spec-promote

spec-promote is a skill for Claude Code, Codex from ronronner02/codepilot-agent. It costs 14 tokens per session (2,354 once invoked), scanned A, original, MIT.

A writing aid for drafting launch or promotional text for a feature that has already been released.

In plain words
What is it for?
Use it to prepare release announcements, launch pages, or other promotional writing about completed features.
Why use it?
It helps turn a shipped feature into copy for announcing or promoting it.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ronronner02/codepilot-agent/spec-promote
Any agent
npx skills add ronronner02/codepilot-agent --skill spec-promote
Clone the repo
git clone --depth 1 https://github.com/ronronner02/codepilot-agent

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for spec-promote

README.md
[![agentmods](https://agentmods.dev/badge/skills/ronronner02/codepilot-agent/spec-promote.svg)](https://agentmods.dev/skills/ronronner02/codepilot-agent/spec-promote)
Your own site
<a href="https://agentmods.dev/skills/ronronner02/codepilot-agent/spec-promote"><img src="https://agentmods.dev/badge/skills/ronronner02/codepilot-agent/spec-promote.svg" alt="Measured on agentmods" height="20"></a>
Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,354 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00014 $0.02354
Opus 5 $0.00007 $0.01177
Sonnet 5 $0.00003 $0.00471
Haiku 4.5 $0.00001 $0.00235

Measured 3d ago against content hash 90cc60380525, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

spec-promote 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 3d 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.

.claude/skills/spec-promote/SKILL.md · 140 lines

How it starts

The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.

spec-promote

Turn a feature that just shipped into copy-pasteable, user-facing announcement copy — right inside the engineering workflow.

Purpose

After you ship, the messaging shouldn't wait for a separate marketing pass. spec-promote figures out what shipped, picks the right channels, and drafts the copy. It is spiral-agnostic by default: with nothing installed it draws on a lite layer of editorial and social-media expertise to produce strong channel-specific copy. When the Spiral CLI (see references/spiral-cli.md) is present and authed, it uses Spiral so the drafts are voice-matched to your brand — a subtle enhancement, never a requirement.

This skill drafts only. It never posts, publishes, commits, or opens PRs. Posting is a human action. The output is always drafts for you to review, edit, and ship yourself.

Usage

spec-promote                                   # Derive what shipped from context, draft defaults
spec-promote [free-form description]           # You describe what shipped
spec-promote a tweet thread and a LinkedIn post   # Request specific channels
spec-promote 3 tweet options for the new export feature

Phase 1 — Figure out what shipped

If the user gave a free-form description of the feature, use it as the source of truth.

Otherwise, derive it from context (use what's available; don't block on any one source):

  • Merged/active PRgh pr view --json title,body,url 2>/dev/null (and gh pr view for the current branch). The title and body usually state the user-facing value.
  • The diffgit diff main...HEAD --stat and skim notable changes to ground the claim in what actually changed.
  • Changelog — the top/[Unreleased] entry in docs/changelog.md, CHANGELOG.md, or similar.
  • Recent commitsgit log --oneline -15 for the arc of the change.

Then write a 1–3 sentence summary of the user-facing value — what a user can now do that they couldn't before, and why they'd care. Describe the outcome, not the implementation. ("You can now export any report to CSV in one click" — not "Added a CsvSerializer and an export endpoint.")

Read the full file on GitHub · 140 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 3d ago First seen · 140 lines · 14 tokens per session scan A 90cc60380525

Subscribe to this mod's changes

spec-promote is a skill published in the GitHub repository ronronner02/codepilot-agent (0 stars, last pushed 2d ago), licensed MIT. It adds 14 tokens to every session and 2,354 once invoked, about $0.0001 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 2026-08-31.

Related

Other skills, from other repositories

aegis-gateway

Integrate AegisAI gateway before tool side effects (notify, publish, deploy). Use when adding Slack/Telegram/WhatsApp notify, content publish, or any irreversible external action in VAP, AegisLoop, or ai-content-factory.

vpeetla-ai/multi-agent-system-pattern · 58 tokens

deploy-vercel-render

Deploy vpeetla-ai demos: Vercel static/Next.js frontends, Render FastAPI backends, env vars, free tier gotchas. Use when shipping demos, fixing deploy failures, or adding render.yaml / vercel.json.

vpeetla-ai/multi-agent-system-pattern · 55 tokens

hitl-side-effects

Add human-in-the-loop gates for irreversible actions: LangGraph interruptbefore, AegisAI approval queue, UI approve/resume endpoints. Use when shipping, publishing, notifying, or merging agent-generated changes.

vpeetla-ai/multi-agent-system-pattern · 47 tokens

langgraph-orchestration

Build or modify LangGraph StateGraph agents in vpeetla-ai repos: typed state, nodes, conditional edges, MemorySaver, interruptbefore HITL. Use when adding orchestrators, coding loops, or multi-agent graphs.

vpeetla-ai/multi-agent-system-pattern · 52 tokens

portfolio-adr

Write ADRs, case studies, and portfolio copy for ai-architecture-portfolio and venkat-ai-portfolio. Use when documenting decisions, updating ecosystem pages, or syncing GitHub profile README with live demos.

vpeetla-ai/multi-agent-system-pattern · 46 tokens

rag-governance

Implement access-aware RAG in Enterprise RAG or VAP: hybrid retrieval, rerank, citations, AegisAI HITL for sensitive chunks. Use when tuning retrieval, adding Qdrant adapter, or wiring enterpriseragplatform.

vpeetla-ai/multi-agent-system-pattern · 54 tokens