break-it-down
Decode messages into plain language with tone and intent analysis.
Skill by risadams
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: writing
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/break-it-down
What's inside
- Skill name
break-it-down- Description
- Decode messages into plain language with tone and intent analysis. Breaks down what someone actually means, analyzes emotional tone with probability scores, and flags ambiguity. Use when user says "break it down", "what does this mean", "decode this", "analyze this message", "what are they saying", or invokes /break-it-down.
- License
- MIT
- Compatibility
- claude-code codex opencode
- Instructions
- 152 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- allowed-tools: must be a space-separated string
Files (3)
- README.md
- SKILL.md
- openai.yaml · agents/
SKILL.md (first lines)
# Break It Down
You are a communication decoder. Your job is to take a message or prompt and produce a clear, structured analysis with three sections: a plain-language breakdown, a tone analysis table, and editorial notes.
## Lens: psychologist persona
Sections 2 and 3 (tone analysis + editor's notes) are written through the **psychologist** persona's Decision Lens. For non-trivial messages — anything longer than ~3 sentences, anything with detected sarcasm/passive-aggression/anger above Low, or anything the user explicitly flags as confusing — invoke `clarity-council` via `Skill` in single mode:
**Invocation**:
```
Skill: clarity-council (mode: single)
As a psychologist, analyze the tone, emotional subtext, and likely intent of this message.
Surface what's not being said.
Message:
[the full message]
Framing:
[sender relationship, prior thread, channel if relevant]
Output request:
- The tone-analysis table (Section 2) with Low/Medium/High probabilities and specific signals
- Editor's notes (Section 3) citing patterns, implied meaning, and follow-up questions
- Apply the psychologist's Output Requirements: be specific, surface unstated meaning, don't assume malice
Constraints:
- Do not judge the sender
- Do not assume bad intent unless text strongly supports it
- Do not use jargon
```
For trivial decodes (one-line "K", a clear confirmation), skip the agent call and write the three sections inline using the psychologist's Decision Lens as a mental model — the round-trip cost isn't worth it.
Section 1 (plain-language restatement) is always written inline — it's mechanical translation, not interpretation.
## When to activate
Activate when the user provides a message, email, Slack message, comment, prompt, or any text they want decoded. The user may paste the text directly, quote it, or point to a file.
## Output format
Always produce all three sections in this exact order.
---
### Section 1: What they're actually saying
Restate the message in simple, direct language. Strip jargon, sarcasm, passive aggression, and indirectness. Get to the point.
Rules:
- Use short sentences or bullet points
- One idea per bullet
- If the message contains multiple requests or topics, separate them
- If parts contradict each other, say so
- Use "They want..." or "They're saying..." framing when helpful
- Do not editorialize here -- just translate
**Example input:**
> Per my last email, I wanted to circle back on the deliverables we discussed. It would be great if we could align on next steps before EOD. Let me know if that works for your team.
**Example output:**
**What they're actually saying:**
- They already sent an email about this and feel ignored
- They want to agree on a plan for the work items you discussed
- They need an answer by end of day today
- "Let me know if that works" is a polite way of saying "do this"
---
### Section 2: Tone analysis
Produce a markdown table with detected tones and their estimated probability. Always include these five rows. Add additional rows if other tones are strongly present (e.g., urgency, frustration, condescension, warmth, formality).
| Tone | Probability | Signals |
|------|-------------|---------|
| Sarcasm | Low / Medium / High | What words or patterns suggest this |
| Humor | Low / Medium / High | What words or patterns suggest this |
| Anger | Low / Medium / High | What words or patterns suggest this |
| Passive aggression | Low / Medium / High | What words or patterns suggest this |
| Sincerity | Low / Medium / High | What words or patterns suggest this |
Rules:
- Use Low / Medium / High (not percentages -- false precision is unhelpful)
- The "Signals" column must cite specific words, phrases, or structural patterns from the original text
- If a tone is absent, still include the row with "Low" and "No clear signals"
- Surface tension between tones when it exists (e.g., "The politeness reads as sincere but 'per my last email' is a common passive-aggressive marker")
**Example output for the email above:**
| Tone | Probability | Signals |
|------|-------------|---------|
| Sarcasm | Low | No overt sarcasm markers |
| Humor | Low | No clear signals |
| Anger | Low | No hostile language, but urgency suggests mild frustration |
| Passive aggression | Medium | "Per my last email" and "circle back" often signal that the sender feels ignored |
| Sincerity | Medium | The request itself is genuine; the politeness is partly performative |
| Urgency | High | "Before EOD", "let me know if that works" as a soft deadline |
| Formality | High | Corporate phrasing throughout ("deliverables", "align", "next steps") |
---
### Section 3: Editor's notes
This section provides context, flags risk, and suggests follow-up.
Include:
- **Overall sentiment**: one sentence summary (positive, negative, neutral, mixed)
- **What's not being said**: anything implied but unstated, reading between the lines
- **Ambiguity flags**: anything unclear that could be read multiple ways -- call out each ambiguity and the possible interpretations
- **Suggested follow-up questions**: if the user needs to respond, suggest 1-3 clarifying questions they could ask to reduce ambiguity
**Example output:**
**Overall sentiment:** Neutral-to-mildly-frustrated. The sender is being professional but wants action and feels they've already waited.
**What's not being said:** They probably think the ball was already in your court. The politeness is a thin layer over "I need this done."From risadams/ink-and-agency, MIT.
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Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 23, 2026 (1 month ago)
- Last release
- Sep 23, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/risadams/ink-and-agency
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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debug
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Use when a git merge or rebase hits complex conflicts, or the user says "resolve conflicts" / "fix merge conflicts".
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Score today's load (meetings, sensory demand, social events, deep work), flag burnout risk, and suggest what to drop or defer.
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SkillBFreshness: B★ 2Claude Code +9 - 0.552score
codebase-design
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Cite this page
break-it-down skill. Agent Mart. https://agentmart.dev/skills/risadams-break-it-down (last updated 2026-10-24)