trace", "just locate X". Use it verbatim.
  2. Question shape when no directive — surface for symbol-location questions ("Where is X defined?", "Is X used?", "Show me Y"); broad for subsystem questions ("How does Z work?", "What handles A?"); deep for multi-component traces, impact analyses, and full subsystem audits ("Trace data flow from A through B to C", "Audit all callers", "full impact map for refactoring X").
  3. Default: broad. Surface drops context; deep wastes time.

Read the full file on GitHub · 167 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 1mo ago First seen · 167 lines · 121 tokens per session scan A 90ef9a782c64

Subscribe to this mod's changes

researching-code is a skill published in the GitHub repository shopwareLabs/ai-coding-tools (45 stars, last pushed 4d ago), licensed MIT. It adds 121 tokens to every session and 3,460 once invoked, about $0.0005 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.

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