Profilecli Insights
Grafana LabsOfficialPublished by Grafana Labs
- Skill
- Research and analysis
- Cloud and DevOps
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for investigating service performance using continuous profiling data. It guides the user through connecting to a Pyroscope server, querying specific profile types (such as CPU, memory, or mutex contention), and mapping identified hot functions directly to the local source code repository. By leveraging profilecli and pprof, it enables developers to pinpoint performance bottlenecks and verify them against the actual implementation.
What it does
- Validates connectivity to a Pyroscope profiling server.
- Discovers available services and profile types for analysis.
- Queries and downloads live performance profiles using profilecli.
- Analyzes profile data with pprof to identify high-impact functions.
- Correlates hot functions with local source code files and line numbers.
- Generates a performance report with actionable optimization recommendations.
When to use it
- When investigating high CPU, memory, or lock contention in a service.
- When you need to map performance profile data to specific lines of code.
- When verifying if recent code changes have impacted service performance.
- When troubleshooting performance issues in a service monitored by Pyroscope.
Advantages
- Bridges the gap between runtime performance data and static source code.
- Standardizes the investigation process for consistent performance analysis.
- Provides clear guidance on handling authentication and connection configurations.
- Helps identify specific optimization targets like allocation or lock pressure.
Usage examples
- “Investigate why our auth-service is experiencing high CPU usage.”
- The agent will list available services, identify the auth-service, query its CPU profile, and report the top functions mapped to the local source code.
- “Analyze the memory allocation pressure for the payment-processor service.”
- The agent will query the memory allocation profile for the specified service and highlight the functions responsible for the most allocations.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/grafana/profilecli-insights/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip profilecli-insights.zip -d .agents/skills
Documentation by Grafana Labs
Profilecli Insights
You are a performance analysis assistant. Query a remote Pyroscope continuous profiling server with profilecli, then correlate the results with source code in the current repository to provide actionable insights.
Follow these steps in order. Do not skip steps.
Step 1: Ensure profilecli is available
Check that profilecli is on PATH:
profilecli --versionIf it is not found, instruct the user to download it from https://github.com/grafana/pyroscope/releases/latest/download/.
Select the command to use for all later profile analysis:
if command -v pprof >/dev/null 2>&1; then
PPROF=(pprof)
else
PPROF=(go tool pprof)
fiStep 2: Verify connectivity and data exists
Run a series query to validate the connection and discover profile types:
profilecli query series --label-names=__profile_type__ --output jsonIf this succeeds, parse the JSON output and retain the available __profile_type__ values. Common types include:
process_cpu:cpu:nanoseconds:cpu:nanoseconds(CPU)memory:alloc_space:bytes:space:bytes(memory allocations)memory:inuse_space:bytes:space:bytes(memory in-use)goroutine:goroutine:count:goroutine:count(goroutines)mutex:contentions:count:contentions:count(mutex contention)block:contentions:count:contentions:count(block contention)
You need these profile types in Step 4.
If the query fails, help the user configure the connection:
Run a local Pyroscope server on port
4040.Or connect to Grafana with a service account token.
PROFILECLI_URL is required. Set it to the Pyroscope server URL, such as http://localhost:4040, or to a Grafana data source proxy URL when using PROFILECLI_TOKEN, such as https://my-grafana.example.com/api/datasources/proxy/uid/<datasource-uid>.
PROFILECLI_TOKEN is required for Grafana Cloud. It must be a Grafana service account token in glsa_... format with the Viewer role. PROFILECLI_TENANT_ID is optional for multi-tenant setups.
Then stop and wait for the user to configure the environment and for the initial query to succeed.
Step 3: Discover services
List available services and find ones that correlate with the checked-out repository:
profilecli query series --query '{}' --label-names service_repository --label-names service_name --output jsonParse the JSON output for service_name and service_repository. Compare service_repository to git remote get-url origin; matching services are most relevant. Match the user's question to one or more service names.
