Is performance-profiling safe to install?

SaferSkills independently audited performance-profiling (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

skills/performance-profiling/SKILL.md · 1 file

skills/performance-profiling/SKILL.md7.9 KB · Markdown

name: performance-profiling

description: Identify computational bottlenecks, analyze scaling behavior, estimate memory requirements, and receive optimization recommendations for any computational simulation. Use when simulations are slow, investigating parallel efficiency, planning resource allocation, or seeking performance improvements through timing analysis, scaling studies, memory profiling, or bottleneck detection.

allowed-tools: Read, Bash, Write, Grep, Glob

Performance Profiling

Goal

Provide tools to analyze simulation performance, identify bottlenecks, and recommend optimization strategies for computational materials science simulations.

Requirements

Python 3.8+
No external dependencies (uses Python standard library only)
Works on Linux, macOS, and Windows

Inputs to Gather

Before running profiling scripts, collect from the user:

Input	Description	Example
Simulation log	Log file with timing information	`simulation.log`
Scaling data	JSON with multi-run performance data	`scaling_data.json`
Simulation parameters	JSON with mesh, fields, solver config	`params.json`
Available memory	System memory in GB (optional)	`16.0`

Decision Guidance

When to Use Each Script

Need to identify slow phases?
├── YES → Use timing_analyzer.py
│         └── Parse simulation logs for timing data
│
Need to understand parallel performance?
├── YES → Use scaling_analyzer.py
│         └── Analyze strong or weak scaling efficiency
│
Need to estimate memory requirements?
├── YES → Use memory_profiler.py
│         └── Estimate memory from problem parameters
│
Need optimization recommendations?
└── YES → Use bottleneck_detector.py
          └── Combine analyses and get actionable advice

Choosing Analysis Thresholds

Metric	Good	Acceptable	Poor
Phase dominance	<30%	30-50%	>50%
Parallel efficiency	>0.80	0.70-0.80	<0.70
Memory usage	<60%	60-80%	>80%

Script Outputs (JSON Fields)

Script	Key Outputs
`timing_analyzer.py`	`timing_data.phases`, `timing_data.slowest_phase`, `timing_data.total_time`
`scaling_analyzer.py`	`scaling_analysis.results`, `scaling_analysis.efficiency_threshold_processors`
`memory_profiler.py`	`memory_profile.total_memory_gb`, `memory_profile.per_process_gb`, `memory_profile.warnings`
`bottleneck_detector.py`	`bottlenecks`, `recommendations`

Workflow

Complete Profiling Workflow

Analyze timing from simulation logs
Analyze scaling from multi-run data (if available)
Profile memory from simulation parameters
Detect bottlenecks and get recommendations
Implement optimizations based on recommendations
Re-profile to verify improvements

Quick Profiling (Timing Only)

Run timing analyzer on simulation log
Identify dominant phases (>50% of runtime)
Apply targeted optimizations to dominant phases

CLI Examples

Timing Analysis

# Basic timing analysis
python3 scripts/timing_analyzer.py \
    --log simulation.log \
    --json

# Custom timing pattern
python3 scripts/timing_analyzer.py \
    --log simulation.log \
    --pattern 'Step\s+(\w+)\s+took\s+([\d.]+)s' \
    --json

Scaling Analysis

# Strong scaling (fixed problem size)
python3 scripts/scaling_analyzer.py \
    --data scaling_data.json \
    --type strong \
    --json

# Weak scaling (constant work per processor)
python3 scripts/scaling_analyzer.py \
    --data scaling_data.json \
    --type weak \
    --json

Memory Profiling

# Estimate memory requirements
python3 scripts/memory_profiler.py \
    --params simulation_params.json \
    --available-gb 16.0 \
    --json

Bottleneck Detection

# Detect bottlenecks from timing only
python3 scripts/bottleneck_detector.py \
    --timing timing_results.json \
    --json

# Comprehensive analysis with all inputs
python3 scripts/bottleneck_detector.py \
    --timing timing_results.json \
    --scaling scaling_results.json \
    --memory memory_results.json \
    --json

Conversational Workflow Example

User: My simulation is taking too long. Can you help me identify what's slow?

