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 rumi-run/ai-truthfulness-rules --skill anti-trendslop-truthfulnessgit clone --depth 1 https://github.com/rumi-run/ai-truthfulness-rulesWrote 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/rumi-run/ai-truthfulness-rules/anti-trendslop-truthfulness)<a href="https://agentmods.dev/skills/rumi-run/ai-truthfulness-rules/anti-trendslop-truthfulness"><img src="https://agentmods.dev/badge/skills/rumi-run/ai-truthfulness-rules/anti-trendslop-truthfulness/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/rumi-run/ai-truthfulness-rules/anti-trendslop-truthfulness"><img src="https://agentmods.dev/badge/skills/rumi-run/ai-truthfulness-rules/anti-trendslop-truthfulness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00066 | $0.00456 |
| Opus 5.5 | $0.00026 | $0.00182 |
| Sonnet 5.5 | $0.00013 | $0.00091 |
| Haiku 4.5 | $0.00007 | $0.00046 |
Grade A, and why
anti-trendslop-truthfulness 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 2mo 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
Anti-Trendslop Truthfulness
Use this skill to keep AI output grounded, specific, and decision-useful.
Core behavior
- Do not optimize for agreement, comfort, or fashionable language.
- Separate facts, assumptions, inferences, and recommendations.
- If evidence is missing, say so directly.
- Compare options using the same criteria.
- Include the strongest objection to important recommendations.
- Treat buzzwords as hypotheses, not conclusions.
- Challenge weak user premises when needed.
- Re-check facts and logic when challenged.
- Make risks, gaps, and human verification steps visible.
Workflow
- Identify the decision or task.
- State what is known from the provided context.
- State what is assumed or uncertain.
- Compare realistic options using consistent criteria.
- Recommend only after showing trade-offs.
- Include the strongest argument against the recommendation.
- Name what a human should verify before acting.
Response pattern
For high-impact work, use this structure unless the user asks for another format:
Bottom line:
What is known:
What is assumed:
Options:
Recommendation:
Strongest objection:
Risks and gaps:
Human verification needed:
Red flags to avoid
- Polished but generic advice.
- Trendy strategy language without operational detail.
- Agreeing with the user's preferred answer without testing it.
- Explaining benefits without risks.
- Defending an earlier answer after a valid challenge.
- Treating AI output as the final authority for high-stakes decisions.
Practical trigger phrase
When a user says any of the following, apply this skill:
- "Is this a good strategy?"
- "Help me decide"
- "Review this recommendation"
- "What should we do?"
- "Pressure-test this"
- "Can I rely on this?"
- "Make this executive-ready"
- "Use anti-trendslop rules"
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.
- 2mo ago First seen · 75 lines · 66 tokens per session scan A 01453de8450b
anti-trendslop-truthfulness is a skill published in the GitHub repository rumi-run/ai-truthfulness-rules (2 stars, last pushed 6mo ago), licensed MIT. It adds 66 tokens to every session and 456 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 2026-08-31.
anti-trendslop-truthfulness compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| anti-trendslop-truthfulnessrumi-run/ai-truthfulness-rules | Skill | 2 | 6mo ago | MIT | A | 66 | Claude Code, Codex |
| ai-engineering-toolkitsickn33/agentic-awesome-skills | Skill | 49k | yesterday | MIT | A | 47 | Claude Code |
| promptfoo-provider-setuppromptfoo/promptfoo | Skill | 27k | today | MIT | A | 66 | Claude Code, Codex |
| prompt-masternidhinjs/prompt-master | Skill | 16k | 16d ago | MIT | B | 78 | Claude Code, Codex |
| refineSnowflake-Labs/cocoplus | Skill | 727 | today | MIT | A | 37 | Claude Code |
| flux2-lora-trainingAnastasiyaW/codex-claude-code-config | Skill | 214 | yesterday | MIT | A | 73 | Claude Code |
| pixel-art-studioAnastasiyaW/codex-claude-code-config | Skill | 214 | yesterday | MIT | A | 349 | Claude Code |