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 Antonod1988/what-are-the-bottlenecks --skill bottleneckgit clone --depth 1 https://github.com/Antonod1988/what-are-the-bottlenecksWrote 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/antonod1988/what-are-the-bottlenecks/bottleneck)<a href="https://agentmods.dev/skills/antonod1988/what-are-the-bottlenecks/bottleneck"><img src="https://agentmods.dev/badge/skills/antonod1988/what-are-the-bottlenecks/bottleneck/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/antonod1988/what-are-the-bottlenecks/bottleneck"><img src="https://agentmods.dev/badge/skills/antonod1988/what-are-the-bottlenecks/bottleneck.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 | $0.00121 | $0.01727 |
| Opus 5.5 | $0.00048 | $0.00691 |
| Sonnet 5.5 | $0.00024 | $0.00345 |
| Haiku 4.5 | $0.00012 | $0.00173 |
Grade A, and why
bottleneck 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 2mo 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What Are the Bottlenecks?
Find the limiting constraint, not the longest list of defects. Move from system goal to critical flow to evidence, then descend only into details capable of changing the diagnosis.
Core rules
- Treat a bottleneck as a constraint on a stated goal under a stated scenario and horizon. Never call something a bottleneck in the abstract.
- Separate observed facts, user claims, inferences, assumptions, and unknowns.
- Distinguish
current constraint,emerging constraint,risk,symptom, andunknown. Not every flaw limits throughput or outcomes. - Prefer repository evidence, measurements, traces, user behavior, budgets, queues, failure history, and dependency limits over generic best-practice checklists.
- Audit the measurement system before trusting it. Contradictory, missing, sampled, or ambiguously labeled observations can make diagnostic capability the immediate constraint.
- Inspect broadly before drilling down. Do not begin with code quality when demand, distribution, compliance, data, ownership, or unit economics may be the real constraint.
- Rank bottlenecks relative to each other. A top-ten list with ten critical items is not a diagnosis.
- Recommend the cheapest decisive test before an expensive redesign.
- Do not optimize a non-constraint. Explain which downstream constraint is likely to appear if the current one is relieved.
- Preserve read-only scope unless the user asks for changes. Do not run load tests, paid calls, destructive probes, or production mutations without authorization.
- Present concise conclusions; do not expose private chain-of-thought. Provide an evidence-backed rationale and explicit uncertainty.
Choose depth
Infer depth from the request and available evidence:
scan: One artifact or early idea. Identify up to three likely constraints and the missing evidence that could reorder them.audit: Default for a repository or mature proposal. Map the system, inspect relevant artifacts, rank up to five constraints, and propose validation experiments.deep: Use only when the user asks for exhaustive analysis or the system is high-impact. Trace cross-layer causal chains, inspect history/runtime evidence, and test competing hypotheses.
What ships with it
3 files 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.
- 2mo ago First seen · 148 lines · 121 tokens per session scan A 462e3ca87a77
bottleneck is a skill published in the GitHub repository Antonod1988/what-are-the-bottlenecks (10 stars, last pushed 2mo ago), licensed MIT. It adds 121 tokens to every session and 1,727 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-31.
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architecture-diagram
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
modular-skills
Build composable skill modules with hub-and-spoke loading. Use when token budget is tight.
plugin-review
Review plugin quality with tiered checks and dependency scoping. Use for PR and pre-release audits.
product-image-processor
Download, resize, and remove backgrounds from product images at scale. Use when the user asks to "process product images", batch-download images from the schedule, strip backgrounds, or standardize product photos.
site-history
Neighborhood context and history — adjacent uses, architectural character, landmarks, commercial activity, and planned development from an address. Use when the user asks about a site's history or surroundings, "what's around this site", neighborhood character, or nearby planned development.