Borrowing it
Nothing to install: this file belongs to guillermoscript/lms-front. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/guillermoscript/lms-front/master/.agents/skills/issue-plan/SKILL.mdgit clone --depth 1 https://github.com/guillermoscript/lms-frontWrote 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/guillermoscript/lms-front/issue-plan)<a href="https://agentmods.dev/skills/guillermoscript/lms-front/issue-plan"><img src="https://agentmods.dev/badge/skills/guillermoscript/lms-front/issue-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/guillermoscript/lms-front/issue-plan"><img src="https://agentmods.dev/badge/skills/guillermoscript/lms-front/issue-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 | $0.00090 | $0.01386 |
| Opus 5.5 | $0.00036 | $0.00554 |
| Sonnet 5.5 | $0.00018 | $0.00277 |
| Haiku 4.5 | $0.00009 | $0.00139 |
Grade A, and why
issue-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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Plan — from issue URL to a posted plan of attack
The output of this skill is a plan someone else could execute: the issue comment is the public record of the approach, and it later seeds the PR body. Write it for the teammates who will read it on GitHub, not for the user in this chat. No code gets written or changed here — this skill is read-only on the codebase and write-only on the issue.
Repo facts (repo, gh user, board config) come from the gh-repo-config
skill; board moves go through gh-board. Neither being configured
blocks a plan — unconfigured pieces are skipped and noted.
1. Read the issue
Accept a full URL, #N, or a bare number. Gather everything in one pass:
gh issue view <N> --json number,title,body,labels,milestone,assignees,comments,url
Read the comments too — later comments often amend or overrule the original body. If the issue references other issues/PRs (an epic parent, a "relates to"), skim those for constraints. If the issue is genuinely ambiguous about what to build (not how — that's this skill's job), ask the user before going further.
If the issue is already assigned to someone else or already In Progress on the board, stop and ask the user before taking it over.
2. Survey the code
Before forming an opinion, survey the code the issue touches — read-only;
Explore subagents are good for this. Check the repo's CLAUDE.md/AGENTS.md
for documented gotchas (multi-tenancy, auth boundaries, basePath quirks,
dual databases, feature flags — whatever that repo calls out). The posture
is survey-first: the plan must be written well enough that someone with
zero session context could follow it.
3. Author the plan
A short plan, four parts:
- Root cause / current behavior — what the code does today and why that's the issue.
- Approach — what will change, in which files.
- Risks / blast radius — what else touches this code, weighing the gotchas found in the survey.
- Test plan — which checks will prove it works (commands + manual steps), using the repo's discovered build/lint/test commands.
What ships with it
1 file 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 · 118 lines · 90 tokens per session scan A da3bc66072e8
issue-plan is a skill published in the GitHub repository guillermoscript/lms-front (40 stars, last pushed today), licensed MIT. It adds 90 tokens to every session and 1,386 once invoked, about $0.0004 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.
Other skills, from other repositories
summarize-activity
Summarize engineering activity in classroomio/classroomio between two dates — merged PRs, still-open PRs opened in that window, and their demo videos — into a markdown report. Use when the user asks what shipped, what happened, or for a summary/recap/digest of engineering work over a date range.
linear-tickets
Create or update high-quality Linear tickets while preserving the originating report, diagnostic links, attachments, and reporter/requester attribution. Read this whenever someone asks to create, recreate, or update a Linear issue.
mission-control
Interact with Mission Control — AI agent orchestration dashboard. Use when registering agents, managing tasks, syncing skills, or querying agent/task status via MC APIs.
linear-app-setup
Create and configure a Linear agent app so the assistant can manage issues, projects, and workflows under its own identity.
agent-phase-closeout
A Chinese-language process for closing out a completed work stage and recording its state. It summarizes finished work, preserves decisions, freezes completed interfaces, and writes a resume checkpoint.
orchestrate
Pipeline orchestration: dispatch the highest-priority ready tasks/work units to agents, manage capacity, and coordinate the Todo to Done flow. Invoked as /agiflow:orchestrate. Uses listtasks, listactivetasksbyorg, listmembers, updatetask, getworkunitprogress.