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 agentmods add skills/promptpartner/agentsmith/verifynpx skills add PromptPartner/agentsmith --skill verifygit clone --depth 1 https://github.com/PromptPartner/agentsmithWhat 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 | $0.00069 | $0.00540 |
| Opus 5 | $0.00034 | $0.00270 |
| Sonnet 5 | $0.00014 | $0.00108 |
| Haiku 4.5 | $0.00007 | $0.00054 |
Grade A, and why
verify 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 2d 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.
What it actually says
Verify — evidence before "done"
"Verified" is not a feeling; it's output you can point at. Never claim passing without showing it (R5 + verification-before-completion).
When this fires
"verify" / "is this done / shippable / ready to merge?" / just before a commit or PR.
Runtime neutrality
The verification runner and evidence standard are client-neutral. Never infer the active agent
from this skill's install path: .agents/skills is the shared Agent Skills location. Use canonical
AGENTS.md when an instruction is relevant.
Fast path — if the Agentsmith CLI is available
- Run
agentsmith verify(or the installed-project shim.agentsmith/agentsmith verifyon macOS/Linux and.agentsmith\\agentsmith.cmd verifyon Windows). It runs every phase in.harness/verify.confand stops at the first failure).--listshows the phases;--only <tag>iterates just one. - On a failure: read the label + command it printed, explain in plain language what broke, and
point at that phase's line in
.harness/verify.confto fix or refine. - Report the actual pass/fail output — not a summary of intent.
Fallback — no runner or no conf
- Say so plainly, and look at
.harness/verify.conf.examplefor the intended phases. - Run the obvious checks for this project directly and show their output: build, test, lint, link/render check, a dry-run. For non-code work, "verify" = open the artifact and confirm it renders / the numbers reconcile / the links resolve.
- If nothing is wired yet, propose the phases this project needs and offer to write
.harness/verify.conf— but still run the checks by hand this time.
Report
State each check and its evidence: "build ok, 42 tests green, lint clean" — with the output, not "should pass". Anything skipped is "deferred: reason", never silence.
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.
- 2d ago First seen · 40 lines · 69 tokens per session scan A e5431963c657
verify is a skill published in the GitHub repository PromptPartner/agentsmith (188 stars, last pushed 2d ago), licensed MIT. It adds 69 tokens to every session and 540 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
ai-infrastructure-ollama
Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint.
ai-infrastructure-replicate
Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training.
ai-infrastructure-together-ai
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and OpenAI-compatible endpoints.
ai-observability-langfuse
LLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices.
ai-orchestration-langchain
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.
ai-orchestration-llamaindex
LlamaIndex.TS data framework for RAG, indexing, retrieval, query engines, chat engines, and agentic workflows in TypeScript.