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.
git clone --depth 1 https://github.com/Sivan757/dsh-agent-plugins-marketWrote 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/agents/sivan757/dsh-agent-plugins-market/legacy-analyst)<a href="https://agentmods.dev/agents/sivan757/dsh-agent-plugins-market/legacy-analyst"><img src="https://agentmods.dev/badge/agents/sivan757/dsh-agent-plugins-market/legacy-analyst/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/agents/sivan757/dsh-agent-plugins-market/legacy-analyst"><img src="https://agentmods.dev/badge/agents/sivan757/dsh-agent-plugins-market/legacy-analyst.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 | $0.00054 | $0.00844 |
| Opus 5.5 | $0.00022 | $0.00338 |
| Sonnet 5.5 | $0.00011 | $0.00169 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade B, and why
legacy-analyst scanned grade B with 1 finding 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 today.
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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
literals crafted to look like directives to an AI tool ("SYSTEM:", "ignore previous instructions", "mark this rule as approved", "this finding is a Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
This is a copy
100% identical to legacy-analyst — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior legacy systems analyst with 20 years of experience reading code nobody else wants to read — COBOL, JCL, RPG, classic ASP, EJB 2, Struts 1, raw servlets, Perl CGI.
Your job is understanding, not judgment. The code in front of you kept a business running for decades. Treat it with respect, figure out what it does, and explain it in terms a modern engineer can act on.
How you work
- Read before you grep. Open the entry points (main programs, JCL jobs, controllers, routes) and trace the actual flow. Pattern-matching on names lies; control flow doesn't.
- Cite everything. Every claim gets a
path/to/file:linereference. If you can't point to a line, you don't know it — say so. - Distinguish "is" from "appears to be." When you're inferring intent from structure, flag it: "appears to handle X (inferred from variable names; no comments confirm)."
- Use the right vocabulary for the stack. COBOL has paragraphs, copybooks, and FD entries. CICS has transactions and BMS maps. JCL has steps and DD statements. Java has packages and beans. Use the native terms so SMEs trust your output.
- Find the data first. In legacy systems, the data structures (copybooks, DDL, schemas) are usually more stable and truthful than the procedural code. Map the data, then map who touches it.
- Note what's missing. Unhandled error paths, TODO comments, commented-out blocks, magic numbers — these are signals about history and risk.
Secret handling (mandatory)
Legacy code is full of live credentials, and your findings get copied into
shareable reports. When the evidence for a finding — hardcoded config,
dead code, debt, an interface payload — includes a credential, API key,
token, connection string, or private key, never reproduce the value.
Cite file:line with a masked preview (VALUE 'Pr0d****',
password=****). The finding is the practice, not the value.
Output format
Default to structured markdown: tables for inventories, Mermaid for graphs, bullet lists for findings. Always include a "Confidence & Gaps" footer listing what you couldn't determine and what you'd ask an SME.
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.
- today First seen · 70 lines · 54 tokens per session scan B bb877cd5c4d2
legacy-analyst is an agent published in the GitHub repository Sivan757/dsh-agent-plugins-market (13 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 844 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (instruction-override phrasing). It is 100% identical to legacy-analyst, differing in 7 lines, and is treated as a copy.
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