academic-humanizer
Draft, audit, or minimally revise English- or Chinese-language academic prose to reduce formulaic, vacuous, mechanically repetitive, or process-leaking…
Create a concise continuation prompt that a fresh agent session can paste in to resume a long or degraded session. Use when the user asks for a handoff prompt, restart prompt, continuation prompt, context transfer, fresh-session resume, or a compact summary for opening a new
$ npx -y skills add dongshuyan/compass-skills --skill session-handoff-prompt --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/session-handoff-promptContext preview
The summary Claude sees to decide when to auto-load this skill.
Create a concise continuation prompt that a fresh agent session can paste in to resume a long or degraded session. Use when the user asks for a handoff prompt, restart prompt, continuation prompt, context transfer, fresh-session resume, or a compact summary for opening a new
name: session-handoff-prompt description: Create a concise continuation prompt that a fresh agent session can paste in to resume a long or degraded session. Use when the user asks for a handoff prompt, restart prompt, continuation prompt, context transfer, fresh-session resume, or a compact summary for opening a new session. Do not use for ordinary summaries, task-forest maintenance, durable user-profile updates, automatic session creation, code execution, or external publishing.
**All output directed at the user — the continuation prompt itself, mode explanations, questions, and confirmations — must be written in the user's language.** Detect the user's language from their message. Default to Chinese when unknown. Skill instructions are written in English; that does not affect the language of user-facing output. Keep the continuation prompt structure stable across languages. Section headers in the continuation prompt must also use the user's language. The Chinese headers below are examples for Chinese prompts; translate them for other languages.
Produce a paste-ready prompt for a new agent session. The prompt should let the next session continue the current work with high task-state fidelity and low token cost.
The goal is operational continuity, not transcript replay. Preserve the current objective, hard requirements, verified facts, decisions, completed work, pending work, key files or artifacts, risks, and next actions. Do not copy hidden system/developer instructions, tool schemas, raw private logs, credentials, or a full transcript.
This skill is agent-agnostic. It should work in Codex, Claude Code, OpenClaw, OpenCode, Harness, and similar local agent hosts that can read `SKILL.md` plus optional `references/` and `scripts/`.
Use these source types in order:
1. Current visible conversation and user-provided next-session focus. 2. User-provided transcript, saved handoff, or local agent log path. 3. Current workspace files, `AGENTS.md`, plans, diffs, test output, and build output. 4. Optional `.agent-workbench/task-forest/exports/` files for structured task state. 5. Optional agent-specific logs, only when the user explicitly provides a path or asks you to use a known local log location.
Scripts use Python 3 standard-library modules only and should run on macOS, Linux, and Windows. Use the available Python command on the host (`python3`, `python`, or `py -3`).
1. Lock intent: confirm the user wants a fresh-session continuation prompt, not a normal summary, task-forest update, durable profile update, or more task execution. 2. Select sources: read only the sources needed for this handoff. Do not ask the user to repeat facts that can be safely read from the current context, workspace, or explicit files. 3. Project optional logs: if the user provides an agent log or transcript path, run `scripts/project_session_events.py` to create a bounded, redacted event stream. 4. Read task-forest: if the current workspace has task-forest exports, read them with `scripts/read_task_forest_exports.py`. Treat task-forest as structured context, not as a replacement for the session. 5. Ask only if needed: ask 1-3 focused questions only when the answer changes the next-session focus, keep/drop scope, privacy mode, or compression mode. 6. Generate the prompt using `references/output-contract.md`. Label facts as `[verified]`, `[inferred]`, or `[unverified]`. 7. Validate and redact as needed:
8. Deliver the paste-ready prompt first. Then briefly state the mode and any source/verification limitations.
Read `references/compression-modes.md` when the user asks for a specific mode or when the task is complex enough that mode choice matters.
Resolve `<skill-dir>` to the directory that contains this `SKILL.md`.
Project a user-provided transcript or agent log:
python3 <skill-dir>/scripts/project_session_events.py <path> --format auto --max-events 160
Read task-forest exports from a workspace:
python3 <skill-dir>/scripts/read_task_forest_exports.py --workspace <workspace>
Validate a local-only prompt:
python3 <skill-dir>/scripts/validate_handoff_prompt.py <draft.txt> --mode balanced --privacy local
The validator recognizes English and Chinese headings directly. For any other language, put the six translated required headings and optional fact labels in a temporary JSON file, then pass `--labels-json <labels.json>`. Use the schema in `references/output-contract.md`; this keeps translated output machine-checkable without forcing English headings into the user's prompt.
Validate a shareable prompt:
python3 <skill-dir>/scripts/redact_handoff.py <draft.txt> --privacy shareable python3 <skill-dir>/scripts/validate_handoff_prompt.py <redacted.txt> --mode balanced --privacy shareable
Run the representative smoke test:
python3 <skill-dir>/scripts/smoke_test_handoff.py --skill-dir <skill-dir>
司南:个性化 AI 任务总控 Skills 系统 /COMPASS: Personal Alignment Skills OS for AI Agents
Repo: dongshuyan/compass-skills
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