linkedin-writer
LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add flaqai/backlink_skills --skill linkedin-writer
Maintenance
fresh
1d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
483
73/100 Quality · 74/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
483 GitHub stars
Repo activity
483 stars, 175 forks
Maintenance
1d since push
License
MIT
Install
npx skills add flaqai/backlink_skills --skill linkedin-writer
Install safety
standard package or runtime install path
Permission surface
filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Review before production
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is declared
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add flaqai/backlink_skills --skill linkedin-writer
- Policy
- review
- Human review
- yes
Trust and risk
- Trust
- 66/100
- Audit
- 81/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add flaqai/backlink_skills --skill linkedin-writerDo not use when
- teams that need a vendor-supported SLA
- production agents without a repository review
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
- Financial research output is not financial advice; require human review before any live investment decision
- The skill references external parent skill files (e.g., fact-check, humanization, R2 upload) that are not included in this submission; proper integration depends on those files being present.
Agent safety v2
57/100 · Review before install
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
medium
Browser automation
Skill may drive a browser or interact with web pages.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Database access
Skill may inspect schemas, query databases, or work with persistent stores.
- Financial research output is not financial advice; require human review before any live investment decision
Install targets
Install this skill in your agent workflow
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install flaqai-linkedin-writerAgent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20linkedin-writer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20linkedin-writer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/flaqai-linkedin-writer/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use linkedin-writer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20linkedin-writer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/flaqai-linkedin-writer/install
Install command: npx skills add flaqai/backlink_skills --skill linkedin-writer
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/flaqai-linkedin-writer/install
LLM text format
/api/skills/flaqai-linkedin-writer/install?format=text
Find alternatives
/api/skills/search?q=linkedin-writer&limit=3
Agent prompt
Use linkedin-writer for this task. Review https://www.openagentskill.com/api/skills/flaqai-linkedin-writer/install, then install with: npx skills add flaqai/backlink_skills --skill linkedin-writerRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/flaqai-linkedin-writer
LLM text
/api/registry/manifest/flaqai-linkedin-writer?format=text
Install alias
/api/registry/install/flaqai-linkedin-writer
Recommend
/api/registry/recommend?task=Use%20linkedin-writer%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 81/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Companion skill for Research agents
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 73/100 quality profile
- 12 OpenAgentSkill engagement events
review first
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Sandbox only
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO483 GitHub stars
Stars/forks activity
INFO483 stars, 175 forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d since push
License clarity
PASSMIT
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Strong candidate for agent workflows
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Use this skill in these scenarios
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Verify behavior
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Compare before you install
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Overview
--- name: linkedin-writer description: LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready packaging. Use when creating, outlining, researching, enriching, rewriting, humanizing, auditing, or packaging LinkedIn Articles, LinkedIn newsletters, LinkedIn long-form posts, LinkedIn thought leadership, LinkedIn B2B articles, LinkedIn 长文, LinkedIn 专栏, LinkedIn 话题调研, LinkedIn 商务内容, 去 AI 化编辑, or LinkedIn 发布包. ---
# LinkedIn Writer
## Goal
Turn a topic, product, argument, report, source bundle, or existing draft into a credible LinkedIn-native long-form article that helps a defined professional audience make a decision, understand a change, or improve how they work.
This skill reuses the parent `writer` workflow for fact checking, humanization, image packaging, and optional Cloudflare R2 delivery, but it does not treat LinkedIn as a generic SEO blog host or as Medium with a different publishing button.
LinkedIn-native writing prioritizes:
- a specific professional reader and work context; - a defensible point of view or useful decision framework; - current LinkedIn search and conversation signals; - expertise demonstrated through evidence, examples, and boundaries; - short, skimmable sections for busy readers; - a discussion-worthy close rather than a generic sales conclusion; - a complete native publishing pack, including LinkedIn SEO settings.
Default language follows the user's request. If the user gives no language, use the language of their source material or target audience.
## Format Routing
Use the requested format, not a blended default:
| Destination | Workflow | |---|---| | LinkedIn Article or LinkedIn newsletter edition | Use this skill in full | | LinkedIn short feed post only | Use the short-post rules and publishing pack in this skill; do not force a long article | | Google-first website article | Use `../SKILL.md` | | Medium article or third-party editorial essay | Use `../medium-writer/SKILL.md` | | Chinese WeChat Official Account article | Use `../wechat-writer/SKILL.md` |
If the user says only “LinkedIn article” or “LinkedIn long-form,” default to a native LinkedIn Article. If they already run a newsletter and provide its name or theme, package the piece as a newsletter edition. Do not claim a newsletter was created or published without direct evidence.
