Tracely is a CI/CD system for AI agents that turns failed production traces into replayable regression tests. Development teams use it to detect and group agent failures, run the resulting cases on pull requests, and block changes that reproduce those failures. The catalogue entries provide skills for operating this trace-based testing and observability workflow.
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 Jwuthri/Tracely-ai --skill tracelygit clone --depth 1 https://github.com/Jwuthri/Tracely-aiWrote 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/jwuthri/tracely-ai/tracely)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/tracely"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/tracely/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/jwuthri/tracely-ai/tracely"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/tracely.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 170 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00120 | $0.02800 |
| Opus 5.5 | $0.00048 | $0.01120 |
| Sonnet 5.5 | $0.00024 | $0.00560 |
| Haiku 4.5 | $0.00012 | $0.00280 |
Grade A, and why
tracely 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 2mo 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tracely
Trace-native CI/CD for AI agents. One loop:
production trace → failure detection → regression test → CI gate
The trace is the source of truth. Evaluators, failure clusters, regression cases, gates and trends are all derived from it — there are no hand-authored datasets. Docs: https://doc.tracely-ai.com.
Pick the path first
Do not start writing spans. Ask what the code already looks like, then pick:
| The user's code | Path | Effort |
|---|---|---|
| Calls OpenAI / Anthropic / Gemini / Mistral / Bedrock / Groq SDKs | Automatic — init(instrument="auto") |
1 line |
| Uses LangChain / LangGraph / LlamaIndex / CrewAI / LiteLLM | Automatic + that extra | 1 line |
| Uses OpenAI Agents SDK / Claude Agent SDK / Google ADK | Automatic, named explicitly | 1 line |
| Has business logic worth seeing (routers, tools, retrievers) | Automatic + @observe |
1 decorator each |
| Needs spans the auto path can't produce (custom retrievers, guardrails, handoffs, multimodal I/O, hand-rolled providers) | Manual context managers | references/manual.md |
| Is TypeScript / Go / Ruby / anything not Python | Emit OTLP directly — no SDK | references/automatic.md § other languages |
| Already emits OpenTelemetry / OpenInference / OpenLLMetry | Point the existing exporter at Tracely, add 2 attributes | references/automatic.md § other languages |
Default to automatic. Manual spans are the escape hatch, not the starting point, and the two compose — manual spans nest inside auto-instrumented traces in the same tree.
Connect
import tracely_sdk as tracely # pip install "tracely-ai[openai]" ([anthropic] [langchain] [all])
tracely.init(
endpoint="http://localhost:8000", # hosted: https://api.tracely-ai.com
api_key="tracely_dev_key", # an ingest key — Settings → API keys. The key IS the workspace.
service_name="support-agent",
env="prod", # prod | staging | ci | dev — the gating axis
instrument="auto",
)
What ships with it
5 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.
- 2mo ago First seen · 202 lines · 120 tokens per session scan A aa8946b56328
tracely is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,311 stars, last pushed yesterday), licensed MIT. It adds 120 tokens to every session and 2,800 once invoked, about $0.0005 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.
tracely compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| tracelyJwuthri/Tracely-ai | Skill | 1.3k | yesterday | MIT | A | 120 | Claude Code, Codex |
| Prompt Version Control WorkflowNotysoty/openagentskills | Skill | 9 | 2mo ago | MIT | A | 30 | Claude Code, Codex |
| llm-testerbuiphucminhtam/forgewright | Skill | 49 | 2d ago | none | A | 49 | Claude Code |
| report-repairastandrik/my_instructions | Skill | 6 | 1mo ago | MIT | A | 16 | Claude Code |
| local-validatorastandrik/my_instructions | Skill | 6 | 1mo ago | MIT | A | 20 | Claude Code |
| oh-my-poshJanDeDobbeleer/oh-my-posh | Skill | 24k | yesterday | MIT | A | 47 | Claude Code, Codex |
| spark-engineerJeffallan/claude-skills | Skill | 12k | 10d ago | MIT | A | 75 | Claude Code |