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 agentmods add skills/masonegger/bpe-claude-code-plugin/gh-issuenpx skills add MasonEgger/bpe-claude-code-plugin --skill gh-issuegit clone --depth 1 https://github.com/MasonEgger/bpe-claude-code-pluginWhat 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 | $0.00019 | $0.00423 |
| Opus 5 | $0.00010 | $0.00211 |
| Sonnet 5 | $0.00004 | $0.00085 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
gh-issue 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 3d 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.
What it actually says
GitHub Issue Command
-
Retrieve issue $ARGUMENTS from GitHub using
gh issue view. -
Review the issue title, body, labels, and any linked discussion.
-
Review the codebase to understand the area of code the issue touches.
-
Validate the issue:
- Run any relevant tests to confirm the problem exists.
- If no tests exist for this area, write tests that demonstrate the issue.
- If you cannot reproduce the issue, inform the user with a detailed explanation and stop.
-
Assess the issue's detail level to decide the next step:
Route to brainstorm (interactive spec development) when the issue is:
- A feature request with only a high-level description
- Missing acceptance criteria or specific requirements
- Ambiguous about scope, approach, or expected behavior
- Broad enough that multiple implementation strategies exist
Route to plan (implementation roadmap) when the issue is:
- A well-defined bug with clear reproduction steps
- A feature request with specific acceptance criteria
- Scoped narrowly enough that the implementation path is clear
- Already has a linked spec or detailed technical description
-
Tell the user which route you chose and why, then execute:
- Brainstorm route: Begin the interactive Q&A process to develop a spec. Use the issue details as the starting idea. Pre-fill any answers you can derive from the issue itself, but still ask the user to confirm and fill gaps. Save the result as spec.md.
- Plan route: Use the issue details as the spec input. Generate the implementation plan (plan.md + todo.md) following the standard plan format, using TDD Feature steps or non-TDD Task steps as the work warrants.
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.
- 3d ago First seen · 36 lines · 19 tokens per session scan A 4182a040a328
gh-issue is a skill published in the GitHub repository MasonEgger/bpe-claude-code-plugin (7 stars, last pushed 11d ago), licensed MIT. It adds 19 tokens to every session and 423 once invoked, about $0.0001 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…