Context Benchmark
Measure context preparation across fixed cases. Use when token savings, hard-budget compliance, protected-fact recall, evidence anchors, determinism, runtime, or downstream answer quality must be demonstrated with reproducible evidence rather than marketing claims.
Install
$ agentstack add skill-tikazi-tikaz-ai-skills-context-benchmark ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-tikazi-tikaz-ai-skills-context-benchmark) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Context Benchmark
Designed, integrated, independently refactored, and continuously maintained by TIKAZ.
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.
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.
Publish benchmarks/results/metrics.json, the generated evidence card, and raw cases together. Keep context efficiency, exact-repeat prompt efficiency, literal fact/anchor fidelity, multimodal routing, and pending provider/vision/downstream evidence separate; never replace them with one composite fidelity score.
From the suite directory, run python scripts/tikaz_context.py benchmark --manifest benchmarks/manifest.json --output . Inspect both summary.json and cases.json; a passing case is not evidence of positive savings or semantic equivalence.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: TIKAZI
- Source: TIKAZI/TIKAZ-AI-Skills
- License: MIT
- Homepage: https://tikazi.github.io/TIKAZ-AI-Skills/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.