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 vasilyu1983/AI-Agents-public --skill foundations-theory-of-constraintsgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/foundations-theory-of-constraints)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-theory-of-constraints"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-theory-of-constraints/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/vasilyu1983/ai-agents-public/foundations-theory-of-constraints"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-theory-of-constraints.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.06270 |
| Opus 5 | $0.00019 | $0.03135 |
| Sonnet 5 | $0.00008 | $0.01254 |
| Haiku 4.5 | $0.00004 | $0.00627 |
Grade A, and why
foundations-theory-of-constraints 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Theory of Constraints Foundations
11 canonical Theory of Constraints primitives for diagnosing and exploiting system constraints. Primitives are domain-agnostic: the same Five Focusing Steps that fix a production line apply to a software delivery pipeline, a product roadmap, or a consulting engagement. Each primitive solves a specific class of throughput-limiting problem.
When to Apply
Apply theory-of-constraints when:
- One bottleneck demonstrably gates total system throughput (the system has a constraint, not many)
- Roadmap or capacity-allocation under a hard limited resource (eng-weeks, GPU-hours, account-managers)
- Funnel debug where a single step blocks downstream conversion
- Policy constraint suspected (a rule, not a physical limit, is what's holding throughput)
- Subordination question — "should other steps slow down to match the bottleneck?"
- Post-AI adoption reassessment — when AI coding tools improve individual velocity but delivery metrics (lead time, deployment frequency, change failure rate) stay flat, re-run 5FS; the constraint has likely shifted downstream to code review, verification, or integration (DORA 2025, n≈5,000; corroborated by IT Revolution 2026 and Logilica 2025)
- LLM / agent-pipeline optimization — when end-to-end latency or task throughput of a multi-step AI pipeline is not meeting targets despite adding models or workers; the constraint is usually a specific stage (LLM decode, serialized tool execution, or a guardrail/eval step), not aggregate capacity — profile per stage before scaling
Skip and use simpler alternatives when:
- System has multiple roughly equal bottlenecks — TOC's "elevate one" model misfires; use queueing networks (foundations-queueing-theory)
- Throughput question is really a feedback-control question (oscillation, instability) — use foundations-control-theory
- The "constraint" is actually a strategic choice (we want this to be the limit) — TOC is a diagnostic, not a strategy
- Bottleneck moves run-to-run (no stable system) — stabilise before applying 5 focusing steps
- T/CU ratio differences are < 20% across initiatives — ranking noise dominates the signal
- Pure capacity addition is cheap and uncontroversial — just add capacity; TOC analysis is overhead
What ships with it
19 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.
- agents/openai.yaml 380 B
- assets/templates/theory-of-constraints/01-five-focusing-steps.md 3.5 KB
- assets/templates/theory-of-constraints/02-drum-buffer-rope.md 5.2 KB
- assets/templates/theory-of-constraints/03-throughput-accounting.md 6.1 KB
- assets/templates/theory-of-constraints/04-evaporating-cloud.md 4.2 KB
- assets/templates/theory-of-constraints/05-current-reality-tree.md 3.9 KB
- assets/templates/theory-of-constraints/06-future-reality-tree.md 3.9 KB
- assets/templates/theory-of-constraints/07-prerequisite-tree.md 3.5 KB
- assets/templates/theory-of-constraints/08-transition-tree.md 3.6 KB
- assets/templates/theory-of-constraints/09-critical-chain.md 8.7 KB
- assets/templates/theory-of-constraints/10-policy-constraints.md 4.8 KB
- assets/templates/theory-of-constraints/11-thinking-processes.md 4.4 KB
- assets/templates/theory-of-constraints/README.md 4.3 KB
- data/sources.json 17 KB
- learnings.consolidated.md 609 B
- learnings.md 395 B
- references/formal-theory-map.md 4.4 KB
- references/patterns-scenarios-traps.md 7.7 KB
- references/primitives-overview.md 8.5 KB
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 · 279 lines · 38 tokens per session scan A a4330567de42
foundations-theory-of-constraints is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 38 tokens to every session and 6,270 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 2026-09-03.
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