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 Demerzels-lab/elsamultiskillagent --skill super-skillsgit clone --depth 1 https://github.com/Demerzels-lab/elsamultiskillagentWrote 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/demerzels-lab/elsamultiskillagent/super-skills)<a href="https://agentmods.dev/skills/demerzels-lab/elsamultiskillagent/super-skills"><img src="https://agentmods.dev/badge/skills/demerzels-lab/elsamultiskillagent/super-skills/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/demerzels-lab/elsamultiskillagent/super-skills"><img src="https://agentmods.dev/badge/skills/demerzels-lab/elsamultiskillagent/super-skills.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 | $0.00067 | $0.03199 |
| Opus 5 | $0.00034 | $0.01599 |
| Sonnet 5 | $0.00013 | $0.00640 |
| Haiku 4.5 | $0.00007 | $0.00320 |
Grade A, and why
task-decomposer 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- task-decomposer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Decomposer & Skill Generator
This skill helps decompose complex user requests into executable subtasks, identify required capabilities for each task, search for existing skills from the open skills ecosystem, and automatically create new skills when no existing solution is available.
Core Workflow
User Request → Task Decomposition → Capability Identification → Skill Search → Gap Analysis → Skill Creation → Execution Plan
Phase 1: Task Analysis & Decomposition
When receiving a user request, follow these steps:
Step 1: Understand User Intent
Analyze the request to identify:
- Core objective: What is the end goal?
- Domains involved: What areas of expertise are needed?
- Trigger mechanism: One-time, scheduled, or event-driven?
Example analysis:
User Input: "Help me get email summaries every morning and send them to Slack"
Analysis:
- Core objective: Automated email digest delivery to Slack
- Domains: Email access, content summarization, messaging
- Trigger: Scheduled (daily morning)
Step 2: Decompose into Atomic Tasks
Break down the complex task into minimal executable units:
Task Decomposition:
- task_id: 1
name: "Access and retrieve email list"
type: "data_retrieval"
input: "Email credentials/session"
output: "List of emails with metadata"
dependencies: []
- task_id: 2
name: "Extract key information from emails"
type: "data_extraction"
input: "Email list"
output: "Structured email data"
dependencies: [1]
- task_id: 3
name: "Generate email summary"
type: "content_generation"
input: "Structured email data"
output: "Formatted summary text"
dependencies: [2]
- task_id: 4
name: "Send message to Slack"
type: "message_delivery"
input: "Summary text, Slack webhook/token"
output: "Delivery confirmation"
dependencies: [3]
- task_id: 5
name: "Configure scheduled execution"
type: "scheduling"
input: "Workflow script, schedule config"
output: "Active scheduled job"
dependencies: [4]
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.
- 9d ago First seen · 472 lines · 67 tokens per session scan A 5de15e34c464
task-decomposer is a skill published in the GitHub repository Demerzels-lab/elsamultiskillagent (10 stars, last pushed 4mo ago), licensed MIT. It adds 67 tokens to every session and 3,199 once invoked, about $0.0003 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 2026-09-03.
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