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 Lingtai-AI/lingtai --skill lingtai-issue-reportgit clone --depth 1 https://github.com/Lingtai-AI/lingtaiWrote 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/lingtai-ai/lingtai/lingtai-issue-report)<a href="https://agentmods.dev/skills/lingtai-ai/lingtai/lingtai-issue-report"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/lingtai-issue-report/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/lingtai-ai/lingtai/lingtai-issue-report"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/lingtai-issue-report.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 | $0.00075 | $0.00790 |
| Opus 5 | $0.00037 | $0.00395 |
| Sonnet 5 | $0.00015 | $0.00158 |
| Haiku 4.5 | $0.00007 | $0.00079 |
Grade A, and why
lingtai-issue-report 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 6d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reporting LingTai Issues
This is a reference router. You use the LingTai system as a real user all day — its skills, capabilities, and procedures — so you are uniquely positioned to notice problems humans miss. When you notice something wrong, surface it. This skill is the protocol: enter through this router, pick the one nested reference matching where you are in the report lifecycle, and read that leaf for the full procedure.
The non-negotiables (read before anything else)
Two rules hold across every path and every leaf:
- Human consent is required, always. You never open a GitHub issue without an explicit "yes" from the human. The human is the accountable owner of what gets filed under their name. Even if
ghis authenticated and you have a shell, per-issue consent is non-negotiable. If they decline, drop it — no nagging, no auto-retry. - Secrets never enter a report. No tokens, keys, or passwords in the body, in chat, in logs, or in files. A human-provided
GH_TOKENstays in the env of the single command that needs it. Redact before you quote.
The nested references elaborate these; they never weaken them.
Nested reference catalog
- name: issue-report-evidence-checklist
location: reference/evidence-checklist/SKILL.md
description: When an observation is worth reporting (and when it isn't), what evidence to capture verbatim, and how to keep secrets out of the report.
- name: issue-report-report-template
location: reference/report-template/SKILL.md
description: The report skeleton — subject/title, the structured body sections, sending it via mail to your parent and the human, and which repo to target.
- name: issue-report-filing-flow
location: reference/filing-flow/SKILL.md
description: The filing decision — human consent boundary, the read-only gh probe, Path A (direct gh filing) and Path B (paste-ready handoff), token hygiene, and proactive surfacing.
Routing table
You will usually move through all three in order, but read one leaf at a time — don't pull the whole protocol into context at once.
What ships with it
3 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.
- 6d ago Changed · -34 tokens per session 48bfd9dd769a
- 13d ago First seen · 48 lines · 109 tokens per session scan A d760ba9b277e
lingtai-issue-report is a skill published in the GitHub repository Lingtai-AI/lingtai (677 stars, last pushed yesterday), licensed Apache-2.0. It adds 75 tokens to every session and 790 once invoked, about $0.0004 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 2026-08-30.
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