context-benchmark

context-benchmark is a skill for Codex from TIKAZI/TIKAZ-AI-Skills. It costs 49 tokens per session (587 once invoked), scanned A, original, MIT.

A benchmark workflow for measuring how well context preparation works across fixed test cases.

In plain words
What is it for?
Comparing context-packing versions, checking whether important facts and evidence survive preparation, and testing document, image, and table handling.
Why use it?
It separates efficiency from quality and provides reproducible evidence about token use, budget compliance, retained facts, anchors, repeatability, and runtime.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Comparing context-packing versions, checking whether important facts and evidence survive preparation, and testing document, image, and table handling.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tikazi/tikaz-ai-skills/context-benchmark
Install

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.

Any agent
npx skills add TIKAZI/TIKAZ-AI-Skills --skill context-benchmark
Clone the repo
git clone --depth 1 https://github.com/TIKAZI/TIKAZ-AI-Skills

Made for: Codex.

Wrote 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.

agentmods badge for context-benchmark

README.md
[![agentmods](https://agentmods.dev/badge/skills/tikazi/tikaz-ai-skills/context-benchmark/github.svg)](https://agentmods.dev/skills/tikazi/tikaz-ai-skills/context-benchmark)
Your own site
<a href="https://agentmods.dev/skills/tikazi/tikaz-ai-skills/context-benchmark"><img src="https://agentmods.dev/badge/skills/tikazi/tikaz-ai-skills/context-benchmark/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.

agentmods 80×15 button for context-benchmark

Your own site · 80×15
<a href="https://agentmods.dev/skills/tikazi/tikaz-ai-skills/context-benchmark"><img src="https://agentmods.dev/badge/skills/tikazi/tikaz-ai-skills/context-benchmark.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 587 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.00587
Opus 5 $0.00024 $0.00293
Sonnet 5 $0.00010 $0.00117
Haiku 4.5 $0.00005 $0.00059

Measured 7d ago against content hash 067645360a95, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

context-benchmark 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 7d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

suites/context-economy/context-benchmark/SKILL.md · 46 lines

How it starts

The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context Benchmark

Designed, integrated, independently refactored, and continuously maintained by TIKAZ.

Inputs

Accept a versioned benchmark manifest, fixed source fixtures, declared budgets, protected facts, expected anchors, optional route labels, and an optional externally scored downstream-answer rubric. Use identical inputs and settings when comparing systems.

Workflow

Run a versioned manifest of independent cases and keep raw per-case results. Report efficiency and quality separately:

  • source and packed tokens;
  • final-budget compliance;
  • protected-fact recall;
  • evidence-anchor correctness;
  • deterministic repeatability;
  • preparation runtime;
  • optional externally supplied answer score.
  • document-route correctness, informative-visual recall, decorative/duplicate skip accuracy, and complex-table fidelity warnings for multimodal fixtures.
  • retrieval Recall@K, Precision@K, MRR, nDCG@K, evidence-facet coverage, and explicit insufficient-evidence fallback on independently labeled cases.
  • dynamic-selection count, selected-evidence recall and precision, facet coverage, and estimated selected-token reduction; never present reduction without the corresponding selection recall.

Do not hide failures inside averages. A smaller pack with lower fidelity is a regression, not a win. Do not claim superiority until the same files, questions, model/detail settings, budgets, and blind answer rubric are used. Use the shared CLI benchmark command and read ../references/benchmark-method.md when publishing results.

Output contract

Publish summary.json, metrics.json, raw cases.json, and the generated evidence card together. Keep context efficiency, exact-repeat prompt efficiency, literal fact and anchor fidelity, multimodal routing, and pending provider, vision, or downstream evidence separate; never replace them with one composite fidelity score.

Validation and fallback

Keep failed cases visible and verify manifest version, fixture identity, budgets, settings, and denominators. Estimated tokens must be labeled estimates. If provider telemetry or blind downstream scoring is unavailable, mark it Pending; do not infer superiority from local fixtures.

Read the full file on GitHub · 46 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 7d ago Changed · +2 lines 067645360a95
  2. 11d ago First seen · 44 lines · 49 tokens per session scan A f3a6416178a8

Subscribe to this mod's changes

context-benchmark is a skill published in the GitHub repository TIKAZI/TIKAZ-AI-Skills (6 stars, last pushed 8d ago), licensed MIT. It adds 49 tokens to every session and 587 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 2026-08-31.

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