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 drn/dots --skill equipgit clone --depth 1 https://github.com/drn/dotsWrote 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/drn/dots/equip)<a href="https://agentmods.dev/skills/drn/dots/equip"><img src="https://agentmods.dev/badge/skills/drn/dots/equip/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/drn/dots/equip"><img src="https://agentmods.dev/badge/skills/drn/dots/equip.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00053 | $0.01996 |
| Opus 5.5 | $0.00021 | $0.00798 |
| Sonnet 5.5 | $0.00011 | $0.00399 |
| Haiku 4.5 | $0.00005 | $0.00200 |
Grade A, and why
equip 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 1mo 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill & Agent Gap Analysis
Analyze a specification document, PRD, RFC, codebase, or set of requirements against the existing skill and agent inventory. Identify gaps, propose new skills or agents to fill them, then write the approved ones.
Arguments
$ARGUMENTS- Required: path to a spec/PRD file, URL, codebase directory, or a description of the requirements to analyze
If no arguments are provided, ask the user what to analyze.
Context
- Project root: !
pwd - Existing skills: !
ls agents/skills/ 2>/dev/null | head -40 - Custom agents: !
ls agents/custom/ 2>/dev/null | head -20 - Skill descriptions: !
grep -r "^description:" agents/skills/*/SKILL.md 2>/dev/null | head -40 - Agent descriptions: !
grep "^description:" agents/custom/*.md 2>/dev/null | head -20 - Project type: !
find . -maxdepth 1 \( -name go.mod -o -name Gemfile -o -name package.json -o -name Cargo.toml -o -name pyproject.toml \) 2>/dev/null | head -3
Instructions
Step 1: Load the Source
Determine the input source from $ARGUMENTS:
- File path — Read the file directly
- URL — Fetch and extract the content
- Notion page — Use Notion MCP tools if available, otherwise fetch the URL
- Directory path — Scan the codebase for workflows, patterns, and integration points
- Pasted text — If the user pasted requirements inline, use that
- Description — If the user described the requirements conversationally, extract the capabilities from their description
If the source cannot be loaded, report the error and stop.
Step 2: Extract Capabilities
Parse the source material and extract a structured list of capabilities — the distinct things the product needs to do. For each capability, note:
- Name — short label (e.g., "user onboarding flow", "webhook retry logic")
- Type — one of: workflow, integration, automation, analysis, content generation, data pipeline, monitoring, deployment
- Actors — who or what performs it (human, agent, system, CI)
- Triggers — what initiates it (user command, schedule, event, manual)
- Complexity — low / medium / high (based on number of steps, external dependencies, error handling needed)
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.
- 1mo ago First seen · 211 lines · 53 tokens per session scan A 939c93210597
equip is a skill published in the GitHub repository drn/dots (23 stars, last pushed 11d ago), licensed MIT. It adds 53 tokens to every session and 1,996 once invoked, about $0.0002 per session on Opus 5.5. 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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