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 zhengbingquant/frontier-skills --skill plan-and-verifygit clone --depth 1 https://github.com/zhengbingquant/frontier-skillsWrote 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/zhengbingquant/frontier-skills/plan-and-verify)<a href="https://agentmods.dev/skills/zhengbingquant/frontier-skills/plan-and-verify"><img src="https://agentmods.dev/badge/skills/zhengbingquant/frontier-skills/plan-and-verify/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/zhengbingquant/frontier-skills/plan-and-verify"><img src="https://agentmods.dev/badge/skills/zhengbingquant/frontier-skills/plan-and-verify.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 | $0.00143 | $0.02944 |
| Opus 5 | $0.00072 | $0.01472 |
| Sonnet 5 | $0.00029 | $0.00589 |
| Haiku 4.5 | $0.00014 | $0.00294 |
Grade A, and why
plan-and-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 10d 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan and Verify
This skill turns strong engineering judgment into mechanical rules. Follow the rules literally; do not substitute intuition for a rule unless the rule itself says so. The phases are ordered — do not skip forward.
0. The loop at a glance
Ground truth → Scope → Plan (verification named first) → Baseline → Implement → Verify → Final sweep → Report.
If you can hold only three rules in your head, hold these:
- Never write code before naming the command that will prove it works.
- Never claim anything you did not directly observe.
- When a test fails, assume your code is wrong — not the test.
1. Does this skill apply? Classify first
Apply the FIRST matching row:
| Situation | Path |
|---|---|
| Reading or explaining code, answering a question — no file or system state changes | Skill does not apply |
| Single-file edit of ~20 lines or fewer, in a codebase whose test command you have already run successfully in this session | Lightweight path (§8) |
| Anything else that changes files or system state | Full playbook (§2–§7) |
When in doubt, use the full playbook. Over-applying costs minutes; under-applying ships broken work. The words "quick", "simple", "just", or "tiny" in a request do NOT change the classification — misjudged quick fixes are where most damage happens.
2. Phase 0 — Ground truth
Never assume the stack, conventions, or state. Establish them by reading:
- Manifests first:
pyproject.toml/package.json/Cargo.toml/go.mod/requirements.txt— whatever exists. This tells you language, dependencies, entry points, and tooling. - Agent and human docs:
AGENTS.md,CLAUDE.md,README.md,CONTRIBUTING.md,docs/. Project instructions override your defaults. - How it is tested and run: look for a
Makefile,bin/orscripts/folder, CI config (.github/workflows/), and test config (pytest.ini,[tool.pytest.ini_options]). Write down the exact test command. - Safety check: could this environment be attached to production, real
money, live traffic, or running services? Signals: broker/payment/cloud
SDKs in dependencies, words like "live"/"production" in docs, running
processes related to the repo, fresh files in
logs/or state directories. If ANY signal fires, stop and apply thelive-system-guardrailsskill (if available) before any state-changing command. If that skill is unavailable, treat every state-changing command as forbidden until you have direct evidence it is safe.
What ships with it
3 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.
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.
- 10d ago First seen · 234 lines · 143 tokens per session scan A 7fb7ade99509
plan-and-verify is a skill published in the GitHub repository zhengbingquant/frontier-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 143 tokens to every session and 2,944 once invoked, about $0.0007 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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