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 inklate/social-skills --skill social-auditgit clone --depth 1 https://github.com/inklate/social-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/inklate/social-skills/social-audit)<a href="https://agentmods.dev/skills/inklate/social-skills/social-audit"><img src="https://agentmods.dev/badge/skills/inklate/social-skills/social-audit/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/inklate/social-skills/social-audit"><img src="https://agentmods.dev/badge/skills/inklate/social-skills/social-audit.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 | $0.00181 | $0.01847 |
| Opus 5 | $0.00090 | $0.00924 |
| Sonnet 5 | $0.00036 | $0.00369 |
| Haiku 4.5 | $0.00018 | $0.00185 |
Grade A, and why
social-audit 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.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit the profile and content history the user gives you against their stated goals, and hand back a prioritized gap list with a 3-move action plan.
Context
Read social-context.md at the project root (also check .agents/social-context.md) for the ## Goals, ## Pillars, ## Audience, and ## Cadence sections — the audit measures reality against these, so they matter more here than in any other task. If it's missing, offer to run the social-context skill first, but don't block — ask 2–3 quick inline questions and proceed:
- What's the goal — leads, audience growth, hiring, authority?
- What pillars did you intend to post about?
- What cadence did you intend to keep?
Workflow
- Collect the evidence. Ask the user to paste what they have:
- recent posts, text plus dates — ideally the last 20–30;
- the bio/profile text, the link, and what's pinned;
- any numbers they can report: follower trend, typical impressions/likes, their best and worst post. Work only from what's pasted. State clearly up front what you cannot assess without more — e.g. "without impression data I can score your hooks but not your reach; paste your top and bottom 5 posts by impressions if you have them." Never fill an evidence gap with a guess.
- Score posting consistency. From the post dates, compute actual posts-per-week and compare to the intended cadence from context. Flag gaps longer than 2× the intended interval, and flag clustering — five posts in three days, then two weeks of silence, is not a 2.5/week cadence. Check this dimension first: consistency gaps explain "flat" more often than content quality does, and every other score is noisy until cadence is stable.
- Score pillar balance. Tag each pasted post with its pillar, or "off-pillar" if it fits none. Report the actual distribution against the intended pillars, with counts. Be specific and honest: "your last 12 posts are all pillar #1" — never soften it to "consider diversifying your content". Both failure modes are findings: single-pillar collapse and off-pillar drift.
- Score hook quality. Take the first line of each of the last N posts (N = what they pasted, cap 20) and score each 0–2:
- 0 — label or throat-clearing: "Some thoughts on hiring." / "I've been meaning to write this."
- 1 — clear topic, no tension: "How we run our onboarding."
- 2 — specific claim, tension, or curiosity gap that survives truncation: "Our onboarding had a 60% drop-off at step 2. One email fixed it."
Report the average, then quote the worst three verbatim with rewrites — the rewrites teach more than the scores do. Then check fit: flag any hook aimed at the wrong reader for the
## Audiencein context — a strong hook for the wrong audience still misses.
- Score format mix. Count the formats used — text post, thread, carousel/document, image, video/Reel — against what the platform rewards and what the goal needs. Two failure modes, both findings: one-format monoculture, and format-chasing where nothing is tried twice so nothing can be learned.
- Read the engagement pattern — from their numbers only. If the user reported numbers, look for the shape:
- which pillar and format overlap their best posts;
- whether engagement concentrates in a few spikes or sits flat across everything;
- replies-vs-likes ratio if known — replies signal resonance, likes-only signals passable-but-skippable. If they reported nothing, write "unknowable from pasted data" for this dimension and move on. Do not infer engagement from post text.
- Audit the profile itself. A growth problem is often a conversion problem — people arrive from a good post and bounce off a vague bio. Check line by line:
- first line of the bio: does it name the audience from
## Audienceand the offer from## Positioning— who it's for and what they get — or is it a job title and three emoji? - credibility: any evidence — numbers, names, track record?
- the link: present, working destination described, matched to the goal?
- pinned post: their best converter, or just their newest?
- profile photo and banner: present and legible at feed size, as described?
- first line of the bio: does it name the audience from
- Diagnose against the goal. Connect the findings to the stated goal, not to generic best practice: flat followers + solid consistency + weak hooks is a top-of-funnel problem; good reach + no leads is a bio/CTA problem; erratic cadence means nothing else is measurable yet — fix that before optimizing anything. Name the single biggest lever explicitly, in one sentence.
- Build the gap list. Every finding as one line: severity (high/med/low), the evidence (a quote, count, or date range from their own data), and the fix. Order by expected impact on their goal, not by ease of fixing.
- Write the 3-move action plan. Exactly three moves, biggest lever first, each concrete enough to start this week — "rewrite your bio's first line to name the audience; here's a draft", not "improve your profile". Wherever a move has a draft or example, include it inline.
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.
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.
- 13d ago First seen · 91 lines · 181 tokens per session scan A 6b523586776f
social-audit is a skill published in the GitHub repository inklate/social-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 181 tokens to every session and 1,847 once invoked, about $0.0009 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 2026-08-31.
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