Agent workflow:

Ask for simulation log file
Run timing analyzer:

   python3 scripts/timing_analyzer.py --log simulation.log --json

Interpret results:

If solver dominates (>50%): Recommend preconditioner tuning
If assembly dominates: Recommend caching or vectorization
If I/O dominates: Recommend reducing output frequency

If user has multi-run data, analyze scaling:

   python3 scripts/scaling_analyzer.py --data scaling.json --type strong --json

Generate comprehensive recommendations:

   python3 scripts/bottleneck_detector.py --timing timing.json --scaling scaling.json --json

Interpretation Guidance

Timing Analysis

Scenario	Meaning	Action
Solver >70%	Solver-dominated	Tune preconditioner, check tolerance
Assembly >50%	Assembly-dominated	Cache matrices, vectorize, parallelize
I/O >30%	I/O-dominated	Reduce frequency, use parallel I/O
Balanced (<30% each)	Well-balanced	Look for algorithmic improvements

Scaling Analysis

Efficiency	Meaning	Action
>0.80	Excellent scaling	Continue scaling up
0.70-0.80	Good scaling	Monitor at larger scales
0.50-0.70	Poor scaling	Investigate communication/load balance
<0.50	Very poor scaling	Reduce processor count or redesign

Memory Profile

Usage	Meaning	Action
<60% available	Safe	No action needed
60-80% available	Moderate	Monitor, consider optimization
>80% available	High	Reduce resolution or increase processors
>100% available	Exceeds capacity	Must reduce problem size

Error Handling

Error	Cause	Resolution
`Log file not found`	Invalid path	Verify log file path
`No timing data found`	Pattern mismatch	Provide custom pattern with --pattern
`At least 2 runs required`	Insufficient data	Provide more scaling runs
`Missing required parameters`	Incomplete params	Add mesh and fields to params file

Optimization Strategies by Bottleneck Type

Solver Bottlenecks

Use algebraic multigrid (AMG) preconditioner
Tighten solver tolerance if over-solving
Consider direct solver for small problems
Profile matrix assembly vs solve time

Assembly Bottlenecks

Cache element matrices if geometry is static
Use vectorized assembly routines
Consider matrix-free methods
Parallelize assembly with coloring

I/O Bottlenecks

Reduce output frequency
Use parallel I/O (HDF5, MPI-IO)
Write to fast scratch storage
Compress output data

Scaling Bottlenecks

Investigate communication overhead
Check for load imbalance
Reduce synchronization points
Use asynchronous communication
Consider hybrid MPI+OpenMP

Memory Bottlenecks

Reduce mesh resolution
Use iterative solver (lower memory than direct)
Enable out-of-core computation
Increase number of processors
Use single precision where appropriate

Limitations

Log parsing: Depends on pattern matching; may miss unusual formats
Scaling analysis: Requires at least 2 runs for meaningful results
Memory estimation: Approximate; actual usage may vary
Recommendations: General guidance; may need domain-specific tuning

References

references/profiling_guide.md - Profiling concepts and interpretation
references/optimization_strategies.md - Detailed optimization approaches

Version History

v1.0.0 (2025-01-22): Initial release with 4 profiling scripts

performance-profiling— agent skill

Is performance-profiling safe to install?

Performance Profiling

Goal

Requirements

Inputs to Gather

Decision Guidance

When to Use Each Script

Choosing Analysis Thresholds

Script Outputs (JSON Fields)

Workflow

Complete Profiling Workflow

Quick Profiling (Timing Only)

CLI Examples

Timing Analysis

Scaling Analysis

Memory Profiling

Bottleneck Detection

Conversational Workflow Example

Interpretation Guidance

Timing Analysis

Scaling Analysis

Memory Profile

Error Handling

Optimization Strategies by Bottleneck Type

Solver Bottlenecks

Assembly Bottlenecks

I/O Bottlenecks

Scaling Bottlenecks

Memory Bottlenecks

Limitations

References

Version History

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