Borrowing it
Nothing to install: this file belongs to Alexander-M-Dickerson/ai-asset-pricing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/skills/write-section/SKILL.mdgit clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricingWrote 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/alexander-m-dickerson/ai-asset-pricing/write-section)<a href="https://agentmods.dev/skills/alexander-m-dickerson/ai-asset-pricing/write-section"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/write-section/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/alexander-m-dickerson/ai-asset-pricing/write-section"><img src="https://agentmods.dev/badge/skills/alexander-m-dickerson/ai-asset-pricing/write-section.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 | $0.00018 | $0.00992 |
| Opus 5 | $0.00009 | $0.00496 |
| Sonnet 5 | $0.00004 | $0.00198 |
| Haiku 4.5 | $0.00002 | $0.00099 |
Grade A, and why
write-section 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 10d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write Section Skill
When this skill is invoked, follow this structured workflow to write a new section or subsection of a paper.
Examples
/write-section introduction-- write the introduction/write-section "robustness checks"-- write a specific subsection/write-section conclusion-- write the conclusion
Input
The user specifies which section to write (by name or description) and any additional instructions.
Workflow
Step 1: Load Context
- Read the project's
CLAUDE.mdfor paper structure, key claims, terminology, and domain concepts - Read
.claude/rules/academic-writing.mdfor style rules and banned words - Read
.claude/rules/latex-conventions.mdfor LaTeX formatting, section markers, and figure/table conventions - Read the current
.texfile(s) to identify what already exists vs. what needs to be written
Step 2: Load Exemplar
Read .claude/exemplars/cochrane_writing_tips.md for foundational writing principles. If the project has its own exemplars (in literature/ or referenced in the project's CLAUDE.md), read those too.
Extract the structural pattern appropriate for the section type:
- Introduction: Punchline first, enumerate contributions, literature after your contribution
- Data / Methods: State approach upfront, define variables precisely, explain identifying assumptions
- Results: Lead with main result, give economic magnitudes, address surprises immediately
- Conclusion: 2 paragraphs maximum, enumerate contributions, no speculation
- Abstract: One sentence per key finding, specific numbers
Step 3: Load Technical References (if needed)
If writing about methodology or formal results:
- Read existing methodology/model sections for notation and definitions
- Use notation consistently with what's already in the paper
Step 4: Draft
Write the section following these rules:
- First sentence: Concrete finding or claim, no throat-clearing
- Structure: Follow the appropriate paragraph flow for the section type
- Voice: Active, present tense for results ("Table 3 shows...")
- Quantitative claims: Use specific numbers from the project's results
- Terminology: Follow the project's
CLAUDE.mdfor paper-specific terms - LaTeX: Follow
.claude/rules/latex-conventions.mdconventions - Length: Every sentence earns its place
- Citations: Check all
\cite{}keys exist in the.bibfile. For any NEW citation, follow the verification protocol in.claude/rules/latex-citations.md. Never cite from memory.
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.
- 10d ago First seen · 84 lines · 18 tokens per session scan A 35184aedddff
write-section is a skill published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 992 once invoked, about $0.0001 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.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.