## Required References
Read each relevant file completely before acting:
- Topic discovery, LinkedIn search, trend expansion, and current seed topics: `references/linkedin-topic-research.md` - Google-to-LinkedIn discovery, business-depth enrichment, and final LinkedIn humanization: `references/linkedin-business-depth-and-humanization.md` - New article, rewrite, or reusable output format: `references/linkedin-article-template.md` - Article review, scoring, and revision gate: `references/linkedin-review-rubric.md` - Fact-heavy claims, comparisons, current products, or statistics: `../references/fact-check-and-style.md` - Final natural-language edit after factual and structural fixes: `../references/humanization.md` - Images, local paths, file packaging, and optional R2 delivery: `../references/output-packaging.md` - R2 upload tasks only: `../references/r2-image-upload.md` and `../references/r2-security.md`
Do not load unrelated references merely because they exist.
## Non-Negotiable Boundaries
1. Do not invent professional experience, product testing, customers, interviews, internal data, quotes, results, credentials, or events. 2. First-person events may appear only when the user supplied them for this task or they exist in an approved, attributable source package. 3. Do not turn LinkedIn search result counts, reactions, comments, or repeated phrases into search-volume claims. 4. Do not call a topic “trending,” “viral,” or “hot” without dated evidence. Use “recurring conversation,” “current topic seed,” or similarly bounded language when evidence is directional. 5. Do not mention or tag people and Pages merely to trigger notifications. Every suggested mention must have a content reason. 6. Do not convert a product announcement into disguised thought leadership. State affiliations, recommendations, and commercial relationships when they materially affect trust. 7. Do not use engagement bait such as “Agree?”, forced polls, empty controversy, or unrelated hashtags. Invite a concrete professional response. 8. Do not copy another LinkedIn creator's hook, framework, story, examples, distinctive phrases, or conclusion. Extract only topic signals and questions, then synthesize an original angle. 9. Do not claim publication, indexing, newsletter delivery, reach, or engagement from a completed local package. 10. Writing, generating images, uploading assets, and publishing externally are separate permission levels.
## LinkedIn Article Task Card
Before research or writing, create or infer a task card in 14 lines or fewer and save it as `linkedin-brief.md`:
- Publish as: personal profile / Company Page / unknown. - Format: standalone Article / newsletter edition / short feed post. - Professional audience: role, seniority, industry, and work situation. - Reader decision: what they should understand, compare, decide, or do. - Core thesis: one sentence the article must establish. - Expertise basis: supplied experience, verified sources, product knowledge, or editorial analysis. - Primary topic phrase: one natural phrase for LinkedIn and external search. - Related topic cluster: 4-8 entities, skills, problems, roles, or outcomes. - Conversation tension: trade-off, change, misconception, or unresolved question. - Business context: stakeholders, buying/approval path, economics, implementation, risk, and measurement dimensions that matter. - Evidence requirement: 3-6 claims that must be checked. - Target length: normally 900-1,800 words; adjust to the subject, not a platform myth. - CTA: discussion question, practical next step, subscription prompt, or disclosed product action.
Make conservative assumptions when details are missing. Ask only when audience, thesis, or authority to use personal experience is materially ambiguous.
## Working Modes
### Continuous mode (default)
Run `brief -> LinkedIn and Google-to-LinkedIn research -> business insight map -> evidence -> outline -> draft -> audit -> rewrite -> final humanization -> integrity recheck -> package` without pausing at every step. A request to “write an article” means deliver the reviewed local package.
### Topic-radar mode
When the user asks for hot topics, search ideas, or content planning, stop after the ranked topic map unless they also ask for an article. Do not draft ten shallow articles.
### Interactive mode
Pause at the topic shortlist or outline only when the user explicitly asks to choose first.
### Audit mode
If the user asks only for review, produce findings without overwriting the draft. If they ask to improve, preserve the original and apply fixes before re-auditing.
## End-to-End Workflow
### 1. Create an isolated article directory
Use:
```text writer/linkedin-writer/output/<article-slug>/ ```
This LinkedIn-specific output directory overrides the parent writer's default `writer/output/<article-slug>/` location. Prefer a short ASCII, hyphen-separated slug under 80 characters. Do not mix multiple campaigns in one directory.
Recommended working files:
```text linkedin-brief.md linkedin-topic-map.md linkedin-insight-map.md source-ledger.md outline.md draft.md article-linkedin.md linkedin-audit.md linkedin-publishing-pack.md image-plan.md ```
Create only the files the task needs. Keep `draft.md` separate from `article-linkedin.md` so an unreviewed draft cannot be mistaken for final copy.
