Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mohitmishra786/low-level-dev-skills --skill flamegraphsgit clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skillsWrote 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/mohitmishra786/low-level-dev-skills/flamegraphs)<a href="https://agentmods.dev/skills/mohitmishra786/low-level-dev-skills/flamegraphs"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/flamegraphs/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/mohitmishra786/low-level-dev-skills/flamegraphs"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/flamegraphs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00081 | $0.01285 |
| Opus 5 | $0.00041 | $0.00642 |
| Sonnet 5 | $0.00016 | $0.00257 |
| Haiku 4.5 | $0.00008 | $0.00128 |
Grade A, and why
flamegraphs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flamegraphs
Purpose
Guide agents through the pipeline from profiler data to SVG flamegraph, and teach interpretation of flamegraphs to drive concrete optimisation decisions.
Triggers
- "How do I generate a flamegraph from perf data?"
- "How do I read a flamegraph?"
- "The flamegraph shows a wide frame — what does that mean?"
- "How do I generate a flamegraph from Callgrind?"
- "I want to compare two flamegraphs (before/after)"
Workflow
1. Install FlameGraph tools
git clone https://github.com/brendangregg/FlameGraph
# No install needed; scripts are in the repo
export PATH=$PATH:/path/to/FlameGraph
2. perf → flamegraph (most common path)
# Step 1: record
perf record -F 999 -g -o perf.data ./prog
# Step 2: generate script output
perf script -i perf.data > out.perf
# Step 3: collapse stacks
stackcollapse-perf.pl out.perf > out.folded
# Step 4: generate SVG
flamegraph.pl out.folded > flamegraph.svg
# Step 5: view
xdg-open flamegraph.svg # Linux
open flamegraph.svg # macOS
One-liner:
perf record -F 999 -g ./prog && perf script | stackcollapse-perf.pl | flamegraph.pl > fg.svg
3. Differential flamegraph (before/after)
# Collect two profiles
perf record -g -o before.data ./prog_old
perf record -g -o after.data ./prog_new
# Collapse
perf script -i before.data | stackcollapse-perf.pl > before.folded
perf script -i after.data | stackcollapse-perf.pl > after.folded
# Diff (red = regressed, blue = improved)
difffolded.pl before.folded after.folded | flamegraph.pl > diff.svg
4. Callgrind → flamegraph
valgrind --tool=callgrind --callgrind-out-file=cg.out ./prog
stackcollapse-callgrind.pl cg.out | flamegraph.pl > fg.svg
5. Other profiler inputs
# Go pprof
go tool pprof -raw -output=prof.txt prog
stackcollapse-go.pl prof.txt | flamegraph.pl > fg.svg
# DTrace
dtrace -x ustackframes=100 -n 'profile-99 /execname=="prog"/ { @[ustack()] = count(); }' \
-o out.stacks sleep 10
stackcollapse.pl out.stacks | flamegraph.pl > fg.svg
# Java (async-profiler)
async-profiler -d 30 -f out.collapsed PID
flamegraph.pl out.collapsed > fg.svg
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.
- 9d ago First seen · 157 lines · 81 tokens per session scan A 5a002fa55eab
flamegraphs is a skill published in the GitHub repository mohitmishra786/low-level-dev-skills (203 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 1,285 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
log-analyzer
Parse agent log files to identify error patterns, rate limit hits, timeout clusters, tool failures, and component-level error counts. Produces a structured anomaly report. Cron-compatible — silent if no issues, alert digest if anomalies found. Also computes per-tool failure rates from a Hermes profile state.db…
debugging
Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.
error-handler
Design error handling, structured logging, and observability with OpenTelemetry (traces, metrics, logs), error classification, recovery patterns (retry with jitter, circuit breaker, bulkhead, timeout), error budgets/SLOs with burn rate alerts, and production incident triage. Use when user asks to implement error…
performance-profiler
Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile…
scientific-debugging
A method for debugging software by observing the problem, forming possible explanations, running small experiments, and then fixing and checking the result.
diagnosing-ml-failures
Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance…