Sync Upstream
nyaruka/phonenumbers
Sync this Go port with a new upstream google/libphonenumber release — regenerate the embedded metadata and reconcile the ported Java logic.
Explain and work with the normalized coding-trace JSONL row format produced by extractclauderounds.py, extractcodexrounds.py, and collectllmtraces.py.
Install Coding Trace Normalize
$ npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install uw-syfi/TraceLab coding-trace-normalize --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/uw-syfi/TraceLab.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/coding-trace-normalize .claude/skills/coding-trace-normalize && rm -rf skills-srcUse ~/.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/
Install the "coding-trace-normalize" agent skill from https://github.com/uw-syfi/TraceLab/tree/main/skills/coding-trace-normalize into .claude/skills/coding-trace-normalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "coding-trace-normalize", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/uw-syfi/TraceLab/tree/main/skills/coding-trace-normalizeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install uw-syfi/TraceLab coding-trace-normalize --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/uw-syfi/TraceLab.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/coding-trace-normalize .agents/skills/coding-trace-normalize && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "coding-trace-normalize" agent skill from https://github.com/uw-syfi/TraceLab/tree/main/skills/coding-trace-normalize into .agents/skills/coding-trace-normalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "coding-trace-normalize", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install uw-syfi/TraceLab coding-trace-normalize --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/uw-syfi/TraceLab.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/coding-trace-normalize .cursor/skills/coding-trace-normalize && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "coding-trace-normalize" agent skill from https://github.com/uw-syfi/TraceLab/tree/main/skills/coding-trace-normalize into .cursor/skills/coding-trace-normalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "coding-trace-normalize", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/uw-syfi/TraceLab.git --path skills/coding-trace-normalize--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install uw-syfi/TraceLab coding-trace-normalize --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/uw-syfi/TraceLab.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/coding-trace-normalize .gemini/skills/coding-trace-normalize && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "coding-trace-normalize" agent skill from https://github.com/uw-syfi/TraceLab/tree/main/skills/coding-trace-normalize into .gemini/skills/coding-trace-normalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "coding-trace-normalize", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install uw-syfi/TraceLab coding-trace-normalizeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/uw-syfi/TraceLab.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/coding-trace-normalize .github/skills/coding-trace-normalize && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "coding-trace-normalize" agent skill from https://github.com/uw-syfi/TraceLab/tree/main/skills/coding-trace-normalize into .github/skills/coding-trace-normalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "coding-trace-normalize", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install uw-syfi/TraceLab coding-trace-normalize --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/uw-syfi/TraceLab.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/coding-trace-normalize .opencode/skills/coding-trace-normalize && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "coding-trace-normalize" agent skill from https://github.com/uw-syfi/TraceLab/tree/main/skills/coding-trace-normalize into .opencode/skills/coding-trace-normalize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "coding-trace-normalize", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
coding-trace-normalize- GitHub stars
- 138
- Token cost
- ~1.6k tokens
- SKILL.md length
- 649 words
- Files
- 2
- Skills in repo
- 7
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Explain and work with the normalized coding-trace JSONL row format produced by extractclauderounds.py, extractcodexrounds.py, and collectllmtraces.py.
- Works in 4 steps: Work from the coding-trace repo root,… → Read README.md section "Normalized Rows"… → Read example_sessions/derived/README.md… → …
- Interpreting normalized rows
- SKILL.md covers Overview, First Steps, Row Identity and Token Semantics, plus 3 more sections
- Calls uv
What it does
Coding Trace Normalize is an agent skill from uw-syfi/TraceLab. Explain and work with the normalized coding-trace JSONL row format produced by extractclauderounds.py, extractcodexrounds.py, and collectllmtraces.py. Use when interpreting normalized rows, provider differences, token semantics, Claude cache creation/read/uncached fields, prefix versus append accounting, input-event summaries, timingevents, tools metadata, tracekey identity, or converting raw Claude/Codex concepts into the common analysis schema.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).
It sits in Business, Finance & HR, covering Accounting and bookkeeping. The repository describes itself as: An open toolkit and public dataset hub for collecting, sanitizing, analyzing, and visualizing coding agent traces. The licence is Apache-2.0.
When your agent uses it
- Interpreting normalized rows
- Provider differences
- Token semantics
- Claude cache creation/read/uncached fields
Example prompts
- “/coding-trace-normalize”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Work from the coding-trace repo root, identified by pyproject.toml, README.md, and scripts/collect_llm_traces.py.
- Read README.md section "Normalized Rows" for the public contract.
- Read example_sessions/derived/README.md and inspect example_sessions/derived/round_trace.expanded.json for a small worked example.
