Plannotator is a local browser-based review tool for coding agents that lets people annotate plans, documents, HTML artifacts, code diffs, and pull requests, then send feedback back to the agent. It is used by developers and teams reviewing work from agents across supported coding tools and version-control systems.
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 backnotprop/plannotator --skill plannotator-setup-goalgit clone --depth 1 https://github.com/backnotprop/plannotatorWrote 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/backnotprop/plannotator/plannotator-setup-goal)<a href="https://agentmods.dev/skills/backnotprop/plannotator/plannotator-setup-goal"><img src="https://agentmods.dev/badge/skills/backnotprop/plannotator/plannotator-setup-goal/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/backnotprop/plannotator/plannotator-setup-goal"><img src="https://agentmods.dev/badge/skills/backnotprop/plannotator/plannotator-setup-goal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00046 | $0.02126 |
| Opus 5.5 | $0.00018 | $0.00850 |
| Sonnet 5.5 | $0.00009 | $0.00425 |
| Haiku 4.5 | $0.00005 | $0.00213 |
Grade A, and why
plannotator-setup-goal 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup Goal
Turn an idea into a goal package at goals/<slug>/ through structured discovery, user interview, and codebase exploration.
Phases
1. Rearticulate
State back what the user wants in your own words. If the conversation already has rich context, summarize it. If the goal is bare or vague, do minimal shallow exploration of the codebase to ground your understanding. Keep it to 2-3 sentences. Wait for the user to confirm or correct before continuing.
Create the goal directory once the slug is clear:
mkdir -p goals/<slug>
Use goals/<slug>/ for both working JSON files and final docs. The JSON files are provenance and iteration state; the markdown files are the human-readable authoritative goal package.
Browser session patience rule: Plannotator goal setup is a user-driven browser session. After launching an interview or facts command, be absolutely patient and keep waiting on the user until they submit, dismiss, or explicitly ask you to stop. Do not close, kill, restart, refresh, or open a second copy just because the UI is idle or the user is taking time. Never close and reopen the session as a way to update state; if a rerun is needed after the prior session ends, update the working JSON file and launch a new command from that file.
Optional: grill first (deep, one-at-a-time interview). Before building the compact interview bundle, suggest a grilling pass whenever the goal is vague or carries many interdependent decisions — and run one whenever the user asks for it ("grill me first"). This is opt-in: for a clear, well-scoped goal, skip it and go straight to the bundle, so grilling never fights the bundle's "fewer, higher-leverage questions" philosophy. When you grill, run the protocol below verbatim, then fold the resolved decisions forward into a higher-quality interview bundle (Phase 2) — or, if grilling fully resolves scope, straight into the fact sheet (Phase 3).
What ships with it
2 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.
- 1mo ago First seen · 183 lines · 46 tokens per session scan A fa516f70415d
plannotator-setup-goal is a skill published in the GitHub repository backnotprop/plannotator (9,469 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 2,126 once invoked, about $0.0002 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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