一键导入
llp-init-retrofit
Add LLP to an existing repository by surveying the codebase and docs, drafting the root LLP, and proposing a phased adoption and annotation plan.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Add LLP to an existing repository by surveying the codebase and docs, drafting the root LLP, and proposing a phased adoption and annotation plan.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Team AI Usage Review for a HypAware server — a team improvement report written to be shared in the open, that engineers and supervisors both enjoy reading. Supervisors (eng manager, lead, CTO — no HypAware knowledge assumed) get the overview — how much AI the team uses and what it costs in tokens, what the work is and whether it pays off, which way it's trending; engineers get section detail they can act on, ending in ranked improvements (cost levers plus skill/subagent/AGENTS.md changes) with estimated weekly token savings and ready-to-apply artifacts on a dedicated proposed-changes page — one skill, one report, with the changes list as its own linked page. Findings attach to patterns and defaults, never person-rankings; granularity scales with team size. Token volume, never dollars. Saves a dated report under hypaware-reports/; first asks which HypAware source to query (local logs or a remote server) via the hypaware-query skill. Supersedes hypaware-ai-adoption-report, hypaware-ai-spend-report, and hypaware
Render the Markdown HypAware reports under hypaware-reports/ into a static HTML site: enrich the report Markdown with the data-report component vocabulary (metric cards, charts, callouts), run the repo's build.sh (pandoc), and regenerate the top-level landing page. Use when the user says "convert the reports to HTML", "build/render the report site", "rebuild the HTML", "publish the reports", "update the reports landing page / index", or wants to preview or GitHub-Pages the reports. Operates on the ~/hypaware-reports git repo only. Does NOT run any report skill, does NOT touch local HypAware recordings, and does NOT push to the remote unless the user explicitly asks.
Explore the HypAware context graph: the activity graph projected from ai_gateway_messages (Sessions, Apps, Models, Tools, Files, Skills, Programs, and git Repos/Commits, and how they connect), plus optional server-side GitHub enrichment (Repos, PullRequests, Commits, Reviewers) that bridges AI sessions to code review. Use when the user asks what connects to a file/session/tool/model, which sessions ran a skill or invoked a program, wants co-occurrence or N-hop traversal, wants to join AI sessions to GitHub repos/PRs/reviewers, or wants to build/refresh the graph. Covers `hyp graph project` and `hyp graph neighbors`.
Inspect local HypAware recordings with the hyp query CLI. Use when the user asks about recorded logs, traces, metrics, AI gateway exchanges, query cache freshness, or wants SQL over local HypAware data.
Scan HypAware recordings for sensitive content (secrets, credentials, secret-file reads, PII, private-repo code) and recommend what to opt out of recording - a folder-scoped `.hypignore` / `hyp ignore <path>` for directory-bound exposure, or a per-session opt-out for one-off sensitive sessions. Use when the user says "scan my logs for secrets", "what should I hypignore", "did I record anything sensitive", or wants ignore recommendations grounded in recorded data. REDACTS every secret (never echoes a value). Recommends PROSPECTIVE opt-outs only - it does NOT purge already-recorded rows. Asks which source to scan (local logs or a remote server) first.
Inspect local HypAware recordings with the hyp query CLI. Use when the user asks about recorded logs, traces, metrics, AI gateway exchanges, query cache freshness, or wants SQL over local HypAware data.
| name | llp-init-retrofit |
| description | Add LLP to an existing repository by surveying the codebase and docs, drafting the root LLP, and proposing a phased adoption and annotation plan. |
Use this skill when the user wants to adopt LLP in an existing repository with meaningful code, documentation, or history. This is the retrofit workflow described in LLP 0002.
Invoke as /llp-init-retrofit.
Build a high-level map before creating LLPs:
README and notable documentationThe output of this step should be a concise architecture summary and an initial subsystem list.
llp/ tree already exists, do not blindly create a second setup layerCreate or revise the root LLP so it acts as the entry point for the codebase:
llp/0000-<project-slug>.explainer.md**Type:** Explainer**Role:** RootThis document should reduce cold-start cost for someone reading the repo for the first time.
Identify the 2-5 highest-value follow-on LLPs based on:
For each proposed LLP, provide:
Only create additional LLPs immediately if the user asks.
Add LLP guidance to AGENTS.md, CLAUDE.md, or the repo's equivalent:
@ref annotations when implementing non-obvious documented decisionsPropose a phased adoption plan:
Do not mass-annotate the whole repository unless the user explicitly asks.