claude-code-log is a Python command-line tool that turns Claude Code transcript files into readable HTML or Markdown logs. It helps people browse individual sessions or complete project archives, with options for interactive browsing and live updates. The catalogue add-ons extend its use as an agent-related workflow.
Borrowing it
Nothing to install: this file belongs to daaain/claude-code-log. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/daaain/claude-code-log/main/.claude/skills/tool-renderer/SKILL.mdgit clone --depth 1 https://github.com/daaain/claude-code-logWrote 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/daaain/claude-code-log/tool-renderer)<a href="https://agentmods.dev/skills/daaain/claude-code-log/tool-renderer"><img src="https://agentmods.dev/badge/skills/daaain/claude-code-log/tool-renderer/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/daaain/claude-code-log/tool-renderer"><img src="https://agentmods.dev/badge/skills/daaain/claude-code-log/tool-renderer.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 | $0.00041 | $0.03282 |
| Opus 5.5 | $0.00016 | $0.01313 |
| Sonnet 5.5 | $0.00008 | $0.00656 |
| Haiku 4.5 | $0.00004 | $0.00328 |
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 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 — 446 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.
- 1mo ago Changed · +5 lines fdd883303bd7
- 2mo 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 (1,255 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 3,282 once invoked, about $0.0002 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.
tool-renderer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| tool-rendererdaaain/claude-code-log | Skill | 1.3k | yesterday | MIT | A | 41 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |