ECC is a toolkit that organizes and improves how coding agents work through skills, memory, security checks, research practices, and related extensions. It is for developers using agents such as Claude Code, Codex, OpenCode, and Cursor.
Borrowing it
Nothing to install: this file belongs to affaan-m/ECC. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/affaan-m/ECC/main/.agents/skills/brand-voice/SKILL.mdgit clone --depth 1 https://github.com/affaan-m/ECCWrote 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/affaan-m/ecc/brand-voice)<a href="https://agentmods.dev/skills/affaan-m/ecc/brand-voice"><img src="https://agentmods.dev/badge/skills/affaan-m/ecc/brand-voice/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/affaan-m/ecc/brand-voice"><img src="https://agentmods.dev/badge/skills/affaan-m/ecc/brand-voice.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00775 |
| Opus 5 | $0.00026 | $0.00387 |
| Sonnet 5 | $0.00011 | $0.00155 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
brand-voice 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 11d 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
8 near-identical copies found in the catalogue:
- brand-voice — 100% identical, 0 lines differ
- brand-voice — 98% identical, 2 lines differ
- brand-voice — 98% identical, 2 lines differ
- brand-voice — 98% identical, 2 lines differ
- brand-voice — 95% identical, 1 lines differ
- brand-voice — 95% identical, 1 lines differ
- brand-voice — 95% identical, 1 lines differ
- brand-voice — 95% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Voice
Build a durable voice profile from real source material, then use that profile everywhere instead of re-deriving style from scratch or defaulting to generic AI copy.
When to Activate
- the user wants content or outreach in a specific voice
- writing for X, LinkedIn, email, launch posts, threads, or product updates
- adapting a known author's tone across channels
- the existing content lane needs a reusable style system instead of one-off mimicry
Source Priority
Use the strongest real source set available, in this order:
- recent original X posts and threads
- articles, essays, memos, launch notes, or newsletters
- real outbound emails or DMs that worked
- product docs, changelogs, README framing, and site copy
Do not use generic platform exemplars as source material.
Collection Workflow
- Gather 5 to 20 representative samples when available.
- Prefer recent material over old material unless the user says the older writing is more canonical.
- Separate "public launch voice" from "private working voice" if the source set clearly splits.
- If live X access is available, use
x-apito pull recent original posts before drafting. - If site copy matters, include the current ECC landing page and repo/plugin framing.
What to Extract
- rhythm and sentence length
- compression vs explanation
- capitalization norms
- parenthetical use
- question frequency and purpose
- how sharply claims are made
- how often numbers, mechanisms, or receipts show up
- how transitions work
- what the author never does
Output Contract
Produce a reusable VOICE PROFILE block that downstream skills can consume directly. Use the schema in references/voice-profile-schema.md.
Keep the profile structured and short enough to reuse in session context. The point is not literary criticism. The point is operational reuse.
Affaan / ECC Defaults
If the user wants Affaan / ECC voice and live sources are thin, start here unless newer source material overrides it:
What ships with it
2 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.
- 11d ago First seen · 97 lines · 53 tokens per session scan A 1507b4251d07
brand-voice is a skill published in the GitHub repository affaan-m/ECC (255,484 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 775 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.
Other skills, from other repositories
pr-reviewer
Reviews a diff or security scope read-only using evidence-tiered findings, structural and context-error rubrics, and repository review policy. Use when asked to "review my changes", "structural review", "review for AI patterns", or "security audit". For applying fixes use tidy; for UI defects use ui-design.
ui-design
Designs and builds React/Next/Tailwind UI and audits visual and interaction defects. Use when asked to "build a landing page", "extract our design system", "add dark mode", "make this responsive", "remove UI slop", or "audit this component". For product decisions use product-design; for browser measurements use…
spawn-reviewers
Spawn and collect the reviewer fleet at stage20spawnreviewers. Consumes spawn.json.spec (the authoritative spawn spec from derive-spawn-spec / derive-static-spec), resolves CODEINTELALLOWED, builds per-agent prompts from the per-agent template + role suffixes (Bug Hunter A/B, Unified Auditor, Domain Critics, Impact…
pr-babysitter
Monitors or repairs an open GitHub PR: CI failures, conflicts, review threads, and merge readiness, reporting state changes. Use when asked to "watch this PR", "fix CI", "resolve conflicts", or "address review comments". For PR metadata use pr-creator; for npm release PRs use autoship.
readme-creator
Creates or rewrites a README for the project consumer, using verified install commands, a runnable quickstart, and house presentation conventions. Use when asked to "write a README", "rewrite our README", or replace scaffold boilerplate. For an in-place prose audit use docs-writing; for agent instructions use…
ui-verification
Runs scoped browser probes for focus, hit targets, overflow, themes, request failures, and performance attribution, with evidence linked to UI rule IDs. Use when asked to "verify this in the browser", "reproduce this finding", or "check the fix". For source audits and severity use ui-design; field metrics require RUM…