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 agentmods add skills/jwuthri/tracely-ai/ai-observability-langchain-pythonnpx skills add Jwuthri/Tracely-ai --skill ai-observability-langchain-pythongit 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/ai-observability-langchain-python)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/ai-observability-langchain-python"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/ai-observability-langchain-python.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 | $0.00021 | $0.01996 |
| Opus 5 | $0.00010 | $0.00998 |
| Sonnet 5 | $0.00004 | $0.00399 |
| Haiku 4.5 | $0.00002 | $0.00200 |
Grade A, and why
ai-observability-langchain-python 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 5d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PostHog AI Observability for LangChain (Python)
Wire up PostHog's AI Observability so calls made through LangChain (Python) land in LLM Analytics as a full session → trace → span → generation tree — not just isolated $ai_generation events.
Prerequisite — vendor LLM SDK
This skill instruments the LLM calls the project already makes. It does not install the vendor SDK for you.
Check the project's manifest for an LLM package. The catalog is far wider than the obvious providers — 68 variants covering agent frameworks (openai-agents, claude-agent-sdk, LangGraph, CrewAI, Mastra, …) and OpenAI-compatible gateways (Groq, OpenRouter, Together, Ollama, …), which an app reaches through the openai package plus a baseURL override. 1-begin.md carries the ordered decision rules; follow them rather than matching on the first familiar package name. If no LLM SDK is present, switch to the manual-capture variant — it posts $ai_generation events directly and works standalone.
Everything else this skill needs — PostHog credentials, instrumentation packages, env vars — the skill installs and configures itself. It does not require a pre-existing posthog.init(...). If one is already there, reuse its env-var names in 3-instrument.md; if not, that step sets fresh values via set_env_values.
Steps
Read every referenced file before editing. Then work through them in order:
- Begin — see
references/1-begin.md. Pick the variant with the ordered rules (framework before provider, gateway base URL before the SDK it borrows), then read four facts from the code: the conversation, the user, the turn, and whether the app registers tools. - Install — see
references/2-install.md. Declare the variant's packages in the manifest — and only those. For providers and gateways that's the PostHog SDK alongside the vendor SDK, with no OpenTelemetry packages. - Instrument — see
references/3-instrument.md. Swap the vendor client for PostHog's wrapper, attach$ai_session_id, a per-turnposthog_trace_id, and the distinct id to every call, and capture tool runs as$ai_spanevents. This step is what turns isolated generations into a session tree. - Verify — see
references/4-verify.md. Describe a request the user can trigger, and grade what lands in PostHog — one session, grouped traces, right attribution — rather than what the diff contains.
What ships with it
13 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.
- .posthog-wizard 0 B
- references/1-begin.md 3.5 KB
- references/2-install.md 1.5 KB
- references/3-instrument.md 3.8 KB
- references/4-verify.md 2.4 KB
- references/basics.md 4.3 KB
- references/COMMANDMENTS.md 3.1 KB
- references/generations.md 10 KB
- references/langchain.md 8.0 KB
- references/python.md 32 KB
- references/sessions.md 3.5 KB
- references/spans.md 2.5 KB
- references/traces.md 7.1 KB
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.
- 5d ago First seen · 86 lines · 21 tokens per session scan A 4d3fec79f9ed
ai-observability-langchain-python is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,189 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 1,996 once invoked, about $0.0001 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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