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 charlieviettq/awesome-agent-skill --skill grad-agenda-settinggit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/grad-agenda-setting)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/grad-agenda-setting"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/grad-agenda-setting/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/charlieviettq/awesome-agent-skill/grad-agenda-setting"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/grad-agenda-setting.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 | $0.00097 | $0.00910 |
| Opus 5 | $0.00048 | $0.00455 |
| Sonnet 5 | $0.00019 | $0.00182 |
| Haiku 4.5 | $0.00010 | $0.00091 |
Grade A, and why
"grad-agenda-setting" 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 9d 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.
This is a copy
91% identical to grad-agenda-setting — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agenda-Setting Theory
Overview
Agenda-setting theory posits that mass media may not tell people what to think, but powerfully influences what they think about. By selecting, emphasizing, and repeating certain issues, media transfers issue salience from the media agenda to the public agenda.
When to Use
Trigger conditions:
- Analyzing how media coverage shapes public issue priorities
- Evaluating the relationship between media attention and public concern
- Designing strategic communication to elevate issue salience
When NOT to use:
- When analyzing HOW people think about issues (use framing theory instead)
- When studying long-term worldview formation (use cultivation theory instead)
- When examining minority opinion suppression (use spiral of silence instead)
Assumptions
IRON LAW: Media May Not Tell People WHAT to Think, But It Tells Them WHAT TO THINK ABOUT
Issue salience is transferred from media to public agenda. The MORE
coverage an issue receives, the MORE important the public perceives it
to be — regardless of objective importance. This operates at two levels:
1. First level: OBJECT salience (which issues matter)
2. Second level: ATTRIBUTE salience (which aspects of issues matter)
Methodology
Step 1: Identify Agendas
Define the media agenda (content analysis of coverage frequency/prominence) and public agenda (survey data on "most important problem").
Step 2: Measure Salience
Quantify issue salience on both agendas. Media: column inches, airtime, front-page placement. Public: survey rankings, social media volume.
Step 3: Analyze Transfer
Examine the correlation between media salience and public salience over time. Account for time lag (typically 4-8 weeks for traditional media).
Step 4: Assess Contingent Conditions
Evaluate moderators: need for orientation (relevance + uncertainty), obtrusiveness of issues, media credibility, audience characteristics.
Output Format
# Agenda-Setting Analysis: {Issue/Context}
## Media Agenda
- Issues ranked by salience: {list with coverage metrics}
- Time period: {dates analyzed}
- Sources: {media outlets examined}
## Public Agenda
- Issues ranked by perceived importance: {survey/social data}
- Measurement method: {MIP survey, social media analysis, etc.}
## Salience Transfer
- Correlation: {media-public agenda correlation}
- Time lag: {observed lag period}
- Direction: {media→public, public→media, or intermedia}
## Moderating Factors
- Need for orientation: {high/low and why}
- Issue obtrusiveness: {obtrusive vs unobtrusive}
## Implications
{Strategic recommendations based on findings}
What ships with it
3 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.
- 9d ago First seen · 90 lines · 97 tokens per session scan A ad4b1e4e5fba
"grad-agenda-setting" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 910 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to grad-agenda-setting, differing in 8 lines, and is treated as a copy.
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