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/commands/sivan757/dsh-agent-plugins-market/modernize-reimagine)<a href="https://agentmods.dev/commands/sivan757/dsh-agent-plugins-market/modernize-reimagine"><img src="https://agentmods.dev/badge/commands/sivan757/dsh-agent-plugins-market/modernize-reimagine/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/commands/sivan757/dsh-agent-plugins-market/modernize-reimagine"><img src="https://agentmods.dev/badge/commands/sivan757/dsh-agent-plugins-market/modernize-reimagine.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 | $0.00021 | $0.01617 |
| Opus 5.5 | $0.00008 | $0.00647 |
| Sonnet 5.5 | $0.00004 | $0.00323 |
| Haiku 4.5 | $0.00002 | $0.00162 |
Grade A, and why
modernize-reimagine 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 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.
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.
This is a copy
92% identical to modernize-reimagine — 188 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The first token of $ARGUMENTS is the system dir ($1); everything
after it is the target vision — it is usually multiple words, so do not
truncate it to one token. Below, <vision> means that full remainder.
Reimagine legacy/$1 as:
This is not a port — it's a rebuild from extracted intent. The legacy system becomes the specification source, not the structural template. This command orchestrates a multi-agent team with explicit human checkpoints.
The brief is binding — read it first. If analysis/$1/MODERNIZATION_BRIEF.md
exists, this reimagine is executing one of its phases: read it before doing
anything below. Find the phase that names this command with a scope matching
$1 and , and treat that phase's scope, entry criteria, exit
criteria, and any edits the user made to it as binding on the phases below
— on top of, never instead of, this command's own two HITL checkpoints.
Entry criteria are gates, not context: if one is not met (a prior phase's
exit criteria, an SME sign-off the brief requires), meeting it is the
next step — do not proceed past it and do not silently re-plan around it. If
the brief exists but no phase matches, stop and ask which phase this is. The
user steers execution by editing the brief; a brief the execution command
never reads cannot steer anything.
Phase A — Specification mining (parallel agents)
Spawn concurrently and show the user that all three are running:
-
business-rules-extractor — "Extract every business rule from legacy/$1 into Given/When/Then form. Output to a structured list I can parse."
-
legacy-analyst — "Catalog every external interface of legacy/$1: inbound (screens, APIs, batch triggers, queues) and outbound (reports, files, downstream calls, DB writes). For each: name, direction, payload shape, frequency/SLA if discernible. Mask any credential embedded in endpoints or payload examples per your secret-handling rules."
-
legacy-analyst — "Identify the core domain entities in legacy/$1 and their relationships. Return as an entity list + Mermaid erDiagram."
Collect results. Write analysis/$1/AI_NATIVE_SPEC.md containing:
- Capabilities (what the system must do — derived from rules + interfaces)
- Domain Model (entities + erDiagram)
- Interface Contracts (each external interface as an OpenAPI fragment or AsyncAPI fragment)
- Non-functional requirements inferred from legacy (batch windows, volumes)
- Behavior Contract (the Given/When/Then rules — these are the acceptance tests)
Credential values are masked everywhere in the spec; connection details
appear as env-var placeholders (${DATABASE_URL}), never literals.
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 · 144 lines · 21 tokens per session scan A b6a9b4a75f65
modernize-reimagine is a command published in the GitHub repository Sivan757/dsh-agent-plugins-market (13 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 1,617 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to modernize-reimagine, differing in 188 lines, and is treated as a copy.
Other commands, from other repositories
memory-report
Calibration + inventory check of your causal memory (prediction ledger accuracy, what you know).
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.