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 divinevideo/divine-mobile --skill plangit clone --depth 1 https://github.com/divinevideo/divine-mobileWrote 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/divinevideo/divine-mobile/plan)<a href="https://agentmods.dev/skills/divinevideo/divine-mobile/plan"><img src="https://agentmods.dev/badge/skills/divinevideo/divine-mobile/plan/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/divinevideo/divine-mobile/plan"><img src="https://agentmods.dev/badge/skills/divinevideo/divine-mobile/plan.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 | $0.00073 | $0.01885 |
| Opus 5.5 | $0.00029 | $0.00754 |
| Sonnet 5.5 | $0.00015 | $0.00377 |
| Haiku 4.5 | $0.00007 | $0.00188 |
Grade A, and why
plan 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Investigation & Planning Skill
Purpose
Investigate a GitHub issue thoroughly and produce a structured, actionable implementation plan. The plan follows the project's layered architecture (UI -> BLoC -> Repository -> Client) and includes files to modify, implementation steps, testing strategy, and risk assessment.
Workflow
Phase 1: Fetch Issue
Retrieve the full issue context using gh CLI and GraphQL.
# Get issue details
gh issue view <number> --json title,body,comments,labels,state,assignees
# Get issue type, hierarchy, and sub-issues
gh api graphql -f query='{ repository(owner: "divinevideo", name: "divine-mobile") {
issue(number: <NUMBER>) {
id
title
body
issueType { id name }
subIssues(first: 20) { nodes { number title state } }
parentIssue { number title }
}
} }'
Determine the issue type from:
- Title prefix:
fix:= Bug,feat:= Feature,task:= Task - GraphQL
issueTypefield - Labels (e.g.,
buglabel)
Extract key information:
- Bug: Steps to reproduce, actual vs expected result, environment, evidence
- Feature: Requirements, acceptance criteria, user stories
- Task: Description, scope, implementation notes
Phase 2: Explore Codebase
Search the codebase to understand the relevant code. Approach varies by type.
For Bugs
- Search for keywords from the bug description (error messages, screen names, widget names) using
Grep - Identify the screen/page where the bug manifests using
Globonlib/screens/andlib/widgets/ - Trace the code path from UI through BLoC to Repository to Client
- Check for related tests using
Globontest/ - If Nostr-related, use
mcp__nostr__read_kindormcp__nostr__read_nipfor protocol context
For Features
- Search for similar existing features using
GrepandGlob - Identify which architectural layers are needed (new BLoC? New repository? New package?)
- Check if related models/repositories already exist in
mobile/packages/ - Look at the router (
mobile/lib/router/) for navigation integration - If Nostr-related, use
mcp__nostr__*tools to understand relevant NIPs and event kinds
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 · 215 lines · 73 tokens per session scan A 8acc74750e0c
plan is a skill published in the GitHub repository divinevideo/divine-mobile (264 stars, last pushed today), licensed MPL-2.0. It adds 73 tokens to every session and 1,885 once invoked, about $0.0003 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 2026-09-03.
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