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 jstoup111/ai-conductor --skill assessgit clone --depth 1 https://github.com/jstoup111/ai-conductorWrote 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/jstoup111/ai-conductor/assess)<a href="https://agentmods.dev/skills/jstoup111/ai-conductor/assess"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/assess/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/jstoup111/ai-conductor/assess"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/assess.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 9 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00044 | $0.01834 |
| Opus 5 | $0.00022 | $0.00917 |
| Sonnet 5 | $0.00009 | $0.00367 |
| Haiku 4.5 | $0.00004 | $0.00183 |
Grade A, and why
assess 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Performs a comprehensive technical assessment of a codebase using 9 specialist agents that each deeply evaluate one dimension of code health, followed by a CTO orchestrator that synthesizes findings into a prioritized report with opinionated recommendations.
Correctness gate: every finding is a claim about the codebase. Per the /verify-claims
protocol, each specialist finding carries a grounded confidence % and its basis (verified — you
observed it in the code — vs inferred), never asserts a finding it has not verified, and marks a
low-confidence judgment as tentative rather than stating it as fact. The CTO orchestrator must
not raise a finding's confidence beyond what its evidence supports when prioritizing.
Provider-native delegation
Dispatch each specialist through the selected host's available subagent facility. Preserve the specialist persona, fresh assessment context, report output, and the assessment gates regardless of host. Claude delegation: Claude uses the Agent tool; the Claude model names below apply only to that facility. A Codex-selected run uses its available subagent facility and configured Codex provider policy, without translating Claude model names.
Invocation:
- Onboarding: Runs as part of
/conductafter bootstrap for existing projects - On-demand: User invokes
/assessanytime for a health check - Selective:
/assess --area securityruns only the security specialist
Practices
1. Determine Scope
Check invocation arguments:
| Argument | Behavior |
|---|---|
| (none) | Full assessment — all 9 specialists + CTO synthesis |
--area <name> |
Single specialist only (security, data-integrity, dependencies, architecture, duplication, testing, infrastructure, observability, devex) |
2. Prepare Assessment Directory
Create .pipeline/assessment/ if it doesn't exist. Clear any stale reports from previous runs.
3. Gather Shared Context
Before dispatching specialists, gather context they all need:
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 First seen · 185 lines · 44 tokens per session scan A eb65eb55941f
assess is a skill published in the GitHub repository jstoup111/ai-conductor (7 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 1,834 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.