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 TIKAZI/TIKAZ-Codex-Context-Economy --skill context-benchmarkgit clone --depth 1 https://github.com/TIKAZI/TIKAZ-Codex-Context-EconomyWrote 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/tikazi/tikaz-codex-context-economy/context-benchmark)<a href="https://agentmods.dev/skills/tikazi/tikaz-codex-context-economy/context-benchmark"><img src="https://agentmods.dev/badge/skills/tikazi/tikaz-codex-context-economy/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.
<a href="https://agentmods.dev/skills/tikazi/tikaz-codex-context-economy/context-benchmark"><img src="https://agentmods.dev/badge/skills/tikazi/tikaz-codex-context-economy/context-benchmark.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 | $0.00049 | $0.00587 |
| Opus 5 | $0.00024 | $0.00293 |
| Sonnet 5 | $0.00010 | $0.00117 |
| Haiku 4.5 | $0.00005 | $0.00059 |
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 8d 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.
This is a copy
100% identical to context-benchmark — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
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.
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.
- 8d ago Changed · +2 lines 067645360a95
- 12d ago First seen · 44 lines · 49 tokens per session scan A f3a6416178a8
context-benchmark is a skill published in the GitHub repository TIKAZI/TIKAZ-Codex-Context-Economy (3 stars, last pushed 5d 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. It is 100% identical to context-benchmark, differing in 0 lines, and is treated as a copy.
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