Borrowing it
Nothing to install: this file belongs to r3bl-org/r3bl-open-core. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/r3bl-org/r3bl-open-core/main/.agents/skills/analyze-performance/SKILL.mdgit clone --depth 1 https://github.com/r3bl-org/r3bl-open-coreWrote 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/r3bl-org/r3bl-open-core/analyze-performance)<a href="https://agentmods.dev/skills/r3bl-org/r3bl-open-core/analyze-performance"><img src="https://agentmods.dev/badge/skills/r3bl-org/r3bl-open-core/analyze-performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/r3bl-org/r3bl-open-core/analyze-performance"><img src="https://agentmods.dev/badge/skills/r3bl-org/r3bl-open-core/analyze-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00038 | $0.01889 |
| Opus 5 | $0.00019 | $0.00945 |
| Sonnet 5 | $0.00008 | $0.00378 |
| Haiku 4.5 | $0.00004 | $0.00189 |
Grade A, and why
analyze-performance 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 10d 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Regression Analysis with Flamegraphs
When to Use
- Optimizing performance-critical code
- Detecting performance regressions after changes
- Establishing performance baselines for reference
- Investigating performance issues or slow code paths
- Before creating commits with performance-sensitive changes
- When user says "check performance", "analyze flamegraph", "detect regressions", etc.
Instructions
Follow these steps to analyze performance and detect regressions:
Step 1: Generate Current Flamegraph
Run the automated benchmark script to collect current performance data:
./run.fish run-examples-flamegraph-fold --benchmark
What this does:
- Runs an 8-second continuous workload stress test
- Samples at 999Hz for high precision
- Tests the rendering pipeline with realistic load
- Generates flamegraph data in:
tui/flamegraph-benchmark.perf-folded
Implementation details:
- The benchmark script is in
script-lib.fish - Uses an automated testing script that stress tests the rendering pipeline
- Simulates real-world usage patterns
Step 2: Compare with Baseline
Compare the newly generated flamegraph with the baseline:
Baseline file:
tui/flamegraph-benchmark-baseline.perf-folded
Current file:
tui/flamegraph-benchmark.perf-folded
The baseline file contains:
- Performance snapshot of the "current best" performance state
- Typically saved when performance is optimal
- Committed to git for historical reference
Step 3: Analyze Differences
Compare the two flamegraph files to identify regressions or improvements:
Key metrics to analyze:
-
Hot path changes
- Which functions appear more/less frequently?
- New hot paths that weren't in baseline?
-
Sample count changes
- Increased samples = function taking more time
- Decreased samples = optimization working!
-
Call stack depth changes
- Deeper stacks might indicate unnecessary abstraction
- Shallower stacks might indicate inlining working
What ships with it
1 file 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.
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.
- 10d ago First seen · 309 lines · 38 tokens per session scan A 898d301ce4cf
analyze-performance is a skill published in the GitHub repository r3bl-org/r3bl-open-core (483 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 1,889 once invoked, about $0.0002 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.
Other skills, from other repositories
cw-dogfood
Use when a Codewhale change needs proving in the real product, or when asked to build/install/dogfood the local binaries: stamped release build, atomic install, fresh-shell verification, and the manual QA that gates cannot cover.
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
cw-land
Use when turning verified Codewhale work into commits, branches, or a merge: choosing direct-main vs. worktree vs. integration branch, preserving contributor credit, and honoring the gate artifact before merging.
cw-slice
Use before writing code for any Codewhale feature, upgrade, or refactor: find the existing owner of the behavior, bound the change to one reviewable slice, and fix the evidence bar before you start.
cw-handoff
Use when writing a Codewhale takeover prompt, continuation note, or end-of-session summary for another agent or a later session: a paste-ready handoff grounded in live state, with done/suspected/blocked kept separate.
cw-orient
Use at the start of any Codewhale work session, or when unsure which checkout, branch, or worktree is authoritative: establish live repo truth before reading a plan or editing a file.