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.
npx skills add shopwareLabs/ai-coding-tools --skill researching-codegit clone --depth 1 https://github.com/shopwareLabs/ai-coding-toolsWrote 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/skills/shopwarelabs/ai-coding-tools/researching-code)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00121 | $0.03460 |
| Opus 5.5 | $0.00048 | $0.01384 |
| Sonnet 5.5 | $0.00024 | $0.00692 |
| Haiku 4.5 | $0.00012 | $0.00346 |
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.
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 — 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:
- Explicit directive from the caller — phrases like "quick check", "surface", "deep dive", "full trace", "just locate X". Use it verbatim.
- 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").
- Default: broad. Surface drops context; deep wastes time.
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.
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.
- 1mo ago First seen · 167 lines · 121 tokens per session scan A 90ef9a782c64
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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