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Answers discussion #934, which asked for a strategic assistant for growing a LinkedIn presence organically rather than a post generator. Six skills under marketing/linkedin/: an orchestrator (context: fork) plus profile, strategy, content, engagement, and analytics lanes. 17 stdlib-only tools, 15 references, 2 agents, 8 /cs:* commands. The design constraint is the differentiator: no LinkedIn credentials, no API calls, no scraping, nothing auto-sent. Automated posting, connecting, and commenting are prohibited by LinkedIn's User Agreement 8.2, and a restricted account ends a compounding asset. linkedin_policy_gate.py runs before any drafting and refuses seven request classes — automation, scraping, engagement pods, bulk messaging, fake identity, fabricated proof, named third-party automation platforms — each carrying the policy anchor and a compliant substitute, so the gate never just says no. Refusals are real rather than advisory. A cadence under 90 minutes a week returns a comment-only plan instead of a schedule that dies in week five. A newsletter whose six-month cost exceeds the budget is refused before the promise is made. An experiment needing more posts than a quarter allows is reported infeasible rather than quietly re-sized. The pattern miner refuses to test anything below 10 posts and reports NOTHING_SURVIVED as a finding. Evidence discipline: two widely repeated claims are corrected rather than propagated. The "personalised note triples acceptance" claim is not supported by the largest samples (acceptance is near-identical either way, ~26.4%); what a note moves is the post-accept reply rate (~5.4% to ~9.4%), which is why the message builder refuses an ask in a first-touch note. The ~19% in-body link reach reduction has never been confirmed by LinkedIn as a penalty and has a plausible dwell-time explanation, so it is a warning rather than a block. Every reference carries per-claim confidence levels. Accessibility is a blocking lint finding: Unicode pseudo-bold is announced by screen readers as mathematical symbols and is not indexed by search. All six SKILL.md files are 6/6 PASS on the write-a-skill checklist. Every tool supports --help, --sample, and --output json with typed exit codes. Counters: skills 380 -> 386; plugins 96 -> 97; tools 706 -> 723; refs 823 -> 838; agents 114 -> 116; commands 138 -> 146 (derive_counters.py --check). Also syncs three previously-merged skills (agent-memory, hivemind, skill-doctor) into the .hermes/ and .vibe/ mirror trees, which had drifted behind .codex/. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JSPxUHU6utqme7qC6EwHEh
6.3 KiB
linkedin — organic LinkedIn presence, with the rules enforced in code
A strategic assistant for building an authentic LinkedIn presence over months, not a post generator. Built in response to discussion #934.
The design constraint that shapes everything else: this plugin holds no LinkedIn credentials, makes no API calls, scrapes nothing, and sends nothing. Every output is text the account holder posts themselves. Automated posting, connecting, commenting, and liking are prohibited by LinkedIn's User Agreement §8.2, and a restricted account ends a compounding asset that took months to build. So the refusals are in code, at the front of every lane, and each one names a compliant substitute.
/cs:linkedin # gate + route
/cs:grill-linkedin # five forcing questions before the work starts
/cs:linkedin-profile # headline, About, whole-profile audit
/cs:linkedin-plan # brief, pillars, cadence, newsletter
/cs:linkedin-post # format, draft, lint
/cs:linkedin-repurpose # article/talk -> posts, with a reuse ledger
/cs:linkedin-outreach # comment roster + one message at a time
/cs:linkedin-analyze # your own export, analysed honestly
What is here
| Skill | Tools | Does |
|---|---|---|
linkedin-skills (orchestrator, context: fork) |
2 | Policy gate (ALLOW / CONSTRAIN / REFUSE) then a deterministic five-lane router with cross-lane prerequisites |
| linkedin-profile | 3 | Headline scored on five dimensions vs the 220-char cap; whole profile 0-100 with fixes ranked by points per hour; About section assembled to survive the fold |
| linkedin-strategy | 3 | Positioning brief validator; cadence priced in minutes with a 90-minute floor; newsletter eligibility + six-month sustainability gate |
| linkedin-content | 3 | Post linter (mechanics / hook / integrity / accessibility); format picker; repurpose splitter with a reuse ledger |
| linkedin-engagement | 3 | Capped commenting roster; message builder that refuses templates; volume guard that refuses automation-shaped plans |
| linkedin-analytics | 3 | Median/MAD describer; four-gate permutation pattern miner; experiment planner |
Plus 2 agents, 8 commands, 15 references (7 sources each), 11 assets.
