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 algo-social-influencegit 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/algo-social-influence)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-social-influence"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-social-influence/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/algo-social-influence"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-social-influence.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 | $0.00067 | $0.00889 |
| Opus 5 | $0.00034 | $0.00445 |
| Sonnet 5 | $0.00013 | $0.00178 |
| Haiku 4.5 | $0.00007 | $0.00089 |
Grade A, and why
"algo-social-influence" 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
92% identical to algo-social-influence — 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Influence Measurement
Overview
Influence scoring evaluates an account's ability to drive actions (engagement, sharing, conversions) beyond mere reach. Combines reach, resonance (engagement depth), and relevance (topical authority). Computes as weighted composite score.
When to Use
Trigger conditions:
- Evaluating and comparing influencers for marketing campaigns
- Building an influence scoring or ranking system
- Assessing brand ambassador effectiveness
When NOT to use:
- When measuring content virality dynamics (use viral spread models)
- When computing basic engagement rates (use engagement rate calculator)
Algorithm
IRON LAW: Follower Count ≠ Influence
Influence requires ENGAGEMENT. An account with 1M followers and
0.01% engagement rate has less influence than one with 10K followers
and 5% engagement. Measure: reach × engagement rate × relevance.
Phase 1: Input Validation
Collect per account: follower count, avg likes/comments/shares per post, posting frequency, audience demographics, topic categories. Gate: Minimum 20 recent posts for stable metrics.
Phase 2: Core Algorithm
- Reach score: Normalize follower count to log scale (diminishing returns)
- Engagement score: (avg engagements / followers) × 100, weighted by type (share > comment > like)
- Relevance score: Topic overlap between influencer content and target campaign
- Composite: Influence = w₁×Reach + w₂×Engagement + w₃×Relevance (weights tuned per campaign goal)
- Adjust for: audience authenticity (bot follower %), post frequency consistency
Phase 3: Verification
Spot-check: do high-scoring accounts actually drive actions? Cross-reference with historical campaign performance data if available. Gate: Top-ranked accounts have demonstrable engagement history.
Phase 4: Output
Return ranked influence scores with component breakdown.
Output Format
{
"rankings": [{"account": "@handle", "influence_score": 82, "reach": 75, "engagement": 90, "relevance": 85}],
"metadata": {"accounts_analyzed": 50, "weights": {"reach": 0.2, "engagement": 0.5, "relevance": 0.3}}
}
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 · 85 lines · 67 tokens per session scan A 83e2d043304d
"algo-social-influence" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 889 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to algo-social-influence, differing in 8 lines, and is treated as a copy.
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