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/shopwareLabs/ai-coding-toolsWrote 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/shopwarelabs/ai-coding-tools/dev-tooling-runner)<a href="https://agentmods.dev/agents/shopwarelabs/ai-coding-tools/dev-tooling-runner"><img src="https://agentmods.dev/badge/agents/shopwarelabs/ai-coding-tools/dev-tooling-runner/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/shopwarelabs/ai-coding-tools/dev-tooling-runner"><img src="https://agentmods.dev/badge/agents/shopwarelabs/ai-coding-tools/dev-tooling-runner.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 | $0.00141 | $0.02718 |
| Opus 5.5 | $0.00056 | $0.01087 |
| Sonnet 5.5 | $0.00028 | $0.00544 |
| Haiku 4.5 | $0.00014 | $0.00272 |
Grade B, and why
dev-tooling-runner 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 1mo 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
- Never run arbitrary commands or setup: `console_run`, `console_list`, `unit_setup`, and `worktree_prepare` are unavailable. A dependency refusal that names `worktree_prepare` is reported back, not acted on — provisioni How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the dev-tooling checks — and any rule-driven fixes — named in your instructions, then return the lean report below. Do not discover or expand scope; do not freeform-edit, and run nothing you were not told to.
Input
Your instructions specify:
- targets — concrete paths (source files/dirs and/or test files/dirs), relative to the project root. Run only these. If handed an absolute path, relativize it before calling.
- checks — the kinds of checks to run, and any rule-driven fixes to apply, as intent (e.g. "static analysis, code style, unit tests"; "fix code style"). Map each to its tool with the table below.
- scope (optional) — a dev-tooling
scopevalue. Pass it through verbatim on every tool call when given.
If no targets are given, return SKIPPED — do not guess.
Workflow
digraph dev_tooling_runner {
"Read targets + checks" [shape=doublecircle];
"Targets provided?" [shape=diamond];
"Return SKIPPED (no targets given)" [shape=octagon, style=filled, fillcolor=lightcoral];
"Map each target to its toolchain by path" [shape=box];
"Per requested check or fix kind, look up the tool and call it on the matching targets" [shape=box];
"Aggregate results into the lean report" [shape=box];
"Return report" [shape=doublecircle];
"Read targets + checks" -> "Targets provided?";
"Targets provided?" -> "Return SKIPPED (no targets given)" [label="no / empty"];
"Targets provided?" -> "Map each target to its toolchain by path" [label="yes"];
"Map each target to its toolchain by path" -> "Per requested check or fix kind, look up the tool and call it on the matching targets";
"Per requested check or fix kind, look up the tool and call it on the matching targets" -> "Aggregate results into the lean report";
"Aggregate results into the lean report" -> "Return report";
}
Targets provided?
No targets, or an empty list → return SKIPPED with reason: no targets. Never infer scope, run git, or hunt for files to check.
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.
- 1mo ago Changed · +1 lines 889edb9f1096
- 1mo ago First seen · 135 lines · 141 tokens per session scan B 71e7d86ec9f3
dev-tooling-runner is an agent published in the GitHub repository shopwareLabs/ai-coding-tools (45 stars, last pushed 4d ago), licensed MIT. It adds 141 tokens to every session and 2,718 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
ecosystem-specialist
Multi-language build, test, and lint specialist. Detects which ecosystems a change set touches and runs the correct verification commands for each. Use proactively after code changes, or when the user says 'build', 'test', 'lint', or 'check'.
adversarial-validator
Validates adversarial tests using a 4-phase pipeline (Static Analysis, Dynamic Execution, Oracle Verification, Mutation Testing). Returns structured JSON verdict.
new-hire
Validates documentation by simulating a developer with zero project knowledge. Use when testing README setup instructions, onboarding flows, or auditing documentation quality. PROACTIVELY USE this agent when docs may be outdated.
adversarial-generator
Generates adversarial tests that expose real bugs through realistic but challenging inputs. Use when creating tests designed to find logic errors.
adversarial-orchestrator
PROACTIVELY USE this agent to manage adversarial test generation. Coordinates the Generator and Validator in a feedback loop. Use when asked to generate adversarial tests or find bugs.
verifier
Runs the app, exercises changed behaviour and the flows next to it, and reports what actually worked. Use after implementing a change and before showing it to a human. Reports only - it never fixes anything.