### 2. Research LinkedIn search demand and conversation context
Read `references/linkedin-topic-research.md` completely.
Do not begin with a static list of broad trends. Build a query grid around:
```text core entity or skill × audience or role × work outcome × tension or decision × current change or timeframe ```
Use LinkedIn search suggestions and Posts results when available. Filter by recent date, content type, author industry/company, or source type when useful. Triangulate recurring questions with primary reports, official product or policy sources, credible industry research, customer questions, and the user's own content goals.
Then use Google to discover publicly indexed LinkedIn material with exact phrases, date operators, exclusions, and scoped queries such as:
```text site:linkedin.com/posts "<topic>" "<role or objection>" after:YYYY-MM-DD site:linkedin.com/pulse "<topic>" "<implementation, ROI, risk, or governance>" site:linkedin.com/company "<topic>" "<official case or report>" ```
Record Google results separately. Search snippets and LinkedIn creator claims are conversation signals, not automatically verified facts. Open the original page when possible, verify material claims elsewhere, and never copy a creator's hook, framework, structure, anecdote, or conclusion.
Record the exact query, date, filter, observed signal, and interpretation in `linkedin-topic-map.md`. Separate:
- **Observed:** directly visible search suggestion, repeated topic, question, format, or source. - **Inferred:** a possible reader need or angle derived from the observations. - **Verified demand:** use this label only when reliable demand data actually supports it.
Never imply that a topic is popular merely because it appears in one post or one search result.
### 3. Expand the topic before outlining
For the selected topic, create a useful professional topic cluster:
- Core concept: the named tool, skill, market shift, or decision. - Business outcome: time, quality, growth, cost, risk, hiring, retention, or customer value. - Role impact: what changes for practitioners, managers, executives, buyers, or candidates. - Implementation: workflow, prerequisites, governance, measurement, and failure modes. - Trade-off: what the popular framing misses or where the approach breaks. - Evidence: current data, official documentation, case material, or observable examples. - Adjacent conversation: 3-5 related topics that deepen the article without causing drift. - Discussion gap: a question qualified readers can answer from experience.
Reject adjacent topics that do not strengthen the thesis or reader decision. “More keywords” is not the same as more depth.
Read `references/linkedin-business-depth-and-humanization.md` and create `linkedin-insight-map.md`. Enrich the selected topic across the dimensions that materially affect the business decision:
- decision trigger and cost of waiting; - sponsors, users, approvers, blockers, buyers, and owners; - cost, budget, ROI, revenue, margin, or option value; - workflow, data, integration, adoption, and change management; - baselines, leading indicators, outcome metrics, and guardrails; - risk, strongest objection, failure mode, and reversibility; - one attributable or explicitly hypothetical scenario; - the next artifact, meeting, pilot, or decision the reader should initiate.
For a substantial business article, normally develop at least five relevant dimensions. Do not force irrelevant finance or governance sections into a career essay, but do not omit a material stakeholder, cost, or risk merely to keep the article simple.
### 4. Build the claim-source ledger
Create `source-ledger.md` for any current, factual, comparative, or decision-shaping article.
For each material claim, record:
- claim ID and exact claim; - claim type: fact / inference / editorial judgment / user-provided experience; - source title, publisher, date, and URL; - status: `verified`, `user_provided`, `needs_verification`, `softened`, `removed`, or `unsupported`; - where it will appear; - caveat or expiry risk. - rese
Technical details
- Version
- 1.0.0
- License
- MIT
- Last updated
- Aug 21, 2026
- Published
- Aug 21, 2026
Decision snapshot
Companion skill
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 82/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for linkedin-writer, ready for a manual X post.
linkedin-writer: LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-... 483 stars https://www.openagentskill.com/skills/flaqai-linkedin-writer?ref=x
Optional reply with install command
Listing + install path for linkedin-writer: https://www.openagentskill.com/skills/flaqai-linkedin-writer?ref=x Install: npx skills add flaqai/backlink_skills --skill linkedin-writer
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- flaqai
- Source
- flaqai/backlink_skills
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to flaqai but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer)
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer)
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer/audit)
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer)Author
flaqai
@flaqai
Tags
Platform fit
Health signals
- GitHub stars
- 483
- Quality score
- 42/100
- Last GitHub push
- Aug 21, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 12
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Sandbox only
- GitHub adoption483 GitHub starsINFO
- Stars/forks activity483 stars, 175 forks; issue activity unavailable in current metadataINFO
- Recent maintenance1d since pushPASS
- License clarityMITPASS
- README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
- Dependency/runtime riskno major dependency risk hints in public metadataPASS
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