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academic-humanizer
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Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
Start here: Use & develop your own Skill ecosystem
A practical tutorial for using
SKILL.md, auditing reusable skills, drafting skills with AI, extracting real workflows, and building a local Skill ecosystem.
npx skills add dongshuyan/compass-skills --skill '*' -a claude-code
COMPASS Skills gives AI agents nine local skills: five runtime collaboration skills, two run-history skill-engineering skills, one academic humanization skill, and one local hiring-support skill.
The project currently ships nine SKILL.md skills:
| Skill | Purpose |
|---|---|
task-clarifier | Aligns goals, scope, evidence, acceptance criteria, and risk boundaries before ambiguous, costly, or externally visible work. |
task-forest | Maintains a repo-local task forest / DAG with goals, subtasks, dependencies, progress, deviations, todos, decisions, and conversation history. |
pause-and-resume | Cooperatively pauses unfinished work at a safe boundary and resumes it from a precise checkpoint in the same AI conversation. |
session-handoff-prompt | Compresses the current AI conversation's goal, progress, constraints, and next steps into a paste-ready prompt for a new AI conversation. |
user-profile-keeper | Maintains a local, auditable, correctable collaboration profile for communication preferences, risk style, and recurring working context. |
run-history-skill-builder | Turns completed or repeatedly refined run history into a new reusable skill package or a reviewed skill-design plan. |
run-history-skill-upgrader | Automatically turns session evidence from real execution, encountered and resolved difficulties, validation results, and user feedback into an upgrade plan for an existing skill, forming the simplest controlled self-evolution loop; it applies changes only after explicit approval. |
academic-humanizer | Helps write or revise English and Chinese academic prose by removing formulaic AI-like patterns and restoring a natural scholarly voice while preserving claims, evidence strength, and logical relations. |
assess-interview-candidate | Turns an authorized resume and job description into an auditable evidence layer and a concise three-part offline interviewer report, with locally sanitized resume portraits and bounded timeline-age estimates kept outside scoring. |
For multi-skill repositories, install only the functions you actually need. Use pause-and-resume when the same AI conversation will remain available; use session-handoff-prompt when work must move to a fresh conversation. The run-history pair supports skill engineering, and academic-humanizer improves academic prose without changing its claims.
Quick Start
List the available skills before installing:
npx skills add dongshuyan/compass-skills --list
Install all skills for Claude Code:
npx skills add dongshuyan/compass-skills --skill '*' -a claude-code
Install all skills for both Codex and Claude Code:
npx skills add dongshuyan/compass-skills --skill '*' -a codex -a claude-code
After installation, invoke the skills directly in an AI conversation:
$task-clarifier
$task-forest
$pause-and-resume
$session-handoff-prompt
$user-profile-keeper
$run-history-skill-builder
$run-history-skill-upgrader
$academic-humanizer
$assess-interview-candidate
For manual installation, copy the nine folders under skills/ into the agent's local skills directory and keep their references/, scripts/, assets/, evals/, and agents/ subdirectories intact.
Why COMPASS Exists
Long-running agent work needs five kinds of state:
- User context: communication preferences, risk boundaries, recurring omissions, and collaboration style.
- Project context: where the current request fits, what it depends on, and how far it has progressed.
- Goal context: how the current task contributes to the original objective and whether it still matches it.
- Pause context: the safe stopping point, remaining work, non-repeatable effects, and first action when the same AI conversation resumes.
- Handoff context: what a new AI conversation needs to continue the current task without replaying the whole transcript.
COMPASS organizes that state into five local workflows:
- A local profile that the user can inspect and correct.
- A repo-local task graph that survives AI conversation boundaries.
- A cooperative pause checkpoint for continuing in the same AI conversation.
- A paste-ready continuation prompt for a new AI conversation.
- A clarification gate before ambiguous or risky execution.
How The Core And Meta Skills Work Together
task-clarifier is the entry point for ambiguous, high-cost, high-risk, evidence-sensitive, or externally visible work. It first identifies the user-owned decisions that must be made, asks 1-3 focused questions with recommended answers, confirms shared understanding, and only then searches or executes.
task-forest records long-running work structure: why a task exists, where it fits, how far it progressed, what changed, and what remains unresolved.
pause-and-resume stops an unfinished task at the nearest safe boundary, records what must and must not be repeated, and continues from that checkpoint when the user returns to the same AI conversation. It creates no file solely for pausing.
session-handoff-prompt turns the current AI conversation, explicit transcripts, workspace evidence, and optional task-forest exports into a concise prompt for the next AI conversation. It reads task-forest as structured context but never modifies it.
user-profile-keeper stores collaboration preferences locally. Future AI conversations use the profile to ask relevant questions and apply the right risk boundary. Current files, logs, and user-provided context remain the authority; secrets stay out of the profile.
run-history-skill-builder turns a completed or repeatedly refined workflow into a new skill package or a plan-only design. If the request is really about changing an existing skill, it hands the job off instead of editing that skill directly.
run-history-skill-upgrader takes the next step for existing skills: it automatically reads session evidence from real execution, encountered and resolved difficulties, validation results, and user feedback, then produces a concrete upgrade plan and stops. Only after explicit approval of that plan does it edit files. In practice, this is the simplest controlled self-evolution loop for skills: periodically run a target skill, accumulate real session
// HOW IT'S BUILT
KEY FILES