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 Sivan757/dsh-agent-plugins-market --skill automation-recommendergit 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/skills/sivan757/dsh-agent-plugins-market/automation-recommender)<a href="https://agentmods.dev/skills/sivan757/dsh-agent-plugins-market/automation-recommender"><img src="https://agentmods.dev/badge/skills/sivan757/dsh-agent-plugins-market/automation-recommender/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/sivan757/dsh-agent-plugins-market/automation-recommender"><img src="https://agentmods.dev/badge/skills/sivan757/dsh-agent-plugins-market/automation-recommender.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 | $0.00079 | $0.02604 |
| Opus 5.5 | $0.00032 | $0.01042 |
| Sonnet 5.5 | $0.00016 | $0.00521 |
| Haiku 4.5 | $0.00008 | $0.00260 |
Grade A, and why
automation-recommender 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
86% identical to claude-automation-recommender — 68 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 — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automation Recommender
Analyze codebase patterns to recommend tailored DeepSeek Harness automations across all extensibility options.
This skill is read-only. It analyzes the codebase and outputs recommendations. It does NOT create or modify any files. Users implement the recommendations themselves or ask the agent separately to help build them.
Output Guidelines
- Recommend 1-2 of each type: Don't overwhelm - surface the top 1-2 most valuable automations per category
- If user asks for a specific type: Focus only on that type and provide more options (3-5 recommendations)
- Go beyond the reference lists: The reference files contain common patterns, but use web search to find recommendations specific to the codebase's tools, frameworks, and libraries
- Tell users they can ask for more: End by noting they can request more recommendations for any specific category
Automation Types Overview
| Type | Best For |
|---|---|
| Hooks | Automatic actions on tool events (format on save, lint, block edits) |
| Subagents | Specialized reviewers/analyzers that run in parallel |
| Skills | Packaged expertise, workflows, and repeatable tasks (invoked by the agent or user via /skill-name) |
| Plugins | Collections of skills that can be installed |
| MCP Servers | External tool integrations (databases, APIs, browsers, docs) |
Workflow
Phase 1: Codebase Analysis
Gather project context:
# Detect project type and tools
ls -la package.json pyproject.toml Cargo.toml go.mod pom.xml 2>/dev/null
cat package.json 2>/dev/null | head -50
# Check dependencies for MCP server recommendations
cat package.json 2>/dev/null | grep -E '"(react|vue|angular|next|express|fastapi|django|prisma|supabase|convex|stripe)"'
# Check for existing DeepSeek Harness config
ls -la .agents/ AGENTS.md 2>/dev/null
# Analyze project structure
ls -la src/ app/ lib/ tests/ components/ pages/ api/ 2>/dev/null
Key Indicators to Capture:
What ships with it
5 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.
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 · 290 lines · 79 tokens per session scan A 6b0cec27d6d5
automation-recommender is a skill published in the GitHub repository Sivan757/dsh-agent-plugins-market (13 stars, last pushed today), licensed MIT. It adds 79 tokens to every session and 2,604 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to claude-automation-recommender, differing in 68 lines, and is treated as a copy.
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