Skill Compose is an open-source platform for building and running AI agents that use modular skills. It is intended for creating skill-powered agents without workflow graphs or a command-line interface, and the catalogue skills are examples of those agent capabilities.
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 dp-archive/archive --skill skills-plannergit clone --depth 1 https://github.com/dp-archive/archiveWrote 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/dp-archive/archive/skills-planner)<a href="https://agentmods.dev/skills/dp-archive/archive/skills-planner"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/skills-planner/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/dp-archive/archive/skills-planner"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/skills-planner.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 | $0.00068 | $0.01912 |
| Opus 5 | $0.00034 | $0.00956 |
| Sonnet 5 | $0.00014 | $0.00382 |
| Haiku 4.5 | $0.00007 | $0.00191 |
Grade A, and why
skills-planner 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 — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skills Planner
Plan and compose skills to fulfill user requirements efficiently.
Workflow
- Analyze Requirements - Understand user goals and constraints
- Evaluate Existing Skills - Match requirements to available skills
- Identify Gaps - Determine what capabilities are missing
- Design New Skills - Specify requirements for missing skills (if any)
- Compose Agent - Design system prompt for the agent using all skills
- Present Plan - Output structured plan for user confirmation
Input Requirements
Before planning, gather:
- User Requirements: What the user wants to accomplish
- Existing Skills List: Available skills with their descriptions
Planning Process
Step 1: Analyze Requirements
Break down user requirements into discrete capabilities:
User Request: "Build an agent that can analyze financial reports"
Required Capabilities:
1. PDF reading/parsing
2. Data extraction (tables, numbers)
3. Financial metrics calculation
4. Report generation
5. Visualization (charts)
Step 2: Evaluate Existing Skills
For each capability, check if an existing skill covers it:
Capability: PDF reading/parsing
Existing Skill Match: pdf (extracts text/tables from PDFs) ✓
Capability: Financial metrics calculation
Existing Skill Match: None found ✗
Selection Criteria:
- Prefer skills that directly match the capability
- Consider skill scope (narrow and focused > broad and generic)
- Check for overlapping functionality to avoid redundancy
Step 3: Identify Gaps
List capabilities not covered by existing skills. These become candidates for new skills.
Gap Analysis Questions:
- Is this capability truly necessary, or can existing skills be combined?
- Can the base model handle this without a skill?
- Is this capability reusable across other tasks?
Step 4: Design New Skills (if needed)
For each gap, specify:
skill_name: financial-analyzer
skill_requirements: |
Purpose: Calculate and interpret financial metrics from extracted data
Core Capabilities:
- Calculate common ratios (P/E, ROE, debt-to-equity, etc.)
- Identify trends across time periods
- Flag anomalies or concerns
- Generate insights in plain language
Input: Structured financial data (revenue, expenses, assets, etc.)
Output: Analysis report with metrics, trends, and recommendations
Workflow:
1. Validate input data completeness
2. Calculate standard financial ratios
3. Compare to industry benchmarks (if provided)
4. Identify significant changes or outliers
5. Generate narrative summary
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 · 284 lines · 68 tokens per session scan A ce9dc9cd36a8
skills-planner is a skill published in the GitHub repository dp-archive/archive (1,107 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 68 tokens to every session and 1,912 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 2026-08-30.
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