system-skill-advisor

system-skill-advisor is a skill for Claude Code, OpenCode from MichelKerkmeester/skilled-agent-harness_spec-driven-loops. It costs 24 tokens per session (6,645 once invoked), scanned C, original, MIT.

A routing tool that recommends the appropriate coding skill for a task based on available skill metadata and the current request.

In plain words
What is it for?
Use it to get skill recommendations, inspect advisor status, validate the skill graph, or rebuild its recommendations.
Why use it?
It avoids choosing an unsuitable workflow and helps direct work to the skill responsible for that type of task.

Skill for Claude CodeOpenCode

Written for Claude Code and OpenCode: allowed-tools in frontmatter, but also installed under .opencode/. Also seen: mentions AGENTS.md; mentions Codex; mentions OpenCode.

Good fit Use it to get skill recommendations, inspect advisor status, validate the skill graph, or rebuild its recommendations.

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Install with agentmods
npx agentmods add skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/system-skill-advisor
Install

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.

Any agent
npx skills add MichelKerkmeester/skilled-agent-harness_spec-driven-loops --skill system-skill-advisor
Clone the repo
git clone --depth 1 https://github.com/MichelKerkmeester/skilled-agent-harness_spec-driven-loops

Made for: Claude Code, OpenCode.

Wrote 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.

agentmods badge for system-skill-advisor

README.md
[![agentmods](https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/system-skill-advisor/github.svg)](https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/system-skill-advisor)
Your own site
<a href="https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/system-skill-advisor"><img src="https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/system-skill-advisor/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.

agentmods 80×15 button for system-skill-advisor

Your own site · 80×15
<a href="https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/system-skill-advisor"><img src="https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/system-skill-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,645 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.06645
Opus 5 $0.00012 $0.03322
Sonnet 5 $0.00005 $0.01329
Haiku 4.5 $0.00002 $0.00664

Measured yesterday against content hash 18228762b78e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade C, and why

system-skill-advisor scanned grade C 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 yesterday.

The scan reads SKILL.md. This mod also ships 3 executable files (hooks/claude/directive-lifecycle-boundary.ts, hooks/claude/user-prompt-submit.ts, hooks/lib/directive-lifecycle-contract.ts), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Hidden instructionshighPrompt injection

Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.

<!-- Keywords: system-skill-advisor, skill-advisor, advisor_recommend, advisor_status, advisor_rebuild, advisor_validate, skill_graph_scan, skill_graph_query, skill_graph_status, skill_graph_validate, skill_graph_propaga
.opencode/skills/system-skill-advisor/SKILL.md · 444 lines

How it starts

The opening of the file, as written. The whole thing — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.

System Skill Advisor

Routes non-trivial user requests to the right skill through the standalone Skill Advisor MCP package.


1. WHEN TO USE

Use this skill when the work is about skill selection, Gate 2 routing, advisor MCP tools, prompt-time skill-advisor hooks, skill graph freshness or the standalone advisor package.

Activation signals:

  • A request asks which skill should handle a task.
  • A runtime hook needs a skill recommendation before execution.
  • An operator asks about advisor_recommend, advisor_status, advisor_rebuild, advisor_validate, skill_graph_scan, skill_graph_query, skill_graph_status, skill_graph_validate or skill_graph_propagate_enhances.
  • A packet touches the skill graph, skill metadata, advisor scorer, advisor feature catalog or manual testing playbook.
  • A migration step references ADR-001: <spec-folder>.

Do not use this skill as a replacement for the recommended target skill. For example, route code implementation to sk-code, documentation authoring to sk-doc, git work to sk-git and MCP orchestration to mcp-code-mode after the advisor has made the recommendation.


2. SMART ROUTING

This package is mandatory context for non-trivial Gate 2 routing. The live advisor scores prompts through system_skill_advisor; this smart router controls which local documentation resources an agent should load while maintaining the advisor package.

Routing model:

user prompt
  |
  +-- exact skill name or explicit user direction -> named skill wins
  |
  +-- non-trivial or ambiguous request
        |
        +-- advisor_recommend on system_skill_advisor MCP
        |
        +-- top recommendation above confidence threshold -> invoke that skill
        |
        +-- ambiguous top scores -> surface top candidates and ask or route with caveat

Read the full file on GitHub · 444 lines

Files

What ships with it

60 files 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.

Changes

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.

  1. yesterday Changed 18228762b78e
  2. 6d ago Changed 6534b09a8305
  3. 8d ago First seen · 444 lines · 24 tokens per session scan C d94eeeb64db8

Subscribe to this mod's changes

system-skill-advisor is a skill published in the GitHub repository MichelKerkmeester/skilled-agent-harness_spec-driven-loops (35 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 6,645 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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