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 synthesisengineering/synthesis-skills --skill synthesis-content-framinggit clone --depth 1 https://github.com/synthesisengineering/synthesis-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/synthesisengineering/synthesis-skills/synthesis-content-framing)<a href="https://agentmods.dev/skills/synthesisengineering/synthesis-skills/synthesis-content-framing"><img src="https://agentmods.dev/badge/skills/synthesisengineering/synthesis-skills/synthesis-content-framing/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/synthesisengineering/synthesis-skills/synthesis-content-framing"><img src="https://agentmods.dev/badge/skills/synthesisengineering/synthesis-skills/synthesis-content-framing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 261 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00057 | $0.04431 |
| Opus 5 | $0.00028 | $0.02216 |
| Sonnet 5 | $0.00011 | $0.00886 |
| Haiku 4.5 | $0.00006 | $0.00443 |
Grade A, and why
synthesis-content-framing 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 10d 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 — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthesis Content Framing
A methodology for writing and publishing synthesis engineering and synthesis coding thought leadership. This skill covers topic selection, sophistication standards, engagement patterns, quality gates, and operational rules.
Document structure:
- Public Principles — Core concepts suitable for explanation and public content
- Quality Gates Summary — Overview of blocking gates every article must pass
- Operational Rules — Mechanics of content creation
For detailed gate criteria and checklists, see: references/quality-gates.md
Public Principles
Core Principle
Synthesis coding and synthesis engineering describe how expert humans direct AI agents to build software. The human is the leader, decision-maker, and ultimately responsible party. The AI is like a team of tireless, multi-domain expert programmers with vast knowledge — but the human is the boss.
Think of it like managing a team of superpowered beings: a skilled leader knows how to tap into each team member's expertise, learn from their knowledge, delegate effectively, and course-correct when needed — while remaining in charge and accountable.
The dynamic:
- Human: Expert leader with deep technical knowledge who makes final decisions
- AI: Tireless, knowledgeable team that executes, suggests, and sometimes errs
- Collaboration: Human directs, AI executes; human verifies, AI iterates
The human isn't a passive recipient of AI output — the human is actively directing, verifying, and learning.
The Expert Operator's Toolkit
Beyond directing and verifying, the expert human uses sophisticated techniques to extract maximum value from AI collaboration:
Intelligent Questioning
The human asks probing questions to arrive at better answers:
- "What assumptions are you making here?"
- "What could go wrong with this approach?"
- "How confident are you in this recommendation?"
- "What would you need to know to give a better answer?"
Challenging for Alternatives
What ships with it
2 files 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.
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.
- 10d ago First seen · 464 lines · 57 tokens per session scan A d57e621d356f
synthesis-content-framing is a skill published in the GitHub repository synthesisengineering/synthesis-skills (18 stars, last pushed today), licensed Apache-2.0. It adds 57 tokens to every session and 4,431 once invoked, about $0.0003 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 2026-08-30.
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