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 danicat/speedgrapher --skill draftgit clone --depth 1 https://github.com/danicat/speedgrapherWrote 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/danicat/speedgrapher/draft)<a href="https://agentmods.dev/skills/danicat/speedgrapher/draft"><img src="https://agentmods.dev/badge/skills/danicat/speedgrapher/draft/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/danicat/speedgrapher/draft"><img src="https://agentmods.dev/badge/skills/danicat/speedgrapher/draft.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 | $0.00119 | $0.02397 |
| Opus 5.5 | $0.00048 | $0.00959 |
| Sonnet 5.5 | $0.00024 | $0.00479 |
| Haiku 4.5 | $0.00012 | $0.00240 |
Grade A, and why
draft 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 4d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draft: Adaptive-Voice Technical Writing
draft transforms structured outlines, interview briefs, and raw engineering notes into clear, authoritative, human-grade technical prose. You never impose a generic machine persona; instead, you fingerprint and replicate the author's natural voice while enforcing strict structural, pedagogical, citation, and brand-safety standards from the very first paragraph.
1. End-to-End Drafting Workflow
Follow this five-stage workflow whenever authoring or expanding a technical document:
flowchart LR
Voice["1. Voice Fingerprinting\nInspect workspace style & posts"] --> Framing["2. Outcome-First Framing\n150-word lead & GEO section leads"]
Framing --> Pedagogy["3. Grounded Drafting\nWhy-before-how code & citations"]
Pedagogy --> Safety["4. Brand, Slop & Jargon Guardrails\nSentence case & trademark rules"]
Safety --> Gate{"5. Pre-Delivery Gate\nRun `fog`, `slop` & `vale`"}
Gate -->|Out of Bounds| Pedagogy
Gate -->|Clean| Deliver["Deliver Draft + Speedgrapher Pill"]
2. Core Editorial Pillars
A. Voice Fingerprinting
Before writing the first paragraph, inspect the workspace for existing posts, README.md files, or style guidelines to capture the author's voice fingerprint:
- Pronoun & Perspective Stance: Match the author's established stance consistently—whether conversational first-person singular ("I tracked the memory leak to..."), collaborative engineering plural ("We benchmarked three column readers..."), or direct second-person instructional ("Configure the listener...").
- Sentence Cadence: Mirror the author's natural mix of crisp declarative statements (5–10 words) and analytical clauses (20–28 words).
- Peer-to-Peer Tone: Write as an experienced engineer sharing hard-won context with a colleague. Never use patronizing minimizers (simply, merely, just, obviously, trivial) or synthetic teacher-mode fillers ("Let's break this down", "Here's the kicker").
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.
- 4d ago First seen · 128 lines · 119 tokens per session scan A 2e4633894b35
draft is a skill published in the GitHub repository danicat/speedgrapher (51 stars, last pushed 4d ago), licensed Apache-2.0. It adds 119 tokens to every session and 2,397 once invoked, about $0.0005 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 2026-10-17.
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mm-web-bridge
An idea partner for a developer who discusses plans, checks current information online, and prepares self-contained tasks for Claude Code running in Windows PowerShell. It is designed for Russian-language work and mentions Telegram bots, Python, SQLite, and Railway deployment.
mm-init-project
A project setup workflow that creates a shared project record in Markdown files, including a passport, dashboard, handoff notes, and instructions for Claude.ai. Obsidian is a note-taking app that can store these files as a connected project vault.
mm-save-session
An end-of-session recorder for Claude Code that saves the conversation summary and project state in Obsidian notes. Obsidian is a note-taking app that stores information as files.
mm-handoff
A skill that creates a compact handoff.md summary for starting a new Claude chat when the current chat is full. The file records recent work, current status, open questions, and selected project context.