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-model-and-harness-updatesWrote 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-model-and-harness-updates)<a href="https://agentmods.dev/skills/athola/claude-night-market/night-market-model-and-harness-updates"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/night-market-model-and-harness-updates/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-model-and-harness-updates"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/night-market-model-and-harness-updates.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 | $0.00048 | $0.01076 |
| Opus 5 | $0.00024 | $0.00538 |
| Sonnet 5 | $0.00010 | $0.00215 |
| Haiku 4.5 | $0.00005 | $0.00108 |
Grade A, and why
night-market-model-and-harness-updates 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Night Market Model and Harness Updates
When Anthropic ships a model or Claude Code ships a version, the pins scattered through this repo rot silently. This skill runs the sweep that finds the rot, researches what actually changed, applies the updates, and records where upstream stood so the next run reports only the new delta.
The watermark is the point. Without it every audit restarts from zero
and re-derives the same answer by hand. .claude/upstream-baseline.json
holds the last recorded upstream state, and each run diffs against it.
When to run
| Trigger | Signal |
|---|---|
| Model release | A tier or model ID ships that the ledger does not record |
| Harness release | claude --version differs from the ledger |
| Scheduled check | Monthly, to catch a release nobody noticed |
The five steps
Run them in order. Each one gates the next.
# 1. Detect. Deterministic, no model in the loop.
python3 scripts/check_upstream_drift.py
# 2. Research what changed (only when step 1 reports drift).
# Release notes and model cards are mandatory sources.
# 3. Map findings onto asset classes.
# 4. Sweep the implicated classes.
# 5. Prove, then record the new watermark.
python3 scripts/check_upstream_drift.py && \
python3 scripts/check_agent_model_matrix.py
Step 5 runs before the ledger is written, never after. Recording a migration that has not passed its proof is the failure the ledger exists to prevent.
What the detector proves and what it cannot
Harness drift is fully deterministic: the installed binary reports its own version. Model drift is not. No local command enumerates the current roster, so the ledger holds the last known roster and research establishes the current one. The skill states this split rather than pretending both halves are automatic.
The detector reports four classes:
| Class | Meaning |
|---|---|
harness |
Installed version differs from the ledger |
vocabulary |
A gate's frozen set omits a value the ledger records |
dated_ids |
Dated model IDs above the recorded ratchet |
unknown_tier |
Frontmatter names a tier absent from the roster |
What ships with it
4 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.
- 12d ago First seen · 114 lines · 48 tokens per session scan A 68cca69596fe
night-market-model-and-harness-updates is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 1,076 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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