Skill Compose is an open-source platform for building and running AI agents that use modular skills. It is intended for creating skill-powered agents without workflow graphs or a command-line interface, and the catalogue skills are examples of those agent capabilities.
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 dp-archive/archive --skill trace-qagit clone --depth 1 https://github.com/dp-archive/archiveWrote 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/dp-archive/archive/trace-qa)<a href="https://agentmods.dev/skills/dp-archive/archive/trace-qa"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/trace-qa/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/dp-archive/archive/trace-qa"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/trace-qa.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00072 | $0.00803 |
| Opus 5.5 | $0.00029 | $0.00321 |
| Sonnet 5.5 | $0.00014 | $0.00161 |
| Haiku 4.5 | $0.00007 | $0.00080 |
Grade A, and why
trace-qa 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trace QA
Analyze agent execution traces to answer questions about what happened, why it failed, how efficient it was, or any other aspect of the run.
Workflow
Always start with overview to understand the trace before diving into details.
1. Get the overview first
python scripts/fetch_trace.py <trace_id> overview
This returns metadata (status, duration, tokens, model) and summaries (request, answer preview, tool usage counts). Use this to orient yourself before going deeper.
2. Explore steps or LLM calls as needed
Depending on the user's question, drill into the relevant data:
| User wants to know... | Command |
|---|---|
| What tools were called and in what order | steps [start] [count] |
| Full input/output of a specific tool call | step <N> |
| How many LLM calls and their token costs | llm-calls [start] [count] |
| What messages were sent to Claude in a specific turn | llm-call <N> |
| Just the final result | answer |
3. Handle long content with segmented reads
When content is large, the script automatically segments output to ~4000 characters.
If you see a [CONTINUED: ...] message at the end of output, call the command shown
in that message to read the next segment. Repeat until all content is read.
Example sequence:
python scripts/fetch_trace.py <id> step 5
# Output ends with: [CONTINUED: use 'step 5 --offset 4000' for next segment]
python scripts/fetch_trace.py <id> step 5 --offset 4000
# Output ends with: [CONTINUED: use 'step 5 --offset 8000' for next segment]
python scripts/fetch_trace.py <id> step 5 --offset 8000
# Full content now read
Command Reference
| Mode | Syntax | Description |
|---|---|---|
overview |
fetch_trace.py <id> overview |
Metadata + summary stats |
steps |
fetch_trace.py <id> steps [start] [count] |
Paginated step list (default: 30/page) |
step |
fetch_trace.py <id> step <N> [--offset <chars>] |
Single step full content |
llm-calls |
fetch_trace.py <id> llm-calls [start] [count] |
Paginated LLM call list |
llm-call |
fetch_trace.py <id> llm-call <N> [--offset <chars>] |
Single LLM call full content |
answer |
fetch_trace.py <id> answer |
Final answer only |
What ships with it
1 file 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 · 81 lines · 72 tokens per session scan A d7dd643eabb6
trace-qa is a skill published in the GitHub repository dp-archive/archive (1,105 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 803 once invoked, about $0.0003 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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