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.
npx skills add diagrid-labs/dapr-skills --skill review-agent-toolsgit clone --depth 1 https://github.com/diagrid-labs/dapr-skillsWrote 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/diagrid-labs/dapr-skills/review-agent-tools)<a href="https://agentmods.dev/skills/diagrid-labs/dapr-skills/review-agent-tools"><img src="https://agentmods.dev/badge/skills/diagrid-labs/dapr-skills/review-agent-tools/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/diagrid-labs/dapr-skills/review-agent-tools"><img src="https://agentmods.dev/badge/skills/diagrid-labs/dapr-skills/review-agent-tools.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 | $0.00037 | $0.01131 |
| Opus 5.5 | $0.00015 | $0.00452 |
| Sonnet 5.5 | $0.00007 | $0.00226 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
review-agent-tools 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Agents — Tools
Overview
Scans agent tool definitions for idempotency issues, missing docstrings / descriptions, unbounded return payloads, swallowed exceptions, and convention violations. Applies to both supported paths: the Python dapr-agents SDK and diagrid[<extra>] wrappers, and .NET Microsoft Agent Framework agents running on Dapr Workflow. "Dapr Agents" is the Python framework specifically — it has no .NET SDK. Read-only: this skill never modifies source files. Memory configuration is covered by review-agent-memory; multi-agent orchestration by review-agent-orchestration; observability by review-agent-observability.
Execution Order
You MUST follow these phases in strict order. Do not load files outside the agreed scope, and do not write or edit any files.
- Resolve scope — Read
../dapr-shared/review-scope-prompt.mdand follow it to setscope_root. - Detect target — Read
../dapr-shared/review-detect-target-agent.mdand follow it to producelanguage,flavor,file_flavors,wrapper_frameworks, andtool_files. Iftool_filesis empty, there is nothing to review: emit the report with the generic nothing-to-review line from../dapr-shared/review-report-format.md(reason: no tool definitions found in scope — none of the per-framework tool markers listed inreview-detect-target-agent.md(@tool,@function_tool,FunctionTool(,@<agent>.tool, tool classes, functions intools=[...], …) orAIFunctionFactory.Create(). It carries no rule id and no severity. Stop. - Load checklist — Based on
language, read exactly one of: - Scan — For each tool file in scope, apply every rule from the loaded checklist using
GrepandRead. For each match, capturefile:line, the rule id, and a short evidence snippet. Confirm the match is inside a tool body (as defined inreview-detect-target-agent.mdfor Python, or the method wrapped withAIFunctionFactory.Createfor .NET) before promoting it from "Please verify" to a graded severity. In amixedPython project, apply each rule with the flavor of the file being scanned. - Report — Format every finding using the canonical template from
../dapr-shared/review-report-format.md. Group by severity, then rule id, then file path. - Show final message — Your last output is the report. Do not append a summary or next-action prompt other than the
## Next stepsblock defined by the report format. Clarifying questions are always allowed during the review (see the report format); once answered, the report is still your last output.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago Changed · -19 tokens per session 65c5022d94c1
- 13d ago Changed 3d53f3a806a9
- 14d ago Changed 52d5be895f79
- 1mo ago First seen · 57 lines · 56 tokens per session scan A 3b8ae539397c
review-agent-tools is a skill published in the GitHub repository diagrid-labs/dapr-skills (13 stars, last pushed 10d ago), licensed MIT. It adds 37 tokens to every session and 1,131 once invoked, about $0.0001 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 2026-08-30.
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