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 MidnightDarling/collate --skill kaozhenggit clone --depth 1 https://github.com/MidnightDarling/collateWrote 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/midnightdarling/collate/kaozheng)<a href="https://agentmods.dev/skills/midnightdarling/collate/kaozheng"><img src="https://agentmods.dev/badge/skills/midnightdarling/collate/kaozheng.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.00588 |
| Opus 5 | $0.00020 | $0.00294 |
| Sonnet 5 | $0.00008 | $0.00118 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
kaozheng 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 8d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
kaozheng
The work this skill is for
This is not typo-catching. That is proofread.
kaozheng reads in the Qian-Jia evidential tradition: not asking whether the
paper sounds plausible, but whether its evidentiary bridge bears weight.
The source may be real and the bridge still false. A citation may be accurate
and still be pressed into service beyond what it can support.
What it audits
For a post-OCR, post-proofread paper, test:
- whether the claim is actually supported by the data cited
- whether quotations are complete enough to preserve original sense
- whether cited authorities are first-rank, second-rank, or hearsay
- whether the warrant is visible and defensible
- whether the paper leans on a solitary witness (
孤证不立)
Input
Target: $ARGUMENTS
- workspace directory → read
<ws>/final.md .mdfile → read it directly- empty → ask what to read
If only raw.md exists, note that the text has not yet been proofread and mark
the report accordingly.
Method
Read the argument in layers:
- Extract the paper's main claims.
- Pair each claim with its offered evidence.
- Identify the warrant that connects the two.
- Audit quotations and citation framing:
- what is quoted
- what is omitted
- what rank the source holds
- whether a footnote quietly carries more force than the body text
- Flag cardinal errors first; breadth comes second.
The framework is a scale, not a blade. Do not deform the paper merely to force it into Toulmin language.
Output
- workspace mode →
<workspace>/analysis/{stem}_kaozheng.md - single-file mode →
analysis/{stem}_kaozheng.md
Report structure:
- Argument skeleton — claim / data / warrant / backing / qualifier / rebuttal
- Citation audit table — original fragment → suspected source → verifiable? → truncated?
- Cardinal errors — one to three, ranked by severity, with repair suggestions
- Suspicious but unverified — handed off for further checking
Guardrails
- Never modify the source text.
- Do not confuse disagreement with disproof.
- One decisive structural error outweighs twenty decorative observations.
- If verification is impossible from available material, mark it as unverified rather than pretending certainty.
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.
- 8d ago First seen · 75 lines · 0 tokens per session scan A d9f43fac0a35
kaozheng is a skill published in the GitHub repository MidnightDarling/collate (7 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 588 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 2026-08-31.
Other skills, from other repositories
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
parallel-feature-development
Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system…
cost-optimization
Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
istio-traffic-management
Configure Istio traffic management including routing, load balancing, circuit breakers, and canary deployments. Use when implementing service mesh traffic policies, progressive delivery, or resilience patterns.