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 evidence-checklistgit 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/evidence-checklist)<a href="https://agentmods.dev/skills/lingtai-ai/lingtai/evidence-checklist"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/evidence-checklist/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/evidence-checklist"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/evidence-checklist.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 | $0.00054 | $0.01094 |
| Opus 5 | $0.00027 | $0.00547 |
| Sonnet 5 | $0.00011 | $0.00219 |
| Haiku 4.5 | $0.00005 | $0.00109 |
Grade A, and why
issue-report-evidence-checklist 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 12d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue report — evidence checklist
This is a nested lingtai-issue-report reference: whether an observation deserves a report, and what evidence to capture (without leaking secrets) before you draft one. Read it while the problem is still fresh — you can rarely reconstruct exact tool output later.
The kinds of problem you are placed to catch: a doc URL that 404s, a capability that errors silently, a skill whose claims don't match what the API actually returns, a preset that ships a broken default, a procedure step that contradicts another.
When to invoke the issue-report protocol
You should reach for lingtai-issue-report whenever you spot any of:
- Stale documentation — a skill claims a model/endpoint/feature that no longer exists or behaves differently than described
- Broken URLs — a doc link, console URL, or example URL returns 404 or the wrong page
- Silent failure — a capability accepts your call but returns nothing useful, or
setupswallows an error and leaves you without a tool you should have - Wrong defaults — a preset, capability config, or environment variable name in the docs doesn't match what users actually have
- Missing capability — you genuinely need a tool that doesn't exist (this is rarer than the others; check carefully that you haven't missed an existing one)
- Procedure contradiction — two skills or sections of
procedures.mdgive incompatible guidance for the same situation - Reproducibly wrong output — a model/tool returns clearly wrong answers in a way that isn't just a one-off hallucination (e.g. a vision model claims it can't see an image when image_url content is present)
- Migration / rename gaps — you encounter old names that
lingtai-kernel-anatomy'sreference/changelog.mddoesn't document
You should not invoke this skill for:
- One-off LLM hallucinations or non-determinism (file a bug only if you can reproduce)
- Personal preference about wording or formatting in a doc — unless it's actually misleading
- Complaints about a model's quality on hard tasks (that's the model, not LingTai)
- Feature requests for things the system was never designed to do
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.
- 12d ago First seen · 55 lines · 54 tokens per session scan A 00f7cf60be2a
issue-report-evidence-checklist is a skill published in the GitHub repository Lingtai-AI/lingtai (677 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 1,094 once invoked, about $0.0003 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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