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/ai-analyst-lab/ai-analyst-plugin/tracenpx skills add ai-analyst-lab/ai-analyst-plugin --skill tracegit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/trace)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/trace"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/trace.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00069 | $0.00607 |
| Opus 5 | $0.00034 | $0.00303 |
| Sonnet 5 | $0.00014 | $0.00121 |
| Haiku 4.5 | $0.00007 | $0.00061 |
Grade A, and why
trace 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 4d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/trace: tie every number to its source
Purpose
Answer "where did that number come from?" with evidence, not memory. Every number in a brief or readout gets linked to the exact computation and source data that produced it.
When to Use
- The user asks where a number came from, or to show the work.
- Before any deliverable leaves the session (analyst-core rule: trace numbers to source).
- Reviewing an analysis produced earlier in the session or found in
.knowledge/analyses/.
Instructions
-
Collect the findings. List every specific number in the deliverable being traced: headline figures, table cells that carry the argument, chart values called out in text.
-
Collect the computations. Gather the queries and code run this session (re-read your own steps; if the analysis logged queries to
.knowledge/query-log.md, read that too). -
Build the trace table and include it in the output (or save as
trace.mdnext to the deliverable if the user wants a file):# Number Where it appears Produced by Source data Confidence 1 $1.2M brief, headline SUM(amount) over Q2 orders query orders.csv cited Confidence labels:
- cited: the computation for this number was run this session and is shown.
- value-match: a run computation produced this value, but the deliverable did not name it.
- inferred: no run computation produced it; state where it came from (an input doc, an assumption) or mark it unverified.
-
Surface the gaps loudly. Unverified numbers and orphan queries (run but unused) are the most important rows. Never hide them. If a headline number is inferred or unverified, say so at the top of the trace, not in a footnote.
-
Offer the fix. For any unverified number, offer to recompute it live from the source data so it can be promoted to cited.
Notes
- This is a reading-and-reporting skill: no scripts, no extra infrastructure. The evidence is the session's own work plus the files in the working folder.
- Pairs with the reconcile check (parts sum to totals) and the reliability skill (run it again).
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.
- 4d ago First seen · 53 lines · 69 tokens per session scan A 0c49824d1ccb
trace is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 8d ago), licensed MIT. It adds 69 tokens to every session and 607 once invoked, about $0.0003 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.
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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
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