Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/athola/claude-night-marketnpx agentmods add skills/athola/claude-night-market/night-market-research-methodologyWrote 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/athola/claude-night-market/night-market-research-methodology)<a href="https://agentmods.dev/skills/athola/claude-night-market/night-market-research-methodology"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/night-market-research-methodology/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/athola/claude-night-market/night-market-research-methodology"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/night-market-research-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 148 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00045 | $0.03837 |
| Opus 5 | $0.00023 | $0.01919 |
| Sonnet 5 | $0.00009 | $0.00767 |
| Haiku 4.5 | $0.00005 | $0.00384 |
Grade A, and why
night-market-research-methodology 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 13d 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 — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Night Market Research Methodology
The discipline that turns a hunch into an accepted result in this repo. An "accepted result" is a change that survived the evidence bar and landed through change control as a rule, a skill module, a config gate, or an ADR. Everything else is either a local working note or a documented retirement. This skill covers the full path: score the idea, experiment behind a default-off flag, meet the evidence bar, land the durable artifact, or retire the idea on the record.
The evidence bar
A claim graduates from hunch to result only when it passes all four tests.
-
One mechanism explains all observations, including negatives. If the hypothesis explains the three failing cases but not why the fourth case passed, it is incomplete. Keep digging until a single mechanism accounts for everything you saw.
-
Predict numbers before running. Write down the expected measurement first, then measure. In-repo anchor: the forced-eval harness labels expected activations in
prototypes/forced-eval/activation_cases.jsonbefore any run, then compares baseline against treatment with a McNemar paired test (a significance test for paired binary outcomes). -
Survive assigned adversarial refutation. Assign a reviewer or agent whose explicit job is to break the claim. Use
Skill(attune:war-room)for hard-to-reverse decisions andSkill(imbue:rigorous-reasoning)to counter agreement bias. A claim nobody tried to break is unproven. -
Never let the generator judge itself. The agent that produced the work must not be its sole verifier. See
plugins/imbue/skills/proof-of-work/modules/independent-verification.md. Prefer executable checks over an LLM judge, and prove the check can fail before trusting it (Guards 2 and 3 inplugins/imbue/skills/proof-of-work/modules/verifier-integrity.md).
Corollary from verifier-integrity: a green check proves the code satisfies the spec as written. It cannot prove the spec says what you meant, and it proves nothing if the check cannot fail. Validate the spec separately from the code, and mutation-test the check itself.
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.
- 13d ago First seen · 363 lines · 45 tokens per session scan A 870d7484a2b8
night-market-research-methodology is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed yesterday), licensed MIT. It adds 45 tokens to every session and 3,837 once invoked, about $0.0002 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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