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.
npx skills add sam-dumont/claude-skills --skill technical-blog-postgit clone --depth 1 https://github.com/sam-dumont/claude-skillsWrote 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/sam-dumont/claude-skills/technical-blog-post)<a href="https://agentmods.dev/skills/sam-dumont/claude-skills/technical-blog-post"><img src="https://agentmods.dev/badge/skills/sam-dumont/claude-skills/technical-blog-post/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/sam-dumont/claude-skills/technical-blog-post"><img src="https://agentmods.dev/badge/skills/sam-dumont/claude-skills/technical-blog-post.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00058 | $0.01916 |
| Opus 5 | $0.00029 | $0.00958 |
| Sonnet 5 | $0.00012 | $0.00383 |
| Haiku 4.5 | $0.00006 | $0.00192 |
Grade A, and why
technical-blog-post scanned grade A with 1 finding 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.
Asks the agent to reveal its instructionslowSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
The blog template displays the hero image above the article content with a collapsible "AI-generated image — show prompt" caption that reveals the `heroImagePrompt` text. This is intentional transparency about AI-generat Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Blog Post from Raw Data
Overview
A process skill for turning raw project material into a structured technical blog post. Complements sams-voice (which handles tone and language): this skill handles structure and workflow.
REQUIRED: Always invoke sams-voice skill alongside this one. Voice = how it sounds. This skill = how it's organized.
When to Use
- Sam asks to write a blog post about a project
- Raw source material exists (research notes, session logs, drafts, code)
- The post is technical: "I built/decoded/reverse-engineered/automated X"
When NOT to Use
- Opinion pieces or commentary (no raw data to process)
- Short announcements (no structure needed)
- Non-technical content
Process
Phase 1: Gather
Find and read all source material in the project repo. Look for:
| Source Type | Where to Find | What It Contains |
|---|---|---|
| README | Root | Project summary, usage, results |
| Research notes | RESEARCH_NOTES.md, docs/ |
Chronological narrative of discoveries |
| Format specs | *_SPEC.md, *_FORMAT.md |
Technical details, struct layouts |
| Session logs | SESSION_LOG.md |
Claude Code session summaries |
| Drafts | .nocommit/, docs/drafts/ |
Reddit, LinkedIn, or blog drafts |
| Code | src/, *.py, *.go |
Implementation details |
| Tests | tests/, *_test.* |
Validation approach, edge cases |
| Binary data | *.hex, hex dumps in notes |
Raw data examples |
Key: Don't just read: extract the narrative arc. What was the problem? What failed? What worked? What was surprising?
Phase 2: Extract the Story
Pull out the six-beat narrative from the source material:
- The hook: What's the one-sentence version? (e.g., "Five years of telemetry trapped in a proprietary format")
- The problem: Why did this matter? What was at stake? What failed before?
- The approach: How was it tackled? What was the methodology? What tools?
- The discoveries: What were the key technical findings? Order by "aha moment", not chronology.
- The validation: How was each discovery confirmed? Real numbers, cross-checks.
- The result: What was delivered? Links, numbers, concrete output.
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 · 184 lines · 58 tokens per session scan A a02df205db18
technical-blog-post is a skill published in the GitHub repository sam-dumont/claude-skills (39 stars, last pushed 6mo ago), licensed MIT. It adds 58 tokens to every session and 1,916 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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