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 OutlineDriven/odin-codex-plugin --skill perf-profilegit clone --depth 1 https://github.com/OutlineDriven/odin-codex-pluginWrote 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/outlinedriven/odin-codex-plugin/perf-profile)<a href="https://agentmods.dev/skills/outlinedriven/odin-codex-plugin/perf-profile"><img src="https://agentmods.dev/badge/skills/outlinedriven/odin-codex-plugin/perf-profile/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/outlinedriven/odin-codex-plugin/perf-profile"><img src="https://agentmods.dev/badge/skills/outlinedriven/odin-codex-plugin/perf-profile.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.01077 |
| Opus 5.5 | $0.00000 | $0.00431 |
| Sonnet 5.5 | $0.00000 | $0.00215 |
| Haiku 4.5 | $0.00000 | $0.00108 |
Grade A, and why
perf-profile 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- perf-profile — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance is a contract with reality. Intuition about hot paths is wrong more often than right. Capture, locate, hypothesize, optimize, re-measure, prove the regression with a benchmark — then defend the win with an invariant.
When to Apply / NOT
Apply: latency or throughput SLO violation; memory pressure (RSS growth, GC churn); benchmark regression; pre-optimization scoping; cold-start vs steady-state cost split; cache locality / branch-prediction concerns.
NOT apply: defect with wrong outputs; architectural redesign; micro-optimization without budget pressure; untested code (write tests first).
Anti-patterns
- Optimize without profile: intuition-driven changes.
- Single-run benchmarks: variance dominates. Use
hyperfine --warmup 3 --min-runs 10. - Profile in debug build: optimizer-disabled binaries lie.
- Confuse flat profile with call-graph: self-time vs total-time tell different stories.
- Ignoring tail latency: p50 stays flat while p99 explodes.
- Cherry-picking the win: re-measure end-to-end.
- Allocation blind spot: CPU profiler hides GC.
- Forgetting the regression guard.
Workflow (language-neutral)
- Define budget — restate target metric: latency p95 < X ms, throughput > Y rps, RSS < Z MB.
- Establish baseline — run unoptimized workload under
hyperfineplus profiler. Save raw artifacts. - Capture profile — sampled CPU profile → flamegraph; allocation profile if memory-bound; latency histogram for tail.
- Locate hotspot — top self-time function or widest plateau. Cross-check with allocation profile.
- Hypothesize — one falsifiable claim with predicted delta.
- Optimize minimally — smallest change targeting the hypothesis.
- Re-profile — capture same metric; differential flamegraph.
- Prove the win —
hyperfine 'baseline' 'optimized' --warmup 3 --min-runs 10. - Guard the win — add CI benchmark with regression bound.
Reading Flamegraphs
- Wide plateau on top: hot self-time function — primary target.
- Narrow towers: deep call chains — examine for over-abstraction.
- Repeated motifs: same callee under many parents — candidate for inlining or caching.
- Missing frames: rebuild with
-fno-omit-frame-pointer/RUSTFLAGS=-C force-frame-pointers=yes. - Differential flamegraph (
hotspot --diff): red = added cost, blue = removed.
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.
- 1mo ago First seen · 66 lines · 0 tokens per session scan A c3b66bcdd461
perf-profile is a skill published in the GitHub repository OutlineDriven/odin-codex-plugin (14 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,077 tokens. 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
fix
Polymorphic iterative repair loop — accept a verifier failure, structured findings (review/resolve/triage-issue), or a bug description; modify→verify→keep on green, auto-revert on guard regression, until clean or iteration cap. Use when the user says "fix", "make it pass", or "apply the findings", or hands an artifact…
ast-grep
Code search, analysis, and refactoring using ast-grep (sg). Use for AST-based code modifications, structural search, and linting.
qa
Conversational QA mode — user reports bugs in plain language, agent clarifies minimally, files GitHub issues that survive refactors. Trigger on "QA", "QA session", or ad-hoc bug reporting without a fixed deliverable shape. Distinct from branch-scoped and PR-scoped review.
triage-issue
Investigate a reported bug to root cause, then emit a TDD-shaped fix plan as an issue artifact. Trigger when the user reports a bug, says "triage", asks for issue investigation, or wants a fix plan before code changes.
gh-fix-ci
Inspect GitHub PR checks with gh, pull failing GitHub Actions logs, summarize the failure, then plan and implement the fix after user approval. Use when the user asks to debug or fix failing PR CI on GitHub Actions; external checks (Buildkite, etc.) are reported as URLs only.
debug
Hypothesis-driven defect isolation — stack-trace forensics, breakpoint strategy, state inspection, and root-cause confirmation via minimal repro. Use when a defect surfaces (test failure, crash, exception, wrong output, intermittent flake) and the cause is not immediately obvious from the change set.