TeaQL Agent Kit is a development harness that places an executable domain model between a coding agent's requirements and its implementation. It evaluates and repairs that model, generates typed API boundaries, and guides the agent toward implementing and verifying software within those constraints. The catalogue entries provide the kit's agent, skill, and instruction workflows.
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 teaql/teaql-agent-kit --skill build-teaql-appgit clone --depth 1 https://github.com/teaql/teaql-agent-kitWrote 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/teaql/teaql-agent-kit/build-teaql-app)<a href="https://agentmods.dev/skills/teaql/teaql-agent-kit/build-teaql-app"><img src="https://agentmods.dev/badge/skills/teaql/teaql-agent-kit/build-teaql-app/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/teaql/teaql-agent-kit/build-teaql-app"><img src="https://agentmods.dev/badge/skills/teaql/teaql-agent-kit/build-teaql-app.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 45 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 228 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Excessive Agency · line 198 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00112 | $0.04115 |
| Opus 5.5 | $0.00045 | $0.01646 |
| Sonnet 5.5 | $0.00022 | $0.00823 |
| Haiku 4.5 | $0.00011 | $0.00411 |
Grade A, and why
build-teaql-app 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 1mo 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 — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build TeaQL App
Turn a business requirement into a KSML contract and a verified TeaQL application. Never run model evaluation before the first complete KSML model has been written and saved.
Mandatory Workflow Order
Do not reorder these stages:
- Understand the requirement and choose the model target.
- Draft and save the first complete KSML model.
- Only after the model exists, verify the TeaQL client.
- Evaluate the saved model.
- Repair from the report and re-evaluate repeatedly.
- At zero Errors, signal Model Ready and continue without waiting.
- Generate, implement, compile, test, run, and report.
Prepare the Model Target
- Read the target repository's nearest
AGENTS.md. - Capture the original business requirement, model target path, requested
language and outputs, and runnable or testable outcome. Supported language
families are Java, Rust, Go, Swift, Python, C#/.NET, and TypeScript. A
Kotlin/JVM application uses the Java-generated library and Java runtime; do not look
for a separate Kotlin generator. Swift uses the
swift-lib-coretarget for a generated Swift package with local SQLite and TFP client support. C++, Dart, Ruby, and other unlisted smaller language ecosystems are not supported. - Keep a compact evidence ledger while working. Record commands, evaluation counts, generated guides, assist calls, policy checks, tests, and artifact paths as they occur.
Do not load a full KSML rule catalog before modeling. Use
references/golden-example.xml as the grammar
example and adapt its structure—not its pet-clinic concepts—to the domain.
The golden example exists only to demonstrate KSML syntax. Never carry over its
domain names (clinic, pet, appointment, species, microchipped) or its
organizational structure into a different business domain.
Model First
Create and save a complete KSML model before running any TeaQL command. Do not evaluate an absent, empty, or placeholder model target.
What ships with it
11 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.
- agents/ERROR-FIX.md 1.2 KB
- agents/openai.yaml 669 B
- references/golden-example.xml 2.0 KB
- references/incremental-editing.md 1.4 KB
- references/multi-file-golden-example/main.xml 273 B
- references/multi-file-golden-example/operations.xml 1.0 KB
- references/multi-file-golden-example/organization.xml 437 B
- references/multi-file-golden-example/README.md 861 B
- references/source-fallback.md 988 B
- references/toolchains.md 7.1 KB
- references/work-complete.md 3.3 KB
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.
- 1mo ago Changed · +10 lines b35116d0115c
- 1mo ago Changed · +22 lines 9b09acf06c6c
- 1mo ago First seen · 305 lines · 112 tokens per session scan A 2d608444c26f
build-teaql-app is a skill published in the GitHub repository teaql/teaql-agent-kit (2,818 stars, last pushed 24d ago), licensed MIT. It adds 112 tokens to every session and 4,115 once invoked, about $0.0004 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 2026-08-30.
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