If the question does not clearly map to a service, show the available services, highlight repository matches, and ask the user which service to analyze.
Step 4: Query the relevant profile type
Query the target service with an appropriate type discovered in Step 2. The query must be a valid ProfileQL label selector.
PROFILE="$(mktemp -t profilecli-insights)"
profilecli query profile \
--query '<QUERY>' \
--profile-type <PROFILE_TYPE> \
--from now-1h --to now \
--output "pprof=${PROFILE}" -fIf the output is empty, broaden the range to --from now-6h or --from now-24h.
Analyze the generated profile:
"${PPROF[@]}" -lines -top -cum "${PROFILE}"Step 5: Identify hot functions
Extract the functions with the most flat and cumulative samples. Highlight:
High flat time, which identifies self time.
High cumulative time, which includes callees.
Significant runtime and standard-library functions:
runtime.mallocgcsuggests allocation pressure,runtime.futexorruntime.locksuggests lock contention,runtime.gcBgMarkWorkerorruntime.gcDrainsuggests GC pressure, andcompress/gziporcompress/flatesuggests compression overhead.
Step 6: Map hot functions to source code
The pprof -lines -top -cum output lists functions in this format:
<flat> <flat%> <sum%> <cum> <cum%> <function-name> <source-file>:<line>For example:
1859.03s 23.50% ... github.com/grafana/pyroscope/pkg/distributor.(*Distributor).PushBatch.func1 github.com/grafana/pyroscope/pkg/distributor/distributor.go:380Use the source path after the function name to correlate profile frames with this checkout:
Normalize the selected service's
service_repositoryinto its module prefix: remove the URL scheme, any SSH user and host separator, and a trailing.git. For example,https://github.com/grafana/pyroscope.gitbecomesgithub.com/grafana/pyroscope.Frames beginning with that module prefix, without an
@versionsuffix, are likely in this repository. Third-party Go dependencies typically include@v...in their module path.Strip the module prefix from an in-repository frame to get a relative path. For example,
github.com/grafana/pyroscope/pkg/distributor/distributor.go:380becomespkg/distributor/distributor.goat line 380.If the pprof Build ID includes JSON with a
git_ref, compare it withgit log --oneline -1. If they differ, warn that line numbers may be stale. Usegit log --oneline <git_ref>..HEAD -- <file>to see whether the mapped file changed. If the Build ID has nogit_ref, note that source alignment cannot be verified.Read a window of about 20 lines before and after the reported source line. Extract the relevant method or function from the fully-qualified function name.
For significant third-party or runtime functions, report their likely implications even though source cannot be read from this checkout.
Step 7: Deliver analysis
Present a structured report with these sections:
Summary
Give a two- to three-sentence overview of the profile.
Top Hot Functions
Provide a ranked table with function name, flat and cumulative sample percentages, source file and line for repository functions, and a brief description.
Source Code Analysis
For each hot repository function, show the relevant source snippet, explain why it may be hot, and propose specific optimizations such as reducing allocations, caching results, using sync.Pool, or reducing lock contention.
Recommendations
List actionable optimization recommendations in expected-impact order.
Error Handling
If
profilecliis missing, direct the user tohttps://github.com/grafana/pyroscope/releases/latest.For connection errors, verify
PROFILECLI_URLand network access.For
401or403errors, verifyPROFILECLI_TOKENandPROFILECLI_TENANT_ID.For empty results, broaden the time range and verify the service with
query series.If a service is not found, list the available services and ask the user to choose one.
Files
- ATTRIBUTION.mdAdded by AutoGineer858 B
- LICENSELicence file added by AutoGineer11.1 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools
The author says: Requires profilecli and pprof, or Go with go tool pprof, on PATH plus access to a Pyroscope-compatible server.
Security
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Attribution and licence
- Published by
- Grafana Labs
- Publisher
- Grafana Labs (Official)
- Source
- grafana/skills · skills/grafana-lgtm/profilecli-insights · commit 8fb72a5
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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