social-reply

social-reply is a skill for Claude Code from inklate/social-skills. It costs 180 tokens per session (1,848 once invoked), scanned A, original, MIT.

A workflow for sorting batches of social-media comments, mentions, and private messages from LinkedIn, X, Instagram, Facebook, or Threads. It identifies which items deserve replies and drafts responses in the requested voice.

In plain words
What is it for?
It is for triaging social conversations, preserving each author's platform and channel, flagging leads, dismissing trolls with reasons, and preparing reply drafts.
Why use it?
It removes the need to process every message manually and helps separate potential leads from irrelevant or abusive messages. It also accounts for differences between public replies and private messages.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the social-skills plugin — 14 skills shipped together

Good fit It is for triaging social conversations, preserving each author's platform and channel, flagging leads, dismissing trolls with reasons, and preparing reply drafts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/inklate/social-skills/social-reply
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 inklate/social-skills --skill social-reply
Clone the repo
git clone --depth 1 https://github.com/inklate/social-skills

Made for: Claude Code.

Or install social-skills, the plugin that ships this one along with the rest of its 14 skills.

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-reply

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/inklate/social-skills/social-reply"><img src="https://agentmods.dev/badge/skills/inklate/social-skills/social-reply.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 180 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,848 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 original 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 $0.00180 $0.01848
Opus 5 $0.00090 $0.00924
Sonnet 5 $0.00036 $0.00370
Haiku 4.5 $0.00018 $0.00185

Measured 11d ago against content hash 81d57042d703, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

social-reply 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 11d 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-reply/SKILL.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Turn a raw batch of comments, mentions, and DMs into a triaged worklist with on-voice reply drafts — leads flagged first, trolls dismissed with reasons.

Context

Read social-context.md at the project root (also check .agents/social-context.md) for the ## Voice rules and the ## Never list — replies must sound like the user and stay inside the red lines. Product facts (pricing, roadmap, what you may confirm) and who handles escalations are not in that file, so ask for them directly the first time they matter. If social-context.md is missing, offer to run the social-context skill first, but don't block — ask 2–3 quick inline questions and proceed:

  • How formal is your reply voice — emoji or none, first names or handles?
  • Anything you must never promise or confirm (roadmap dates, discounts, integrations)?
  • Who handles escalations (legal, refunds, press), and what's your next step for a warm lead — call link, DM, trial invite?

Workflow

  1. Parse the batch. Number every item. Preserve the author handle, platform, and channel (public comment vs DM) where given — the same words deserve a different reply in public than in private. If items are ambiguous or truncated, note it on the item rather than guessing intent.
  2. Bucket each item into exactly one of:
    • question — wants information;
    • praise — positive, no ask;
    • objection — pushback, skepticism, competitor comparison, complaint;
    • lead signal — buying intent: "is there a trial?", "does it work for teams?", pricing questions, "how do I get started", asking to be contacted;
    • troll/spam — bad faith, off-topic promotion, bait. Intent outranks form: a pricing question is a lead signal, not a question; a complaint from a paying customer is an objection, not a troll, no matter the tone.
  3. Order the worklist by priority: leads first, then questions, then objections, then praise; trolls/spam last. Within leads, order by strength of intent — "where do I sign up" beats "interesting, might look at this".
  4. Assign an action per item:
    • reply — a public answer adds value for onlookers too; default for questions, objections, and leads.
    • react-only — a like/heart is enough; default for short praise, where a written "Thank you so much!!" adds nothing.
    • DM — move to private: anything needing account details, pricing negotiation, or a lead worth a direct conversation. Still reply publicly first with one line ("Answered you in DMs — short version: yes") so onlookers see responsiveness.
    • ignore — trolls/spam, always with a one-line why addressed to the user, e.g. "engagement bait; replying boosts the thread's ranking, not yours".
  5. Draft replies where warranted. In the user's voice: short, human, specific to what the person actually said. The first line responds to their words, not a template. Banned: corporate padding ("Thanks for reaching out!", "Great question!"), exclamation-mark inflation, restating their question back at them, and signing off like an email. Public replies ≤ 2–3 sentences; DMs may run slightly longer and must end with one clear next step.
  6. Handle objections with substance. Concede what's true, correct what's false with a fact from context, never argue tone. If the objection is valid and context has no counter-fact, the honest reply is "fair — here's what we're doing about it", or a flag to the user that they must supply the answer. Never invent capabilities, dates, or policies to win a thread.
  7. Flag every lead prominently. Mark it LEAD at the top of the worklist with three parts:
    • the intent evidence — quote their exact words;
    • the suggested next step from context — send the call link, offer the trial, move to DM;
    • a reply draft that executes the step without being pushy: answer their actual question first, invite second.
  8. Escalate, don't draft, the dangerous ones. Legal threats, refund/chargeback disputes, safety or harassment reports, press/journalist inquiries, anything touching an individual's private data, and public accusations that could become a PR moment. Mark ESCALATE, give the one-line why, name who should handle it (from what the user told you; ask if you don't know), and draft at most a holding line ("Taking this seriously — following up with you directly") for cases where public silence would look worse than acknowledgment.
  9. Sanity-pass the drafts as a set. Read all the replies together. If ten of them open with the same word or lean on the same phrase, vary them — people read whole comment sections, and copy-paste warmth reads as neither warm nor human. Check the set against every voice rule and the Never list, and against every row of the Quality bar — the default action, reply length, and opener for each bucket, plus the hard rules.

Read the full file on GitHub · 89 lines

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. 11d ago First seen · 89 lines · 180 tokens per session scan A 81d57042d703

Subscribe to this mod's changes

social-reply is a skill published in the GitHub repository inklate/social-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 180 tokens to every session and 1,848 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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