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 jellydn/my-ai-tools --skill plannotator-setup-goalgit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/plannotator-setup-goal)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/plannotator-setup-goal"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/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/jellydn/my-ai-tools/plannotator-setup-goal"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/plannotator-setup-goal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00021 | $0.01463 |
| Opus 5.5 | $0.00008 | $0.00585 |
| Sonnet 5.5 | $0.00004 | $0.00293 |
| Haiku 4.5 | $0.00002 | $0.00146 |
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 20d 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 — 149 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 goals/<slug>/ once the slug is clear. Keep working JSON files and final documents there. JSON preserves provenance and iteration state; markdown is the human-readable authoritative goal package.
Browser session patience: After launching an interview or facts command, wait until the user submits, dismisses, or asks you to stop. Do not close, kill, restart, refresh, or open a second session because the UI is idle. If a rerun is needed, wait for the previous session to end, update its working JSON file, and launch from that file.
For a vague goal with interdependent decisions, suggest an optional one-at-a-time grilling pass. Run it when requested, with a recommended answer for each question. Fold its resolved decisions into the bundle below; skip the bundle if grilling fully resolves scope.
2. Interview Bundle
Build a compact bundle of questions that can derive every outcome fact. Infer answers already clear from the request, conversation, or codebase. Only ask where the user's judgment is needed. Prefer fewer, higher-leverage questions over exhaustive confirmation.
- What the feature/change is
- Who it's for
- What problem it solves
- What behavior changes
- What success looks like
- What's in and out of scope (The most important area to determine facts)
- What edge cases to consider
- What constraints or precedent apply
If a question can be answered by exploring the codebase, explore the codebase instead of asking.
Write goals/<slug>/interview.json before showing it to the user:
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.
- 20d ago Changed · +56 lines 7cd0c1f6e4fb
- 1mo ago First seen · 93 lines · 21 tokens per session scan A 7ff681d84424
plannotator-setup-goal is a skill published in the GitHub repository jellydn/my-ai-tools (124 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 1,463 once invoked, about $0.0001 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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