What does this skill do, and when should you use it?
This skill plans the daily operating block for a founder-led B2B social-selling motion. It structures target-account tiers, time-boxed engagement, warm-touch-before-ask cadence, trigger-response plays, and a quarterly diagnostic. It is deliberately human-executed: no automated DMs, connection requests, or engagement. LinkedIn SSI is treated only as an estimated, vendor-defined input diagnostic, never as a target metric.
It reads user-provided target accounts, pipeline context, posts, engagement logs, platform exports, channel records, and fired B2B signals from social-pulse-monitor. It produces a time-boxed daily engagement specification, account tiers, comment quality bar, give:ask ledger, warm-touch threshold, response plays for funding, hiring, product-launch, and leadership-change signals, and a quarterly diagnostic template. Trigger evidence must be verified and labeled; cadence commitments are submitted as proposal events through registry-events.py. The skill does not automate social activity or outreach.
- A founder needs a repeatable 30–45 minute block for adding substantive value to target-account posts.
- A B2B seller needs a same-day response play after a target account raises funding or starts a hiring wave.
- A founder-led sales team wants explicit warm-touch evidence before making a one-to-one ask.
- A team wants to diagnose quarterly social-selling performance from engagement logs, replies, meetings, exports, and optional SSI data.
How do you install this skill?
- Static material does not establish that the referenced shared skills, scripts, registry proposal path, or connectors are available and functioning.
- Do not use LinkedIn SSI or community-derived volume norms as targets; manually verify every external signal before acting.
- Platform rules, account permissions, and network reachability must be checked separately for mainland-China channels; the skill does not automate engagement or messaging.
- Shell / CLI
- Network access
- Local filesystem
Python
Place the repository's social/host/social-selling-planner directory in an Agent Skills-compatible host. The repository README documents Claude Code installation with /plugin marketplace add aaron-he-zhu/aaron-marketing-skills followed by /plugin install aaron-marketing@aaron. It also documents npx skills add aaron-he-zhu/aaron-marketing-skills and git clone installation.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/social/host/social-selling-planner" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Build my daily social-selling block: 45 minutes, target accounts [list], platform LinkedIn (user exports only).
Provide a target-account list and platform, for example: "Build my daily social-selling block: 45 minutes, target accounts [list], platform LinkedIn (user exports only)." You can also provide a fired signal, such as a Series B, and request the trigger-response play. For the quarterly diagnostic, provide the engagement-block log, reply and meeting counts, platform exports, and optionally an SSI screenshot. If no target accounts or specific people posting for them are available, the skill stops with NEEDS_INPUT.
What are this skill's strengths and limitations?
- Separates substantive engagement, warm touches, and asks with an explicit estimated threshold of 3+ touches across 2+ weeks.
- Covers funding, hiring, product-launch, and leadership-change triggers.
- Requires human execution, source labeling, declared denominators, and compliance-aware handling.
- Provides handoffs to social-pulse-monitor, outreach-manager, and channel-registry.
- It cannot directly read or act on closed-platform activity such as LinkedIn engagement, connections, or DMs; users must provide exports or screenshots.
- The 30–45 minute norm, touch threshold, and signal-decay guidance are labeled estimated or community-derived rather than official platform standards.
- LinkedIn SSI has an undisclosed formula and is diagnostic only; one-to-one pitch/DM mechanics and cold-email sequences are out of scope.
- The supplied material gives no independent test results or platform-specific validation for this individual skill.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Social Selling Planner this page | 60 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| Advocacy Program Designer | 59 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| Community Participation Warmup Planner | 57 · Use with care | ★ 3k | 12d ago | Apache-2.0 |
| Social Measurement Loop | 56 · Use with care | ★ 3k | 12d ago | Apache-2.0 |
| Platform Norm Profiler | 60 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
Unlike social-pulse-monitor, this skill consumes trigger-watchlist results and turns them into engagement and response plans; social-pulse-monitor owns the watchlist. Unlike outreach-manager, it handles relationship-building cadence before an ask; outreach-manager handles one-to-one pitch, DM, and follow-up mechanics after the threshold. Cold email belongs to cold-outbound-sequencer.
How did FollowSkills review this skill?
The skill explicitly forbids mass-DM, automated connection requests, and automated engagement, requires human execution, treats supplied content as untrusted, asks before certain writes, and routes registry changes through proposal events. Six points are deducted because platform data, trigger signals, and external URLs remain part of the data flow; rollback and sensitive-data handling are not fully specified in this skill, and attribution relies mainly on labels rather than independent proof.
The contract, inputs, outputs, NEEDS_INPUT behavior, termination rules, thresholds, and abnormal-input boundaries are comparatively clear. Eleven points are deducted because it depends on several unprovided skills, scripts, and runtime state, with no skill-specific tests or static reproduction of key paths; under static calibration the score remains at or below 10.
The audience, founder-led B2B scenario, triggers, exclusions, and argument hint are clearly stated, and Chinese platforms are acknowledged. Three points are deducted because platform access classes and mainland-China reachability are not sufficiently demonstrated, while LinkedIn SSI and some overseas connectors may constrain practical use.
The evidence includes structured frontmatter, version 18.0.0, Apache-2.0 licensing, Quick Start examples, contract, data-source, handoff, save, and next-skill sections, plus repository-level CI and maintenance signals. Five points are deducted because this skill has no own changelog, explicit maintenance owner, or upgrade path, and important installation and troubleshooting details are delegated to unprovided shared documents.
The specified output includes a time-boxed block, account tiers, comment quality bar, warm-touch threshold, trigger plays, quarterly diagnostic, and handoff summary, giving it direct operational value. Nine points are deducted because it depends on user-supplied lists, logs, exports, and other skill outputs; no executed evidence for this skill's key paths is provided, and comparative benefit is not demonstrated.
The skill distinguishes Measured, User-provided, and Estimated data, requires trigger-source URLs and declared denominators, and the repository includes tests and CI material. Six points are deducted because those tests do not cover this skill's key paths, referenced shared protocols and external platform rules are not independently corroborated in the supplied material, and static review cannot establish runtime results.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →