소스 정보
- 저장소
- Jwuthri/Tracely-ai
- 최근 소스 활동
- 2026년 8월 23일 23:26
- 감지된 SKILL.md 언어
- 영어
- 스타
- 1,161
- 포크
- 91
설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/Jwuthri/Tracely-ai --skill ai-observability-langchain-python명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Instrument AI agents with Tracely and turn their production traces into CI gates. Use when the user mentions Tracely, tracely-ai, tracely_sdk, the `tracely` CLI, or asks to trace/observe an AI agent, add LLM evaluators or LLM-as-a-judge columns, debug why a trace or conversation isn't showing up, wire agent regression tests into a PR check, run scenario or red-team suites against an agent endpoint, or replay recorded agent failures in CI. Covers both zero-span-code automatic instrumentation and the manual span API.
Map SEO market leaders, winning content themes, keyword coverage, backlinks, and strategic gaps.
Analyze one competitor's organic footprint, ranking keywords, content themes, backlinks, and gaps.
SKILL.md 표시 중
| name | ai-observability-langchain-python |
| description | PostHog AI Observability integration for LangChain (Python) |
| metadata | {"author":"PostHog","version":"1.48.0"} |
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.
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.
Read every referenced file before editing. Then work through them in order:
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.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.references/3-instrument.md. Swap the vendor client for PostHog's wrapper, attach $ai_session_id, a per-turn posthog_trace_id, and the distinct id to every call, and capture tool runs as $ai_span events. This step is what turns isolated generations into a session tree.references/4-verify.md. Describe a request the user can trigger, and grade what lands in PostHog — one session, grouped traces, right attribution — rather than what the diff contains.references/1-begin.md - Pick the variant that matches this project, then read the four facts the instrumentation needsreferences/2-install.md - Declare the packages the variant needs, and no othersreferences/3-instrument.md - Swap in the wrapper client, then attach identity and tool spans so the calls form a session treereferences/4-verify.md - Give the user a way to trigger one turn, and grade the tree that reaches PostHogreferences/langchain.md - Langchain ai observability installation - docsreferences/python.md - Python - docsreferences/basics.md - Ai observability basics - docsreferences/generations.md - Generations - docsreferences/traces.md - Traces - docsreferences/sessions.md - Sessions - docsreferences/spans.md - SPAns - docsreferences/COMMANDMENTS.md - Framework-specific rules the integration must followThe linked install page carries the exact code blocks for this variant's language. Prefer copying from there over reconstructing from memory — package names and initialization shapes change between AIO releases.
<ph_project_token> and <ph_client_api_host> from env, using the framework's env-var convention. Never hardcode either value.opentelemetry-* variants and LlamaIndex, and never swap a framework's own tracing hook for an instrumentor.$ai_session_id per conversation, one posthog_trace_id per turn, shared by every call in it. An id minted per call is worse than none — it looks instrumented and groups nothing.$ai_span event sharing the turn's trace id — the wrapper never sees your dispatch loop. Framework variants emit these themselves; an app with no tools correctly has none.integration skill — do not add or edit them here.When you finish, write .posthog-wizard-cache/.posthog-ai.json at the project root:
{ "provider": "openai", "package": "@posthog/ai", "otel_init_file": "src/instrumentation.ts" }
otel_init_file keeps its name for the report's sake, but on the wrapper path there is no OTel init — set it to the file where the wrapper client was constructed (or, on the manual path, where the capture helper lives).
The report/ step reads this file to render an AI Observability section in the setup report. If the cache directory does not exist, create it.
pip install posthog or pip install -r requirements.txt and do NOT use unquoted version specifiers like >= directly in shell commands