linkedin-engagement

$npx mdskill add alirezarezvani/claude-skills/linkedin-engagement

From a standing start your posts reach almost nobody, and publishing harder does not fix it. A substantive comment on a post that already has an audience puts your name, headline, and a paragraph of thinking in front of people already reading about your subject, for six minutes of work — the cheapest distribution on the platform, and the most badly used.

SKILL.md

.github/skills/linkedin-engagementView on GitHub ↗
---
name: linkedin-engagement
description: Use when someone wants to grow reach through comments, replies, groups, or outreach on LinkedIn — a commenting roster, a connection request note, a DM or InMail, a networking plan, or a check on whether their outreach volume is safe. Triggers on "who should I engage with", "write a connection request", "cold DM", "LinkedIn outreach", "networking strategy", "how many invites can I send". Builds a weekly comment roster inside a real time budget, assembles one message at a time and refuses templates, and caps volume against LinkedIn's limits. Nothing is ever sent.
license: MIT
metadata:
  version: 1.0.0
  author: Alireza Rezvani
  category: marketing
  updated: 2026-08-25
---

# LinkedIn Engagement — comments first, outreach second

From a standing start your posts reach almost nobody, and publishing harder does not fix it.
A substantive comment on a post that already has an audience puts your name, headline, and a
paragraph of thinking in front of people already reading about your subject, for six minutes
of work — the cheapest distribution on the platform, and the most badly used.

**Nothing here sends anything** — no credentials, no API calls. Automated connecting,
messaging, commenting, liking, and sharing are prohibited by User Agreement §8.2.

## Workflow

**1. Build the comment roster.** Name 8-12 accounts they would read anyway, with audience
overlap and rough size tier, then:

```bash
python3 scripts/comment_target_planner.py --account "Priya Raman:5:4:larger" \
  --account "Tomas Lind:5:3:peer" --minutes-per-day 18 --output human
```

Tiers are `huge` (10x+, crowded), `larger` (2-10x, the best ratio), `peer` (~1x, where
reciprocity compounds), `smaller` (goodwill). The roster caps any account at twice a week —
commenting daily on one person reads as following them around — and keeps the huge tier under
half of any day. It builds the roster, never the comments: a generated comment is exactly
what §8.2 names, and it is recognisable anyway.

**2. Outreach — check the volume before writing anything.**

```bash
python3 scripts/outreach_volume_guard.py --invites 20 --pending 5 --minutes 120 \
  --acceptance 0.42 --output human
```

Exit 0 safe / 2 tight / 3 over a cap or the time budget / **4 refused** above 40 invitations
a day, because nobody reads that many profiles and writes that many specific lines. Pending
invitations count against the weekly limit (observed around 100), and acceptance below 20% is
a stop signal — it is the pattern LinkedIn reviews, and the targeting is wrong.

**3. Write one message, for one person.**

```bash
python3 scripts/outreach_message_builder.py --type connection \
  --recipient "Priya" --specific-line "..." --reason "..." --output human
```

It refuses without a person-specific line, refuses an ask in a first-touch connection note,
and enforces the 200-character cap (300 with `--premium`). In large third-party samples a
note barely moves acceptance (~26.4% either way) but roughly doubles the post-accept reply
rate: **the note earns the conversation, not the meeting.**

**4. The order that works.** Comment on their work for two weeks. Then invite, referencing
something specific from that reading. Then, after acceptance and a pause, ask once, small.

## Rules

- **Nothing is auto-sent.** Ever. The user pastes and sends, one person at a time.
- **No engagement pods.** Coordinated reciprocal commenting is inauthentic engagement under
  §8.2 regardless of who pressed the key. Build a real reciprocity list instead.
- **Every message carries a line that could only have been written for that person.**
- **Never paste the same comment twice.** Identical comments at volume are the definition of
  the thing §8.2 prohibits.
- **One follow-up, a week later, only with something new to say.**
- **Agreement is not a comment.** Add the counter-example, the number, or the case where it
  breaks — or skip the slot.

## Scripts

| Script | Role |
|---|---|
| [`scripts/comment_target_planner.py`](scripts/comment_target_planner.py) | Weekly roster from scored accounts, inside a time budget, with per-account and per-tier caps. |
| [`scripts/outreach_message_builder.py`](scripts/outreach_message_builder.py) | Assembles one message; refuses templates, premature asks, and 14 dead phrases. |
| [`scripts/outreach_volume_guard.py`](scripts/outreach_volume_guard.py) | Caps invitations against the observed weekly limit, pending backlog, acceptance floor, and the hours available. |

## References and assets

- [`references/comment_strategy.md`](references/comment_strategy.md) — tiers, what a comment competes on, pods, replying to your own posts (7 sources)
- [`references/outreach_ethics_and_benchmarks.md`](references/outreach_ethics_and_benchmarks.md) — the benchmark numbers with their provenance, and the personalisation claim corrected (7 sources)

- [`assets/outreach_worksheet.md`](assets/outreach_worksheet.md) — fillable per-person prep sheet
- [`assets/example_outreach.json`](assets/example_outreach.json) — input shape for the builder

## Distinct from

- **`linkedin-content`** — writes posts. A comment is a different craft on a different budget.
- **`marketing-skill/cold-email`** — email. Different channel, law, and caps.
- **`business-growth/`, `commercial/`** — sales process and deal economics, not networking.

---
**Version:** 1.0.0

More from alirezarezvani/claude-skills

SkillDescription
a11y-auditAccessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
ab-test-setupWhen the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
ad-creativeWhen the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
adversarial-reviewerAdversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
aeoAnswer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
agent-decision-receiptsMint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
agent-designerUse when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
agent-harnessTurn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust, and refuse to close until everything is verified or explicitly waived. Use when you want an agent or subagent to pick up a goal and drive it to a verified close across one of this repo's 18 domains ('run this goal through the engineering harness', 'set up an agentic loop for marketing work', 'make the finance domain self-verifying'). NOT for authoring Claude Code Workflow-tool .js scripts (workflow-builder), N-agent tournaments on one task (agenthub), single-file metric optimization (autoresearch-agent), or discovering published loop recipes (loop-library).
agent-launcher-orchestratorUse when a user wants to build, launch, grade, or schedule a Claude Managed Agent (CMA) in their own Anthropic account — "build me an agent", "launch this as a managed agent", "run this on a schedule", "grade my agent against a rubric", "set up a nightly worker". Reads the per-session goal (./my-agent/goal.json), routes deterministically to one of five phase sub-skills (interview → stage-launch → grade-iterate → run-without-you → wrap-up) via goal_router.py, and compiles the goal+phase into an execution shape (single-pass workflow / bounded grade→iterate loop / recurring cron deployment loop) via loop_compiler.py. Forks context so heavy intake (build sheets, payloads, eval cases) stays out of the parent thread. All launches are emitted as BYOK curl the user runs with their own key; no tool makes API calls. Inspired by anthropics/launch-your-agent (Apache-2.0). Distinct from engineering/agent-harness (generic domain loop) and engineering/write-a-skill (authors Claude Code skills, not CMAs).
agent-memoryUse when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.