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 fxd0h/Axelera-Voyager-Local-Assistant --skill voyager-debuggit clone --depth 1 https://github.com/fxd0h/Axelera-Voyager-Local-AssistantWrote 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/fxd0h/axelera-voyager-local-assistant/voyager-debug)<a href="https://agentmods.dev/skills/fxd0h/axelera-voyager-local-assistant/voyager-debug"><img src="https://agentmods.dev/badge/skills/fxd0h/axelera-voyager-local-assistant/voyager-debug/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/fxd0h/axelera-voyager-local-assistant/voyager-debug"><img src="https://agentmods.dev/badge/skills/fxd0h/axelera-voyager-local-assistant/voyager-debug.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 | $0.00103 | $0.02122 |
| Opus 5 | $0.00051 | $0.01061 |
| Sonnet 5 | $0.00021 | $0.00424 |
| Haiku 4.5 | $0.00010 | $0.00212 |
Grade A, and why
voyager-debug 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 12d 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 — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Pipeline or Model
Systematically debug and fix issues with Axelera AI pipelines, models, or inference
Use This Skill When / Not When
- Use when: the user brings an existing error, crash, log, regression, or wrong output.
- Use when: the SDK install is missing, broken, or old, the driver/firmware
mismatch, or the Metis board is not detected -- follow
references/install-and-board-health.md. - Not when: debugging is part of building a brand-new pipeline -- route to voyager-launch.
- Not when: they want pure performance measurement of a working pipeline -- route to voyager-bench.
- Process precedence: when a failure is on the table, this skill runs before any domain skill changes code or config.
Instructions
Debug the specified issue: $ARGUMENTS
{{INCLUDE common/voyager-sdk-setup.md}}
{{INCLUDE common/voyager-task-integration.md}}
Step 1: Issue Classification
Identify the type of issue:
- Deployment Error: Model fails to compile/deploy
- Runtime Error: Inference fails or crashes
- Accuracy Issue: Model produces incorrect results
- Performance Issue: Low throughput or high latency
- Display Issue: Visualization problems
Step 2: Environment Diagnostics
Run initial diagnostics. Only probe hardware directly when
.voyager-runtime.json says execution.mode is execute_on_device.
# Check environment
echo "AXELERA_FRAMEWORK: $AXELERA_FRAMEWORK"
echo "AXELERA_RUNTIME_DIR: $AXELERA_RUNTIME_DIR"
echo "PYTHONPATH: $PYTHONPATH"
# Check hardware on a Voyager SDK host
axdevice
# Check Python environment
python --version
pip list | grep axelera
# Version evidence for install/driver/firmware problems
axversion # SDK version
axversion --driver # driver version
axdevice -v # firmware + board controller
If the problem is the environment itself (SDK missing or old, driver or
firmware mismatch, board not detected, ENOTTY/AXL_IOCTL errors), follow
references/install-and-board-health.md: it carries the fresh-install
commands, the v1.7.0 compatibility matrix, the upgrade path including the
post-upgrade driver/model-recompile trap, and the board-not-detected
ladder. Firmware flashing, device reboots, and system package installs
always need explicit user confirmation first.
What ships with it
7 files 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.
- 12d ago First seen · 330 lines · 103 tokens per session scan A 69c7a2a77c65
voyager-debug is a skill published in the GitHub repository fxd0h/Axelera-Voyager-Local-Assistant (4 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 2,122 once invoked, about $0.0005 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 2026-08-31.
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