social-performance-review

social-performance-review is a skill for Claude Code, Codex from stevenflanagan1/social-ai-team. It costs 73 tokens per session (3,352 once invoked), scanned A, original, no licence file.

A monthly review tool for small and medium-sized businesses that examines social-media results from Instagram, LinkedIn, Facebook, or TikTok. It looks at individual posts and whole accounts.

In plain words
What is it for?
Use it to analyse CSV exports or other supported data, create a client-ready performance report, and improve the content calendar.
Why use it?
It removes the need to interpret exported social-media data manually. It explains what worked, what did not, and why, then turns the findings into content-planning recommendations.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to analyse CSV exports or other supported data, create a client-ready performance report, and improve the content calendar.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/stevenflanagan1/social-ai-team/social-performance-review
Install

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.

Any agent
npx skills add stevenflanagan1/social-ai-team --skill social-performance-review
Clone the repo
git clone --depth 1 https://github.com/stevenflanagan1/social-ai-team

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for social-performance-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/stevenflanagan1/social-ai-team/social-performance-review/github.svg)](https://agentmods.dev/skills/stevenflanagan1/social-ai-team/social-performance-review)
Your own site
<a href="https://agentmods.dev/skills/stevenflanagan1/social-ai-team/social-performance-review"><img src="https://agentmods.dev/badge/skills/stevenflanagan1/social-ai-team/social-performance-review/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.

agentmods 80×15 button for social-performance-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/stevenflanagan1/social-ai-team/social-performance-review"><img src="https://agentmods.dev/badge/skills/stevenflanagan1/social-ai-team/social-performance-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,352 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00073 $0.03352
Opus 5 $0.00036 $0.01676
Sonnet 5 $0.00015 $0.00670
Haiku 4.5 $0.00007 $0.00335

Measured 13d ago against content hash 514c042eb127, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

social-performance-review 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 13d 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.

skills/social-performance-review/SKILL.md · 349 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

1 file 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.

Changes

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.

  1. 13d ago First seen · 349 lines · 73 tokens per session scan A 514c042eb127

Subscribe to this mod's changes

social-performance-review is a skill published in the GitHub repository stevenflanagan1/social-ai-team (212 stars, last pushed 19d ago), with no licence file. It adds 73 tokens to every session and 3,352 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

CSOC Operations & Playbook Automation

SOC alert triage, incident playbook automation, escalation workflows, shift reporting, and SOC KPI tracking.

Masriyan/Claude-Code-CyberSecurity-Skill · 28 tokens

pkpd-modeling

Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…

K-Dense-AI/scientific-agent-skills · 273 tokens

biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…

K-Dense-AI/scientific-agent-skills · 76 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

neuropixels-analysis

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when…

K-Dense-AI/scientific-agent-skills · 98 tokens

onekgpd

Query the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals…

K-Dense-AI/scientific-agent-skills · 143 tokens