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 AnthonyAlcaraz/agentic-graph-rag-skills --skill agent-constraint-triangle-scorergit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-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/anthonyalcaraz/agentic-graph-rag-skills/agent-constraint-triangle-scorer)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/agent-constraint-triangle-scorer"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/agent-constraint-triangle-scorer/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/anthonyalcaraz/agentic-graph-rag-skills/agent-constraint-triangle-scorer"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/agent-constraint-triangle-scorer.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 | $0.00193 | $0.02230 |
| Opus 5 | $0.00097 | $0.01115 |
| Sonnet 5 | $0.00039 | $0.00446 |
| Haiku 4.5 | $0.00019 | $0.00223 |
Grade A, and why
agent-constraint-triangle-scorer 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 12d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Constraint Triangle Scorer
Overview
Ch1 names a fundamental challenge in building agents: the agent constraint triangle, "three interconnected constraints that create an inherently difficult operational problem":
- Complexity management — multistep planning and reasoning. "As tasks require more steps and deeper analysis, cognitive load increases exponentially," producing "compounding errors as the step count increases."
- Tool orchestration — translating natural language into precisely structured API calls. The named failure mode is "bloated tool sets that cover too much functionality or lead to ambiguous decision points about which tool to use." Anthropic's principle: "If a human engineer can't definitively say which tool should be used in a given situation, an AI agent can't be expected to do better."
- Context utilization — organizing a fixed context window, "the model's attention budget." Chroma's needle-in-a-haystack research names context rot: "as the number of tokens in the context window increases, the model's ability to accurately recall information from that context decreases" — "a performance gradient," not "a hard cliff."
The chapter's key point is that these "don't exist in isolation but form a system of competing trade-offs. When improving performance along one dimension, you typically create additional pressure on the others." It names three cyclic pressures:
- complexity → tools → context
- tools → context → complexity
- context → complexity → tools
The governing principle: "the smallest possible set of high-signal tokens that maximizes the likelihood of some desired outcome" — minimal-but-sufficient complexity decomposition, minimal-but-complete tool coverage, and minimal-but-adequate context retention.
This skill scores a configuration against that triangle. The scoring curves are
transparent heuristics that embody the chapter's qualitative claims (exponential
complexity load, ambiguity-dominated tool pressure, context-rot gradient); they
are not chapter-cited benchmarks, and the production seam is documented at each
lib function.
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.
- 12d ago First seen · 160 lines · 193 tokens per session scan A c60f129cfe86
agent-constraint-triangle-scorer is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 193 tokens to every session and 2,230 once invoked, about $0.0010 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 2026-08-31.
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