Borrowing it
Nothing to install: this file belongs to hollandkevint/thinkhaven. 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/hollandkevint/thinkhaven/main/.claude/commands/kevinCreate/tasks/refine-messaging.mdgit clone --depth 1 https://github.com/hollandkevint/thinkhavenWrote 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/commands/hollandkevint/thinkhaven/refine-messaging)<a href="https://agentmods.dev/commands/hollandkevint/thinkhaven/refine-messaging"><img src="https://agentmods.dev/badge/commands/hollandkevint/thinkhaven/refine-messaging.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01235 |
| Opus 5 | $0.00000 | $0.00617 |
| Sonnet 5 | $0.00000 | $0.00247 |
| Haiku 4.5 | $0.00000 | $0.00123 |
Grade A, and why
refine-messaging 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/refine-messaging Task
When this command is used, execute the following task:
Refine Messaging
Task Overview
Polish and optimize content messaging for maximum clarity, impact, and Kevin's authentic voice.
Prerequisites
- Draft content ready for refinement
- Understanding of Kevin's writing principles
- Target audience clearly defined
Steps
1. Voice Authenticity Check
ELICIT: true Format: structured
Let me analyze the current messaging:
- Content to Refine: [Paste the content that needs refinement]
- Target Platform: LinkedIn post, blog article, or other?
- Primary Goal: What should this content accomplish?
- Audience: Who specifically is this for?
2. Kevin's Voice Assessment
I'll evaluate against Kevin's established principles:
Voice Markers:
- Sounds like Kevin in conversation
- Direct without unnecessary complexity
- Practical rather than theoretical
- Includes personal experience/perspective
- Slightly contrarian when appropriate
Red Flags:
- Generic business advice that could come from anyone
- Corporate jargon or buzzwords
- Overly promotional or self-serving
- Abstract concepts without concrete examples
- Trying too hard to sound impressive
3. Clarity and Simplicity Audit
ELICIT: true Format: line-by-line
I'll go through the content systematically:
Sentence Structure:
- Identify overly complex sentences
- Find passive voice constructions
- Highlight jargon or unnecessary complexity
- Note where active voice would improve clarity
Paragraph Flow:
- Check for logical progression
- Ensure each paragraph has a clear point
- Verify smooth transitions between ideas
- Confirm scannable format
4. Value Proposition Strengthening
I'll ensure the core value is crystal clear:
Value Clarity:
- Is the main benefit obvious within first 2 sentences?
- Can readers immediately understand "what's in it for me"?
- Are takeaways specific and actionable?
- Does it solve a real problem readers face?
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 · 219 lines · 0 tokens per session scan A 1daf030f6b06
refine-messaging is a command published in the GitHub repository hollandkevint/thinkhaven (5 stars, last pushed 8d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,235 tokens. 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 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.