Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skillsnpx agentmods add skills/mohitmishra786/low-level-dev-skills/pgoWrote 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/pgo)<a href="https://agentmods.dev/skills/mohitmishra786/low-level-dev-skills/pgo"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/pgo/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/pgo"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/pgo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00080 | $0.01508 |
| Opus 5 | $0.00040 | $0.00754 |
| Sonnet 5 | $0.00016 | $0.00302 |
| Haiku 4.5 | $0.00008 | $0.00151 |
Grade A, and why
pgo 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PGO (Profile-Guided Optimisation)
Purpose
Guide agents through the full PGO workflow: instrument build → representative workload → collect profile → optimised build, covering both GCC and Clang, plus BOLT for post-link optimisation.
Triggers
- "How do I use PGO to speed up my binary?"
- "What is profile-guided optimization and when should I use it?"
- "How do I use
-fprofile-generateand-fprofile-use?" - "My
-O3build isn't fast enough — what next?" - "How does BOLT differ from PGO?"
- "How do I collect representative profile data?"
Workflow
1. When to use PGO
Is -O3 -march=native already applied?
no → apply standard optimisation first
yes → is workload branch-heavy or has irregular call patterns?
yes → PGO will likely help 5-30%
no → PGO may not help; profile first with linux-perf
PGO helps most with:
- Large binaries with many cold/hot code paths (compilers, databases, servers)
- Branch-heavy code where static prediction is wrong
- Function call-heavy code where inlining decisions improve with profile data
2. GCC PGO workflow
# Step 1: Build with instrumentation
gcc -O2 -fprofile-generate -fprofile-dir=./pgo-data \
prog.c -o prog_instr
# Step 2: Run with representative workload(s)
./prog_instr < workload1.input
./prog_instr < workload2.input
# Generates .gcda files in ./pgo-data/
# Step 3: Build optimised binary using profile
gcc -O2 -fprofile-use -fprofile-dir=./pgo-data \
-fprofile-correction \
prog.c -o prog_pgo
-fprofile-correction: handles profile count inconsistencies from parallel or nondeterministic runs. Always include it.
3. Clang PGO workflow (IR-based, preferred)
# Step 1: Instrument build
clang -O2 -fprofile-instr-generate prog.c -o prog_instr
# Step 2: Run workload (generates default.profraw)
./prog_instr < workload.input
LLVM_PROFILE_FILE="prog-%p.profraw" ./prog_instr # per-PID files for parallel runs
# Step 3: Merge raw profiles
llvm-profdata merge -output=prog.profdata *.profraw
# Step 4: Optimised build
clang -O2 -fprofile-instr-use=prog.profdata prog.c -o prog_pgo
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 · 177 lines · 80 tokens per session scan A a807b6b7e48b
pgo is a skill published in the GitHub repository mohitmishra786/low-level-dev-skills (201 stars, last pushed 2mo ago), licensed MIT. It adds 80 tokens to every session and 1,508 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 2026-08-30.
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