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 human-avatar/skills-for-humanity --skill s4h-investigation-source-tracegit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-investigation-source-trace)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-investigation-source-trace"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-investigation-source-trace/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/human-avatar/skills-for-humanity/s4h-investigation-source-trace"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-investigation-source-trace.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 | $0.00095 | $0.01628 |
| Opus 5 | $0.00048 | $0.00814 |
| Sonnet 5 | $0.00019 | $0.00326 |
| Haiku 4.5 | $0.00010 | $0.00163 |
Grade A, and why
s4h-investigation-source-trace 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 9d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigation: Source Trace
Most claims arrive pre-laundered. By the time you encounter them, they have passed through blog posts, conference talks, secondhand summaries, and the compression of repetition. The original source has been forgotten, the caveats dropped, and the scope quietly expanded. Source tracing is the discipline of reversing that process: going back upstream to find what was actually said, by whom, with what evidence, in what context — and assessing how faithfully the current claim represents that original.
Your Process
Step 1: Capture the Claim as Received Write out the claim exactly as you have encountered it. Note: who is asserting it now, in what context, and what confidence level they are expressing ("studies show" vs. "I've heard" vs. "it's well established that").
Framing check: Confirm the specific claim before continuing. State what you've identified — the actual claim being traced and the current form it takes — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the claim and its current form]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different claim than read; incorporate the correction before proceeding
Step 2: Identify Potential Origin Points Work backward. What is the earliest source you can find for this claim? Strategies:
- Search for the earliest publication or statement you can locate
- Look for the claim's attributed origin (is someone cited? Is that citation accurate?)
- Check whether the claim appears in academic literature, a specific study, a book, a news report, or originated as someone's opinion
- Note any "citation laundering" — claims that cite a secondary source which itself doesn't cite a primary source
Step 3: Evaluate the Origin Source Once you have the earliest traceable source, assess it:
- Who made the claim? What are their credentials, institutional affiliation, and potential biases?
- When was it made? What was the context at the time?
- What was the original evidence? Was it a study (what kind?), a data analysis, an observation, an opinion, a hypothesis?
- What caveats did the original source include that have since been dropped?
- What did the original claim actually assert — is it narrower, broader, or different from the current form?
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.
- 9d ago First seen · 135 lines · 95 tokens per session scan A 4860ac9017e8
s4h-investigation-source-trace is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 1,628 once invoked, about $0.0005 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 2026-09-03.
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