- If a field is ambiguous, inspect the extractor that writes it: scripts/extract_claude_rounds.py, scripts/extract_codex_rounds.py, or…
What it can do on your machine
Read from SKILL.md and the folder at commit 11b8b14. 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
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, 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
Coding Trace Normalize loads about 1.6k tokens when it runs. Until then it costs about 120 tokens; SKILL.md has 649 words of instructions outside code blocks.
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 uw-syfi/TraceLab at commit 11b8b14, republished under its Apache-2.0 licence (© uw-syfi). 649 words, ~1,623 tokens.
Download SKILL.mdSave it as
.claude/skills/coding-trace-normalize/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- coding-trace-normalize
- description
- Explain and work with the normalized coding-trace JSONL row format produced by extract_claude_rounds.py, extract_codex_rounds.py, and collect_llm_traces.py. Use when interpreting normalized rows, provider differences, token semantics, Claude cache creation/read/uncached fields, prefix versus append accounting, input-event summaries, timing_events, tools metadata, trace_key identity, or converting raw Claude/Codex concepts into the common analysis schema.
Coding Trace Normalize
Overview
Use this skill to interpret the common JSONL format produced by this repo. Each normalized row represents one LLM invocation, even though the raw Claude and Codex logs use different event schemas.
First Steps
- Work from the coding-trace repo root, identified by
pyproject.toml,README.md, andscripts/collect_llm_traces.py. - Read
README.mdsection "Normalized Rows" for the public contract. - Read
example_sessions/derived/README.mdand inspectexample_sessions/derived/round_trace.expanded.jsonfor a small worked example. - If a field is ambiguous, inspect the extractor that writes it:
scripts/extract_claude_rounds.py,scripts/extract_codex_rounds.py, orscripts/collect_llm_traces.py.
Row Identity
Common top-level fields:
provider:claudeorcodex.session_id: provider session identifier.round_index: zero-based order within the extracted session when available.round_id: provider-specific round identifier.trace_key: stable dedupe key, built as{provider}:{session_id}:{round_id}. Somesession_idvalues already include a provider prefix, so keys such asclaude:claude:...:msg_...are expected.model: model reported by the provider trace.project,store,user,home,turn_id,cwd, orsession_file: private extracted traces may include local context fields. Sanitized traces pseudonymizeproject,user, and ids, and remove local path/source fields.current_input_event_count,current_user_message_count,current_tool_result_count,current_user_message_chars,current_tool_result_chars,current_input_chars, andfirst_input_event_type: summaries derived from the row's input-sidetiming_events.
Token Semantics
Normalized input fields follow this invariant:
input_tokens_total = prefix_tokens + newly_append_tokensProvider mappings:
- Claude
prefix_tokenscomes fromcache_read_input_tokens. - Claude
newly_append_tokenscomes frominput_tokens + cache_creation_input_tokens. - Claude
input_tokens_totaliscache_read_input_tokens + cache_creation_input_tokens + input_tokens. - Claude rows also keep the provider-specific split as
claude_uncached_input_tokens,claude_cache_creation_input_tokens, andclaude_cache_read_input_tokens. - Claude
reasoning_output_tokensis normallynullbecause Claude examples do not expose a separate reasoning-token field in this schema. - Codex
prefix_tokenscomes fromcached_input_tokens. - Codex
newly_append_tokensisinput_tokens - cached_input_tokens, clamped at zero by the extractor. - Codex
input_tokens_totalcomes frominput_tokens. - Codex rows set the Claude-specific cache fields to
null. - Codex
reasoning_output_tokensis a subset ofoutput_tokens, not extra output.
Interpretation guidance:
prefix_tokensapproximates prompt-cache hit tokens.newly_append_tokensapproximates prompt-side work not served from prompt cache.- These are provider prompt-cache semantics, not a direct dump of local serving-engine KV-cache objects.
output_tokensis inclusive generated output for the round. Do not addreasoning_output_tokensto it.
Show full SKILL.md (322 more words)Show less
Timing Events
timing_events[] preserves the ordered trace-observed timeline for the normalized round. Common event types:
user_message: visible user input that triggered or preceded a model round.tool_result: tool output returned to the model before a later model round.reasoning: existence of a reasoning or thinking output item, usually without readable private content.text: visible assistant text.tool_call: model-emitted tool request.usage_report: Codex token accounting event.
For an "input ready to next tool input" latency proxy, use the latest user_message or tool_result before the first following tool_call, then subtract timestamps. For reasoning timing, the summarizer distinguishes latest input to reasoning marker/end from the exact-reasoning TPOT subset. TPOT and TTFT residual estimates require exact numeric reasoning_output_tokens; otherwise they should remain null.
