AgentKit SEO GitHub

Overview

Work through the lens of a pragmatic engineering hiring manager and open-source maintainer skimming the profile. Use this skill to improve GitHub discoverability, comprehension, and trust without claiming undocumented ranking guarantees.

Reference selection

• Bio, pinned repos, repo naming, About text, README packaging: references/profile-and-repo-structure.md
• Copy blocks for bios, READMEs, About text, topics, pins: references/section-recipes.md
• Code search, indexing limits, Linguist, language stats: references/search-indexing-and-linguist.md
• Full-profile or repository audit: references/profile-and-repo-audit.md
• AGENTS.md, Copilot instructions, AI-readable repo structure: references/copilot-and-agent-readiness.md
• Audit scorecard and prioritized fix-first ranking: references/audit-scoring.md

Wiki context

• Read wiki/index.md when the task asks about GitHub searchability, Linguist, .gitattributes, AI-readable repository structure, agent-readiness, confidence labels, platform constraints, known agent failure modes, or full audit source discipline.
• Read wiki/knowledge.md only after wiki/index.md routes the current task there.
• If a wiki file is unavailable in an older install, continue with the relevant references/ file and mark wiki-specific guidance as unavailable when it affects confidence.

Token discipline

• Do not load every repository README unless the user asks for a full profile audit.
• For profile work, inspect profile metadata, pinned repos, and at most 3 highest-signal repositories by default.
• For one repository, stay inside that repository unless cross-profile positioning is explicitly requested.
• Prefer repository metadata, About text, topics, pinned status, README opening sections, and visible language signals before loading entire files.
• Keep source ledgers compact: list input groups, not every minor fetched page.
• Do not restate full checklists in the final output. Report only findings that change the user's next action.
• Name next inspection if bounded.

Depth contract

Use the smallest honest audit depth:

• Quick scan: profile fields, profile README opening, pinned repositories, and obvious metadata gaps.
• Default audit: quick scan plus up to 3 highest-signal repositories, using repository metadata, README openings, topics, and language signals.
• Deep audit: full README/file inspection, .gitattributes, setup paths, CI, licenses, social previews, and repo-by-repo consistency.

Default to Default audit for broad profile requests. Offer Deep audit as an optional next step when the current answer would benefit from more evidence. Do not choose Deep audit silently unless the user asks for a complete audit, every repository, exact file changes, or repository-level remediation.

Intake workflow

• If the user provides a GitHub profile or repository URL, fetch and inspect public profile, pinned repository, repository metadata, README, topics, default branch, and visible language signals when tools allow it.
• To retrieve public profile fields, pinned or popular repositories, recent source repositories, and bounded README excerpts without authentication, execute: node <skill_dir>/scripts/github-fetcher.mjs <profile-or-repository-url> (where <skill_dir> is the current skill directory). Profile mode defaults to 3 repositories; repository mode inspects the exact repository. Read the generated Markdown report for context and the JSON report for structured observations.
• The fetcher creates a unique directory under the operating system's temporary directory unless an explicit output directory is supplied. Read both reports, then remove the temporary directory after the task. Never write reports into the skill directory, user repository, personal context file, or VitaeGraph.
• Treat extraction warnings as unavailable evidence. GitHub HTML is a public observation surface, not a stable API contract, so a missing parsed field does not prove that the field or repository is absent.
• If the user provides only a username, treat it as enough to inspect public GitHub material when tools allow it.
• If the task depends on private repositories, contribution details, or account settings, ask the user for screenshots, copied settings, exports, or explicit local files instead of guessing.
• If the user has or needs a personal career context file, load or recommend agentkit-seo-agent-context-optimization before rewriting profile-level positioning.
• For repository-specific work, prefer concrete file edits when the repository is available locally; otherwise return copy blocks and a change checklist.
• Do not request login or tokens unless the user explicitly asks for private repository work.

Rules

• If the user supplies an explicit VitaeGraph path, read VITAEGRAPH.md, index.md, and only relevant project records. Preserve stated limitations and open questions, omit private paths, and treat visibility: public as eligibility for consideration rather than publication consent.

• Distinguish documented GitHub behavior from inference.

• Separate facts verified on GitHub, facts supplied by the user's context files, and recommendations inferred from those facts.

• Optimize for search clarity, repository comprehension, and maintainer trust.

• Do not promise hidden ranking boosts from stars, forks, or activity patterns.

• Do not invent numbers, percentiles, ranking mechanics, vulnerability impact, award scope, repository health, or pinned-repository status.

• Avoid hype language unless the user provided evidence that supports it. Prefer precise proof over louder branding.

• Keep examples factual to the user's real projects.

• Keep recommendations scoped to the user's actual repositories and public goals.

• Use career direction to choose profile README emphasis, pinned-repository strategy, and repository descriptions, but do not make an emerging direction look like mature repository evidence unless the public work supports it.

• Honor context-file evidence boundaries, positioning constraints, and claims to avoid when selecting proof points.

• Keep profile metadata, pinned repositories, README copy, and repository structure aligned around the same public positioning.

• For rewrites, improve clarity, proof, and discoverability before inventing a more aggressive branding angle.

• Recommend AGENTS.md or Copilot instruction files only when the repository is agent-facing, complex enough to need operational guidance, or the user explicitly asks for agent-readiness work.

Self-review

Before returning, check the draft and fix or flag any failure:

• No fabricated metrics, percentiles, ranking mechanics, or pinned/archived/licensed status; every claim traces to inspected GitHub material, the context file, or is labeled inference.
• Evidence labels are correct and not upgraded beyond their source.
• Output matches the requested scope, the target role, and the user's stated goals and target locations; nothing drifted into unrequested work.
• The highest-impact fixes lead, and copy stays factual to the user's real work.

If a check fails and cannot be fixed from available inputs, say so rather than papering over it.

Response shape

Return:

1. source ledger: public inputs inspected, context files used, and inaccessible inputs
2. priority issues by profile, pinned repos, and repositories
3. ready-to-apply copy or file changes
4. confidence notes that label each major recommendation as verified, context-derived, or inferred
5. next actions, including context-file creation when profile facts are weak

For audits, make the output feel like a grounded review rather than a generic marketing report. Use concise labels such as Verified, From context, and Inference when a claim could otherwise be ambiguous. When the audit is intentionally bounded, include a one-line Depth note that says what was not inspected and what deeper inspection would add. When the user asks for a score, scorecard, or before/after comparison, also apply references/audit-scoring.md: report the overall score, band, per-category breakdown, and a fix-first ranking, labeled as an internal prioritization heuristic rather than a platform ranking.

Human playbook: GitHub optimization.