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 cost-performance-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/cost-performance-scorer)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/cost-performance-scorer"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/cost-performance-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/cost-performance-scorer"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/cost-performance-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 | $0.00155 | $0.02219 |
| Opus 5.5 | $0.00062 | $0.00888 |
| Sonnet 5.5 | $0.00031 | $0.00444 |
| Haiku 4.5 | $0.00015 | $0.00222 |
Grade A, and why
cost-performance-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 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cost-Performance Scorer
Overview
Routing strategies are only as good as the data that informs them. Selective intelligence works only if you can measure it, and the book names two metrics that matter:
- Cost per successful completion. Cost per task completed correctly, not cost per token. A cheap model that fails 40% of the time is not cheaper than an expensive model that succeeds on the first attempt — the wasted spend on failures is amortized over the successes.
- Quality parity threshold. The minimum acceptable quality per node type. The AlertClassifier may need 0.99 (a wrong validation is worse than none); the QueryAnalyst may tolerate 0.90 because downstream nodes recover from misclassification.
The book's harness wraps every node and logs a NodeInvocation per call
(Example 8-3). Success is judged per node — matching a human-labeled severity
for the AlertClassifier, an SRE accepting the recommendation for
PredictionSynthesis. Each candidate model is scored against a per-node
evaluation set with domain failure weights (Example 8-4): a P1 alert
misclassified as P3 is 10x worse than the reverse, an asymmetry a generic
accuracy metric cannot express. Kakao's AI Shopping Mate is the worked anchor:
restructuring GPT-4o-everywhere into a workflow graph of fine-tuned 27-32B
models moved format adherence from 0.655 to 0.987 and accuracy from 0.578 to
0.890, with the smaller models preferred over GPT-4o in 63% of 2,100 turns.
When to Use
- You have run production traffic through a routed pipeline and need to know which node is over- or under-provisioned.
- You are choosing a specialist SLM and must evaluate it on your data, not MMLU.
- A cheap node looks cheap per token but you suspect its failure rate erases the savings.
- You need a per-node quality gate before promoting a routing change.
Phrases that should invoke this skill: "cost per successful completion", "is this model actually cheaper", "per-node evaluation set", "quality parity threshold", "score the routing policy", "weighted error rate".
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 · 162 lines · 155 tokens per session scan A dc6348e4aa85
cost-performance-scorer is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (28 stars, last pushed 3mo ago), licensed MIT. It adds 155 tokens to every session and 2,219 once invoked, about $0.0006 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.
cost-performance-scorer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| cost-performance-scorerAnthonyAlcaraz/agentic-graph-rag-skills | Skill | 28 | 3mo ago | MIT | A | 155 | Claude Code, Codex |
| graphifyGraphify-Labs/graphify | Skill | 143k | yesterday | Apache-2.0 | B | 76 | Claude Code, Codex |
| hugegraph-data-importerapache/hugegraph-ai | Skill | 145 | yesterday | Apache-2.0 | A | 62 | Codex |
| hugegraph-schema-designerapache/hugegraph-ai | Skill | 145 | yesterday | Apache-2.0 | A | 51 | Codex |
| hugegraph-query-analystapache/hugegraph-ai | Skill | 145 | yesterday | Apache-2.0 | A | 49 | Codex |
| lemmalogJordyZomer/lemmalog | Skill | 346 | 1mo ago | MIT | A | 105 | Claude Code, Codex |
| jurisd-researchrussellbrenner/jurisd | Skill | 43 | 2d ago | Apache-2.0 | A | 133 | Claude Code, Codex |