The parts that are different
The policy gate runs before anything is drafted. Seven refusal rules covering automation, scraping, engagement pods, bulk messaging, fake identity, fabricated proof, and named third-party automation tools — each with the User Agreement clause and a substitute that achieves the same goal legitimately. A REFUSE outranks any route.
Evidence is graded, and two popular claims are corrected. Every quantitative claim carries 🟢 (LinkedIn-official), 🟡 (third-party study), or 🔴 (folklore, named as folklore).
- The "a personalised note triples acceptance" claim is not supported by the largest samples,
which show acceptance close to identical either way (~26.4%). What a note actually moves is
the post-accept reply rate (~5.4% → ~9.4%). That changes what the note should say: it
earns the conversation, not the meeting — which is why
outreach_message_builder.pyrefuses an ask in a first-touch connection note. - The ~19% in-body link reach reduction has never been confirmed by LinkedIn as a penalty and has a plausible non-punitive explanation via dwell time. It is a warning, not a block.
The analytics refuse to over-conclude. pattern_miner.py puts every candidate through four
gates: a group-size floor, a 15% minimum relative effect, a seeded permutation test, and a
multiple-comparisons accounting that reports how many candidates would pass on noise alone.
Below 10 posts it refuses to test anything. NOTHING_SURVIVED is the most common honest
answer and is reported as a finding, not a failure.
Accessibility is a blocking finding, not a footnote. Unicode pseudo-bold — the output of "bold text generators" — is a blocking lint failure, because screen readers announce those characters as mathematical symbols and LinkedIn search does not index them as words.
Refusals are refusals. A cadence under 90 minutes a week returns a comment-only plan rather than a schedule that will be abandoned in week five. A newsletter whose six-month cost exceeds the budget is refused before the promise is made. An experiment that needs more posts than a quarter allows is reported as infeasible rather than quietly re-sized.
Quick start
# See every tool run with zero configuration
python3 skills/linkedin-skills/scripts/linkedin_policy_gate.py --sample --output human
python3 skills/linkedin-profile/scripts/headline_scorer.py --sample-weak --output human
python3 skills/linkedin-strategy/scripts/cadence_planner.py --sample --output human
python3 skills/linkedin-content/scripts/post_linter.py --sample --output human
python3 skills/linkedin-engagement/scripts/outreach_volume_guard.py --sample --output human
python3 skills/linkedin-analytics/scripts/pattern_miner.py --sample --output human
Every tool supports --help, --sample, and --output json, uses the standard library
only, and returns typed exit codes so an agent can branch on the verdict rather than parse
prose.
Distinct from
marketing-skill/x-twitter-growth— X/Twitter mechanics. Different platform, different format economics, different rules.marketing-skill/social-content,social-media-manager— multi-platform brand calendars. This is one person's own presence on one platform, in depth.marketing-skill/social-media-analyzer— cross-platform campaign reporting. This reads your own LinkedIn export and refuses to conclude below 10 posts.marketing-skill/cold-email— different channel, different law, different caps.
Provenance
Requested in discussion #934.
Refusal rules derive from LinkedIn's published User Agreement §8.2, the Prohibited Software
and Extensions help article, and the Professional Community Policies. No LinkedIn code, data,
or proprietary material is included. See .claude-plugin/authoring-notes.json.
Version: 1.0.0 · MIT · 6 skills · 17 stdlib tools · 15 references · nothing auto-sent