diff --git a/skills/langfuse/SKILL.md b/skills/langfuse/SKILL.md
index 9de951c..284d726 100644
--- a/skills/langfuse/SKILL.md
+++ b/skills/langfuse/SKILL.md
@@ -24,6 +24,7 @@ Follow these principles for ALL Langfuse work:
 - capturing user feedback (thumbs, ratings, implicit signals) as scores on traces: references/user-feedback.md
 - further tips on using the Langfuse CLI: references/cli.md
 - upgrading or migrating Langfuse SDKs to the latest version: references/sdk-upgrade.md
+- checking trace quality and diagnosing instrumentation issues: references/trace-quality-check.md
 - submitting feedback about this skill: references/skill-feedback.md
 
 ## 1. Langfuse API via CLI
diff --git a/skills/langfuse/references/trace-quality-check.md b/skills/langfuse/references/trace-quality-check.md
new file mode 100644
index 0000000..06af56b
--- /dev/null
+++ b/skills/langfuse/references/trace-quality-check.md
@@ -0,0 +1,241 @@
+---
+name: langfuse-trace-quality-check
+description: Analyze a sample of recent traces from a Langfuse project and suggest concrete instrumentation improvements. Use when a user wants to check their trace quality, debug poorly-structured traces, or improve their observability setup.
+---
+
+# Trace Quality Check
+
+Fetch and analyze recent traces from a user's Langfuse project, then generate actionable instrumentation improvements.
+
+## When to Use
+
+- User says traces "look wrong" or aren't capturing what they expect
+- User wants to verify their instrumentation is set up correctly
+- User asks to improve their observability or trace quality
+- Onboarding a new user who already has traces flowing
+
+## Workflow
+
+### 1. Fetch a Sample of Recent Traces
+
+Use the Langfuse CLI to pull recent traces. Fetch enough to see a variety of trace shapes — aim for 20–50 traces.
+
+```bash
+# Fetch recent traces (adjust limit as needed)
+npx langfuse-cli api traces list --limit 50
+```
+
+If the project has high volume, also sample across different time windows or use tags/names to get diverse trace shapes:
+
+```bash
+# Filter by name if the user reports issues with a specific flow
+npx langfuse-cli api traces list --limit 20 --name "chat-response"
+```
+
+For each trace that looks potentially problematic, fetch its full observation tree:
+
+```bash
+npx langfuse-cli api traces get --id <trace-id>
+```
+
+### 2. Analyze Trace Structure
+
+Check each trace against these anti-patterns:
+
+| Anti-pattern | What it looks like | Severity |
+| --- | --- | --- |
+| Generic HTTP root span | Root observation is `POST /api/chat` or similar with no named children | High |
+| Missing generation spans | No observations with type `GENERATION` — LLM calls aren't being tracked | High |
+| Flat single-span trace | Trace contains only one observation for a multi-step operation | High |
+| Over-instrumentation | 50+ child observations for a simple call — framework internals leaking through | Medium |
+| Missing model name | Generation observations exist but `model` field is null | Medium |
+| Missing token counts | Generation observations have no `usage` data (input/output tokens) | Medium |
+| No user or session IDs | Traces lack `userId` and `sessionId` even though the app is user-facing or conversational | Medium |
+| Generic trace names | Names like `trace-1`, `unnamed`, or auto-generated UUIDs | Low |
+| Missing input/output | Observations have empty or null `input`/`output` fields | Medium |
+| Leaking all function args as input | Trace input contains API keys, configs, or irrelevant parameters instead of just the user message | High |
+
+**What a good trace looks like** (use as a reference when evaluating):
+
+- Represents one self-contained unit of work (a single chat turn, one agent execution, one pipeline run)
+- Uses correct observation types: LLM calls are `GENERATION`, tool calls are `SPAN` or have clear names
+- Has descriptive, action-oriented names (`classify-intent`, `generate-response` — not model names like `gpt-4o`)
+- Every observation has meaningful input and/or output
+- Generations include model name and token usage
+- Metadata, tags, user IDs, and session IDs are present where relevant
+- Noisy HTTP spans, database queries, and framework internals are filtered out
+
+### 3. Generate the Improvement Report
+
+For each issue found, output a specific suggestion with:
+- **What's wrong**: describe the anti-pattern observed, referencing specific trace IDs
+- **Why it matters**: explain the impact on debugging/analytics
+- **How to fix**: provide a concrete code snippet
+
+Structure the report as a prioritized list, grouped by severity (high → medium → low).
+
+#### Common suggestions and code snippets
+
+**Missing OpenAI generation tracking (Python):**
+
+```python
+# Before: raw OpenAI client — calls won't appear as generations
+from openai import OpenAI
+client = OpenAI()
+
+# After: Langfuse-wrapped client — calls are automatically tracked
+from langfuse.openai import OpenAI
+client = OpenAI()
+```
+
+**Missing OpenAI generation tracking (JS/TS):**
+
+```typescript
+// Before: raw OpenAI client
+import OpenAI from "openai";
+const client = new OpenAI();
+
+// After: wrapped with Langfuse observeOpenAI
+import OpenAI from "openai";
+import { observeOpenAI } from "langfuse";
+const client = observeOpenAI(new OpenAI());
+```
+
+**Flat traces — add nested spans (Python):**
+
+```python
+from langfuse.decorators import observe, langfuse_context
+
+@observe()
+def handle_request(user_message):
+    context = retrieve_context(user_message)
+    response = generate_response(user_message, context)
+    return response
+
+@observe()
+def retrieve_context(query):
+    # retrieval logic — appears as a child span
+    ...
+
+@observe()
+def generate_response(query, context):
+    # LLM call — appears as a child span
+    ...
+```
+
+**Flat traces — add nested spans (JS/TS):**
+
+```typescript
+import { Langfuse } from "langfuse";
+const langfuse = new Langfuse();
+
+const trace = langfuse.trace({ name: "handle-request" });
+
+const retrievalSpan = trace.span({ name: "retrieve-context" });
+// ... retrieval logic
+retrievalSpan.end();
+
+const generationSpan = trace.generation({
+  name: "generate-response",
+  model: "gpt-4o",
+  input: messages,
+});
+// ... LLM call
+generationSpan.end({ output: result, usage: { input: 100, output: 50 } });
+```
+
+**Missing user/session IDs:**
+
+```python
+from langfuse.decorators import observe, langfuse_context
+
+@observe()
+def handle_request(user_message, user_id, session_id):
+    langfuse_context.update_current_trace(
+        user_id=user_id,
+        session_id=session_id,
+    )
+    ...
+```
+
+**Generic trace names:**
+
+```python
+# Before: no name — trace will be auto-named after the function
+@observe()
+def main(input):
+    ...
+
+# After: descriptive name
+@observe(name="chat-response")
+def main(input):
+    ...
+```
+
+**Leaking function args as input:**
+
+```python
+@observe()
+def handle_request(user_message, api_key, config):
+    # By default, @observe captures ALL args as input — including api_key and config
+    # Fix: explicitly set the input to only the relevant data
+    langfuse_context.update_current_observation(
+        input={"message": user_message}
+    )
+    ...
+```
+
+**Over-instrumentation — disable noisy auto-tracing:**
+
+If using a framework integration that captures too many internal spans, selectively disable or filter:
+
+```python
+# For OpenTelemetry-based integrations, configure span filtering
+# to exclude low-level HTTP/DB spans
+```
+
+Point the user to the relevant framework integration docs for specific filtering options.
+
+### 4. Present Results
+
+Format the output as a clear, prioritized report. Example structure:
+
+```
+## Trace Quality Report
+
+Analyzed 35 traces from the last 24 hours.
+
+### High Priority
+
+1. **OpenAI calls not captured as generations** (found in 28/35 traces)
+   Your OpenAI calls are going through the raw client, so they don't appear
+   as generation observations. This means no token tracking or cost calculation.
+
+   Fix: [code snippet]
+
+2. **All function args leaking into trace input** (found in 35/35 traces)
+   Trace inputs contain API keys and config objects alongside the user message.
+
+   Fix: [code snippet]
+
+### Medium Priority
+
+3. **No session IDs on conversational traces** (found in 35/35 traces)
+   Your app handles multi-turn conversations but traces aren't grouped by session.
+   This makes it impossible to follow a full conversation flow.
+
+   Fix: [code snippet]
+
+### Looking Good
+
+- Trace names are descriptive and consistent
+- Span hierarchy is well-structured for retrieval steps
+```
+
+Do NOT auto-apply any changes. Present the report for the user to review and decide what to implement.
+
+## References
+
+- What a good trace looks like: https://langfuse.com/faq/all/what-does-a-good-trace-look-like
+- Tracing overview: https://langfuse.com/docs/tracing
+- Framework integrations: https://langfuse.com/docs/integrations