The current-input summary fields are derived only from user_message and tool_result timing events in the row. They are convenience counters for analysis, not replacements for timing_events[] when exact ordering matters.
Tool Metadata
Each row can contain tools[], with one object per model-emitted tool call:
tool_index: order within the row.tool_name: provider/tool runner name such asBash,exec_command,Read, orapply_patch.tool_call_id: call id, paired with timing events when available.emitted_at: timestamp when the model emitted the tool call.result_at: timestamp when a tool result was observed, ornull.input: raw tool input in private extracted traces. Sanitized traces remove it.input_chars: serialized input size retained even wheninputis removed.result_chars: result size; full outputs are not kept in normalized rows.tool_wall_latency_ms: trace-observedresult_at - emitted_at.tool_internal_latency_ms: tool/runner-reported duration when available.is_error: error status if inferable.
Analysis experiments use tool_internal_latency_ms when present, then fall back to tool_wall_latency_ms. The multi-round CSV exporter uses tool_wall_latency_ms by default unless --tool-latency-source internal is passed.
Quick Inspection
Inspect a small public normalized example:
sed -n '1,120p' example_sessions/derived/round_trace.expanded.jsonSummarize a normalized file:
uv run python artifacts/trace_facts/overview_summary/analyze.py \
--db trace/syfi_coding_trace.duckdb --jsonWhen answering field questions, cite the relevant extractor or docs and avoid exposing private tools[].input or local context from unsanitized rows unless the user explicitly asks to inspect their own private data.
© uw-syfi, Apache-2.0. 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 1 other file in skills/coding-trace-normalize of uw-syfi/TraceLab.
- SKILL.md
- agents/openai.yaml
Open the folder on GitHubat commit 11b8b14
Compare with similar skills
Coding Trace Normalize 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Coding Trace Normalize this skilluw-syfi/TraceLab | 138 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Sync Upstreamnyaruka/phonenumbers | 1.6k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Longbridge Value Investinghelsome/folio | 269 | 2 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Radiology Tablehuang-sir1/radiology-skills | 1.9k | — | ~1.3k | Automated safety check: Pass | Custom licence | |
| Odoo Agency Fleet Reviewerpipe-org/mcp-odoo | 420 | — | ~699 | Automated safety check: Pass | MIT | |
| Beancount Closebex-co/beancount-io | 295 | — | ~1.4k | Automated safety check: Pass | MIT |
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Categories
Questions about Coding Trace Normalize
What does Coding Trace Normalize do?
Explain and work with the normalized coding-trace JSONL row format produced by extractclauderounds.py, extractcodexrounds.py, and collectllmtraces.py. Coding Trace Normalize is an agent skill from uw-syfi/TraceLab.py.
When should I use Coding Trace Normalize?
Coding Trace Normalize fits situations like: interpreting normalized rows; provider differences; token semantics; Claude cache creation/read/uncached fields.
How do I install Coding Trace Normalize in Claude Code?
Run `npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a claude-code`. Or copy the skill folder (skills/coding-trace-normalize in uw-syfi/TraceLab) into .claude/skills/coding-trace-normalize in your project. Claude Code loads it when a task matches its description.
How do I install Coding Trace Normalize in Codex?
Run `npx skills add uw-syfi/TraceLab --skill coding-trace-normalize -a codex`. Or copy the skill folder (skills/coding-trace-normalize in uw-syfi/TraceLab) into .agents/skills/coding-trace-normalize in your project. Codex loads it when a task matches its description.
Can I use Coding Trace Normalize 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 uw-syfi/TraceLab --skill coding-trace-normalize -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/coding-trace-normalize, .gemini/skills/coding-trace-normalize, .github/skills/coding-trace-normalize and .opencode/skills/coding-trace-normalize in your project.
What does Coding Trace Normalize need to run?
Going by SKILL.md and its folder, Coding Trace Normalize needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Coding Trace Normalize access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Coding Trace Normalize 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 Coding Trace Normalize use?
Coding Trace Normalize is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Coding Trace Normalize use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Coding Trace Normalize?
Skills that share tags, products or a category with Coding Trace Normalize: Sync Upstream (nyaruka/phonenumbers, 1.6k stars), Longbridge Value Investing (helsome/folio, 269 stars), Radiology Table (huang-sir1/radiology-skills, 1.9k stars) and Odoo Agency Fleet Review (erpipe-org/mcp-odoo, 420 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Coding Trace Normalize?
uw-syfi (a GitHub organization) maintains it in uw-syfi/TraceLab, which has 138 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on August 22, 2026.
Source: uw-syfi/TraceLab on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.