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 Qiushen-first/cn-investment-banking-skills --skill cn-ib-deck-qcgit clone --depth 1 https://github.com/Qiushen-first/cn-investment-banking-skillsWrote 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/qiushen-first/cn-investment-banking-skills/cn-ib-deck-qc)<a href="https://agentmods.dev/skills/qiushen-first/cn-investment-banking-skills/cn-ib-deck-qc"><img src="https://agentmods.dev/badge/skills/qiushen-first/cn-investment-banking-skills/cn-ib-deck-qc/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/qiushen-first/cn-investment-banking-skills/cn-ib-deck-qc"><img src="https://agentmods.dev/badge/skills/qiushen-first/cn-investment-banking-skills/cn-ib-deck-qc.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 | $0.00111 | $0.00892 |
| Opus 5.5 | $0.00044 | $0.00357 |
| Sonnet 5.5 | $0.00022 | $0.00178 |
| Haiku 4.5 | $0.00011 | $0.00089 |
Grade A, and why
cn-ib-deck-qc 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 2mo 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CN IB Deck QC
Review a deck as a banker preparing it for circulation. Treat automated hits as leads, not final conclusions.
Principles
- Protect the client first: wrong entity, stale transaction terms, unsupported claims, and other-project residue are release blockers.
- Reconcile facts before polishing layout.
- Inspect both the editable file and rendered slides; XML or text extraction cannot prove visual correctness.
- Preserve the user's template, slide geometry, and house style unless asked to redesign.
- Report each issue with slide number, exact location, evidence, severity, and a concrete correction.
- Separate confirmed defects from items that require professional judgment.
Workflow
1. Establish the review baseline
Confirm:
- intended audience and circulation status;
- client, transaction, valuation date, and reporting period;
- approved source documents and data cut-off date;
- governing template and any required disclaimers;
- whether the task is a full review or a delta review after refresh.
Do not assume that an old deck is the source of truth.
2. Scan the editable deck
Read references/deck-qc-checklist.md, then run:
python3 scripts/scan_pptx.py input.pptx --output deck-issues.csv
To detect names or terms from another project, create a temporary text file with one forbidden term per line and run:
python3 scripts/scan_pptx.py input.pptx --forbidden-terms forbidden.txt --output deck-issues.csv
The script checks text-accessible placeholders, unit case, duplicate punctuation, superlatives, small font settings, and supplied forbidden terms. It does not validate numbers against sources and does not replace rendering.
3. Reconcile high-risk content
Check every slide containing:
- transaction structure, timetable, valuation, fundraising, share capital, or ownership;
- historical or forecast financials;
- market share, ranking, growth, capacity, customers, or order backlog;
- legal, regulatory, tax, accounting, or eligibility conclusions;
- credentials, tombstones, league tables, or comparable transactions.
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.
- 2mo ago First seen · 97 lines · 111 tokens per session scan A 36c426297ed8
cn-ib-deck-qc is a skill published in the GitHub repository Qiushen-first/cn-investment-banking-skills (101 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 111 tokens to every session and 892 once invoked, about $0.0004 per session on Opus 5.5. 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.
cn-ib-deck-qc compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| cn-ib-deck-qcQiushen-first/cn-investment-banking-skills | Skill | 101 | 2mo ago | Apache-2.0 | A | 111 | Codex |
| dcf-modelNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 14 | Claude Code, Codex |
| comps-analysisNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 13 | Claude Code, Codex |
| lbo-modelNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 19 | Claude Code, Codex |
| lbo-modelanthropics/financial-services | Skill | 41k | 9d ago | Apache-2.0 | A | 57 | Claude Code |
| 3-statement-modelNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 18 | Claude Code, Codex |
| merger-modelNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 18 | Claude Code, Codex |