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-transform)<a href="https://agentmods.dev/commands/sivan757/dsh-agent-plugins-market/modernize-transform"><img src="https://agentmods.dev/badge/commands/sivan757/dsh-agent-plugins-market/modernize-transform/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-transform"><img src="https://agentmods.dev/badge/commands/sivan757/dsh-agent-plugins-market/modernize-transform.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 | $0.00017 | $0.01375 |
| Opus 5.5 | $0.00007 | $0.00550 |
| Sonnet 5.5 | $0.00003 | $0.00275 |
| Haiku 4.5 | $0.00002 | $0.00137 |
Grade A, and why
modernize-transform 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
100% identical to modernize-transform — 184 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transform legacy/$1 module $2 into $3, with proof of behavioral
equivalence.
This is a surgical, single-module transformation — one vertical slice of the
strangler fig. Output goes to modernized/$1/$2/.
Step 0a — Toolchain check (fail fast on target, adapt on legacy)
Verify the build environment before planning, not when the tests first run:
- Target stack ($3) — required. Runtime, package manager, and test
framework all respond (
java -version+mvn -v,node -v+npm -v,python3 -V+pytest --version, …). If any are missing, stop and report what to install — the new code and its tests cannot run without them, so a plan gate now would just defer the failure an hour. Suggest/modernize-preflight $1 $3for the full readiness report. - Legacy stack — advisory, never a blocker. Try a syntax-only compile
of the module being transformed (e.g.
cobc -fsyntax-only). Legacy code often cannot build locally by nature, not by misconfiguration — CICS/IMS programs have no local translator, and the real runtime may be a mainframe you don't have. A failed or impossible legacy compile does not stop the transform; it changes the equivalence strategy:- dual-execution proof is off the table — characterization tests assert against recorded traces / golden-master fixtures (real production outputs, captured reports/screens, SME-confirmed examples) instead of live legacy runs
- say so explicitly in the Step 0b plan and later in TRANSFORMATION_NOTES.md ("equivalence is trace-based; legacy was not executable in this environment"), so reviewers know the strength of the proof they're approving
Step 0b — Plan (HITL gate)
The brief is binding — read it first. If analysis/$1/MODERNIZATION_BRIEF.md
exists, this transform is one phase (or one module of a phase) of that plan:
read it before deciding anything below. Find the phase that names this
command with $2 in scope, and treat that phase's scope, entry criteria,
exit criteria, and any edits the user made to it as binding on the plan
you present below. 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 covers $2, 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.
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 · 124 lines · 17 tokens per session scan A ed630e29c0e7
modernize-transform is a command published in the GitHub repository Sivan757/dsh-agent-plugins-market (13 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,375 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to modernize-transform, differing in 184 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.