kennyzir/7deer_skills/seo-link-strategy
Claude Code and agent skill by kennyzir/7deer_skills
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Research backlink opportunities, record contact evidence, and generate personalized local outreach drafts from user-provided product and contact data. Use for backlink planning and draft preparation; never send messages or submit forms without explicit authorization for the exact target and payload.
Install
npx vskill@latest install kennyzir/7deer_skills/seo-link-strategySKILL.md
SEO Link Strategy
Build an evidence-backed backlink opportunity list and prepare local outreach drafts. Discovery, evidence capture, draft generation, and external execution are separate states.
This Skill does not ship a current directory of platforms or verified contacts. Candidates come from the user or from research performed at runtime. Never treat a guessed address, old list, search snippet, or role-based email pattern as an observed contact.
Operating boundary
Default work is research and local draft preparation. Do not send Gmail or other messages, submit forms, purchase placements, call paid/mutating services, or change remote systems by default.
Before any external side effect:
- show the exact target, payload, account, and expected effect;
- obtain explicit user authorization for that specific action;
- execute only the authorized action; and
- record the actual result and UTC time, including failures or uncertainty.
A draft is not-sent. An opportunity is not a backlink. An attempted submission is not completed unless a real result supports that state.
Evidence contract
Each opportunity or contact record must support these fields:
| Field | Requirement | |
|---|---|---|
name | User-provided or runtime-discovered platform/contact label. | |
source_url | Exact page where the opportunity or contact evidence was observed. | |
observed_at | Past UTC timestamp for the actual observation, or null. Future times are rejected. | |
status | observed only with captured evidence and time; otherwise unknown. | |
email | One value present in captured evidence, or null; never constructed from hints. | |
context | User-supplied limits, conflicts, access failures, or relevance notes. |
Keep sources and observation times when data is handed from research to drafting. If a page cannot be checked, preserve the candidate with status: unknown rather than filling likely values.
1. Normalize captured contact evidence
Use scripts/contact_discoverer.py after the user or an available research tool supplies candidate URLs and captured page text. The script is offline: it extracts addresses only from the supplied capture and makes no network request.
Input JSON:
Run:
Without --output, JSON goes to stdout. An existing output path is never overwritten. The first record can become observed; the second remains unknown with no invented email. If supplied evidence contains multiple distinct addresses, the normalizer emits one contact record per address so every record keeps a single unambiguous recipient.
2. Generate local drafts
Use scripts/email_generator.py with user-provided product, sender, and contact data. It performs no browsing and contains no sending implementation.
The normalizer's contacts array is already the exact contact schema accepted here. Copy that array unchanged into a new object with product and sender; omit only the normalizer's top-level generated_at. No field renaming or implicit conversion is required.
Run:
The output marks every draft delivery_status: not-sent. Invalid inputs fail before output creation, and existing output files are not replaced.
3. Review and optional execution
Review source freshness, recipient relevance, claims, product links, sender identity, and applicable platform rules. Remove unsupported personalization instead of presenting inference as observation.
If the user later authorizes sending or submitting, use an appropriate external tool only for the approved target and payload. Store a separate result record with fields such as attempted_at, status, result_id, and error. Do not rewrite the draft-generation output to imply delivery.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingseo-link-strategy/SKILL.md
Popularity
- Repository stars
- 316
- Repository forks
- 141
- 7-day trend score
- 29
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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