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 agentmods add skills/daaain/claude-code-log/tool-renderernpx skills add daaain/claude-code-log --skill tool-renderergit clone --depth 1 https://github.com/daaain/claude-code-logWhat 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 | $0.00041 | $0.03271 |
| Opus 5 | $0.00020 | $0.01636 |
| Sonnet 5 | $0.00008 | $0.00654 |
| Haiku 4.5 | $0.00004 | $0.00327 |
Grade A, and why
tool-renderer 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.
How it starts
The opening of the file, as written. The whole thing — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementing a Tool Renderer
This guide walks through adding rendering support for a new Claude Code tool, using WebSearch as an example.
Before You Start
Examine existing test data to understand the tool's actual JSON structure:
# Find test files containing the tool
rg -l "ToolName" test/test_data/
# Look at actual JSONL entries
rg '"name":\s*"ToolName"' test/test_data/ -A 2 -B 2
Key fields to identify:
- Input parameters: What's in
tool_use.input? - toolUseResult structure: What metadata does the structured result contain?
- tool_result.content: What does the raw text output look like?
The toolUseResult field on transcript entries often contains richer structured data than tool_result.content. Always prefer parsing from toolUseResult when available.
Overview
Tool rendering involves several components working together:
- Models (
models.py) - Type definitions for tool inputs and outputs - Factory (
factories/tool_factory.py) - Parsing raw JSON into typed models - HTML Formatters (
html/tool_formatters.py) - HTML rendering functions - Renderers - Integration with HTML and Markdown renderers
Step 1: Define Models
Tool Input Model
Add a Pydantic model for the tool's input parameters in models.py:
class WebSearchInput(BaseModel):
"""Input parameters for the WebSearch tool."""
query: str
Tool Output Model
Add a dataclass for the parsed output. Output models are dataclasses (not Pydantic) since they're created by our parsers, not from JSON:
@dataclass
class WebSearchLink:
"""Single search result link."""
title: str
url: str
@dataclass
class WebSearchOutput:
"""Parsed WebSearch tool output."""
query: str
links: list[WebSearchLink]
preamble: Optional[str] = None # Text before the Links
summary: Optional[str] = None # Markdown analysis after the Links
Note: Some tools have structured output with multiple sections. WebSearch is parsed as preamble/links/summary - text before Links, the Links JSON array, and markdown analysis after. This allows flexible rendering while preserving all content.
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.
- 2d ago First seen · 441 lines · 41 tokens per session scan A 9db1e17182e5
tool-renderer is a skill published in the GitHub repository daaain/claude-code-log (1,201 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 3,271 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…