# Draft Brand Tweet

> Draft tweets for the @your-brand company account. Use this unless the user explicitly asks for a specific person's personal voice.

- Skill: `warpdotdev/draft-brand-tweet` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add warpdotdev/draft-brand-tweet`
- Raw SKILL.md: https://api.skillmd.com/api/skills/warpdotdev/draft-brand-tweet/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: warpdotdev (https://skillmd.com/u/warpdotdev)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/warpdotdev/draft-brand-tweet

---


# Draft Brand Tweet

Generate tweet drafts for the **@your-brand** company account following <BRAND>'s established voice, competitor-informed positioning, and messaging guidelines.

Default: if the user asks to draft a tweet without specifying an account, draft it as @your-brand.

## When to Use
- User asks to write, draft, or compose a tweet and doesn't specify an account (assume @your-brand)
- User explicitly wants a tweet for the @your-brand account
- User needs a post about <BRAND> product updates, <CLOUD_PRODUCT>, model support, partnerships, community, or events
- Use a personal-voice tweet skill only if the user explicitly asks for a specific person's personal voice

## Inputs
1. **Topic or objective** (required)
2. **Tweet type** (required): `feature`, `model-support`, `demo/showcase`, `partnership`, `community`, `event`, `POV/opinion`, `incident`
3. **Must-include facts/claims** (optional but strongly recommended for technical posts)
4. **URL/assets to include** (optional)
5. **Person to feature** (optional — name and role, for demo/showcase tweets)
6. **Length target** (optional): `short` (~80-130 chars), `standard` (~180-270 chars)

## Process

1. **Validate inputs**
   - If `topic` or `tweet type` is missing, ask for it.
   - If the request needs concrete claims but no facts are provided, ask for source facts or continue with clearly non-numeric/high-level wording only.

2. **Pull style anchors from corpus and style notes**
   - Read `.agents/skills/draft-brand-tweet/style-notes.md` first — this is the distilled voice guide with patterns, phrases to use/avoid, emoji conventions, tone calibration examples, and terminology guardrails.
   - Then scan your brand's recent tweet corpus (if you maintain one) for recent examples matching the tweet type.
   - Weight recent tweets highest.
   - For each draft, extract 3-5 style cues from matched corpus examples (cadence, phrasing, CTA style, length).

3. **Check competitor context**
   - Read `.agents/skills/draft-brand-tweet/competitor-style-notes.md` for competitor patterns and differentiation angles.
   - If the topic overlaps with something a competitor recently tweeted about (e.g., model support or a shared feature area), note it and ensure <BRAND>'s angle is differentiated.
   - Apply the "where <BRAND> can differentiate" insights when relevant.

4. **Apply messaging and terminology guardrails**
   - If you maintain a separate positioning or messaging repo, align with your own style guide and positioning docs.
   - Preserve your product naming consistently (product name, sub-products or platforms, and any capitalized feature names).

5. **Ground technical claims in docs**
   - Check your product docs (if available) for accuracy on technical/product claims.
   - If docs are unavailable, skip and note under "Open questions."

6. **Draft candidates**
   - Produce 3 options:
     - **Option A**: Default voice — product-first, concise
     - **Option B**: Shorter / punchier
     - **Option C**: More editorial / POV angle
   - Keep wording natural and post-ready.

7. **Self-check before output**
   - Style fidelity: Does this sound like the @your-brand corpus and `style-notes.md`?
   - Terminology: Does it match messaging docs?
   - Factual grounding: Are claims sourced? No fabricated features, metrics, or dates.
   - Banned phrases: None of the phrases from the "avoid" list in `style-notes.md`?
   - Competitor awareness: If relevant, is <BRAND>'s angle differentiated per `competitor-style-notes.md`?

## Output

Return in this format:

1. **Recommended draft**
2. **Alternative drafts** (2)
3. **Why this matches style** (3-5 concise bullets)
4. **Competitor context** (if relevant — what competitors have said on this topic, how this differentiates)
5. **Source grounding** (bullet list mapping non-trivial claims to source file paths)
6. **Open questions** (only if required facts are missing)

## Hard Constraints
- Do not fabricate product capabilities, metrics, dates, customer counts, or roadmap commitments.
- Prefer clear, plain language over hype.
- Keep tone confident and product-first, not generic "AI marketing."
- No hashtags. One emoji max.
- If uncertain, ask for missing facts or use bounded language.

## Refresh Context
If you maintain tweet corpora and scraper scripts in a separate repo, refresh them there. For example:
- Refresh the @your-brand corpus: `scripts/twitter-scraper/scrape_tweets.py --user your-org --since YYYY-MM-DD --cookies ...`
- Refresh competitor corpora: same script with `--user <competitor_a>` or `--user <competitor_b>` and `--min-likes 100`
- Re-run style-anchor selection after refresh.

## References
- `.agents/skills/draft-brand-tweet/style-notes.md` — <BRAND> voice and style patterns
- `.agents/skills/draft-brand-tweet/competitor-style-notes.md` — Competitor analysis and differentiation
- If you maintain a separate positioning or messaging repo, also check your style guide and positioning docs, plus:
  - your brand's tweet corpus (e.g. `tweets/your-org.md`)
  - competitor tweet corpora for differentiation (e.g. `tweets/<competitor_a>.md`, `tweets/<competitor_b>.md`)
  - any scraper tooling used to refresh those corpora