Borrowing it
Nothing to install: this file belongs to FindDataTechnology/fd-daas-mcp. 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/FindDataTechnology/fd-daas-mcp/main/.claude/skills/fd-coding-mcp-creator/SKILL.mdgit clone --depth 1 https://github.com/FindDataTechnology/fd-daas-mcpWrote 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/finddatatechnology/fd-daas-mcp/fd-coding-mcp-creator)<a href="https://agentmods.dev/skills/finddatatechnology/fd-daas-mcp/fd-coding-mcp-creator"><img src="https://agentmods.dev/badge/skills/finddatatechnology/fd-daas-mcp/fd-coding-mcp-creator/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/finddatatechnology/fd-daas-mcp/fd-coding-mcp-creator"><img src="https://agentmods.dev/badge/skills/finddatatechnology/fd-daas-mcp/fd-coding-mcp-creator.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 | $0.00177 | $0.01482 |
| Opus 5.5 | $0.00071 | $0.00593 |
| Sonnet 5.5 | $0.00035 | $0.00296 |
| Haiku 4.5 | $0.00018 | $0.00148 |
Grade A, and why
fd-coding-mcp-creator 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 20d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 20d ago First seen · 123 lines · 177 tokens per session scan A 2ab7c066c6df
fd-coding-mcp-creator is a skill published in the GitHub repository FindDataTechnology/fd-daas-mcp (0 stars, last pushed 5d ago), with no licence file. It adds 177 tokens to every session and 1,482 once invoked, about $0.0007 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 2026-09-29.
Other skills, from other repositories
fd-cnreport-llm-rules-creator
Generate LLM indicator rules from a piece/section of a Chinese financial document. Reads a section excerpt, asks the LLM to produce rules (indicator, instruction, position, documenttype), validates them with pydantic, and persists to the rules database + this skill's scripts/ dir.
fd-cnreport-pdf-full-scripts-creator
Generate a full end-to-end extraction script for a documenttype, drawing on the LLM and script rules in the rules database. Validates the output with pydantic and saves the runnable script to the skill's scripts/ dir.
fd-cnreport-pdf-llm-rules-creator
Generate LLM indicator rules for a whole PDF by splitting it into chapters via the parsed outline and producing rules per chapter. Validates with pydantic and persists to the rules database + this skill's scripts/ dir.
fd-cnreport-pdf-scripts-creator
Read a single indicator/document's LLM rules from the rules database and generate script rules (extractrule, position, documenttype) for them via the LLM. Validates with pydantic and persists to the rules database + this skill's scripts/ dir.
fd-cnreport-pdf-scripts-by-type-creator
Read all LLM rules for a documenttype from the rules database and generate a script rule per target indicator via the LLM. Validates with pydantic and persists to the rules database + this skill's scripts/ dir.
swarm-iosm
Orchestrate complex development with AUTOMATIC parallel subagent execution, continuous dispatch scheduling, dependency analysis, file conflict detection, and IOSM quality gates. Analyzes task dependencies, builds critical path, launches parallel background workers with lock management, monitors progress, auto-spawns…