by dongshuyan
司南：个性化 AI 任务总控 Skills 系统 /COMPASS: Personal Alignment Skills OS for AI Agents
# Add to your Claude Code skills
git clone https://github.com/dongshuyan/compass-skillsGuides for using ai agents skills like compass-skills.
Last scanned: 6/17/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-06-17T09:02:12.368Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how compass-skills compares with popular alternatives.
compass-skills is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by dongshuyan. 司南：个性化 AI 任务总控 Skills 系统 /COMPASS: Personal Alignment Skills OS for AI Agents. It has 725 GitHub stars.
Yes. compass-skills passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/dongshuyan/compass-skills" and add it to your Claude Code skills directory (see the Installation section above).
compass-skills is primarily written in Python. It is open-source under dongshuyan on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh compass-skills against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
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.
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.
Long-running agent work needs five kinds of state:
COMPASS organizes that state into five local workflows:
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 evidence, then let the upgrader turn that evidence into a reviewed upgrade plan and, after approval, an applied change.
academic-humanizer helps authors avoid AI-sounding language while drafting and remove it from existing academic prose. It targets formulaic, vacuous, mechanically repetitive, and process-leaking patterns while protecting claims, evidence strength, quotations, formulas, citations, technical names, modality, logic, and scope. The result aims to read as natural, credible scholarly writing and to reduce the likelihood that readers perceive it as AI-generated; it does not promise a universal authorship judgment.
assess-interview-candidate prepares human recruiters for a structured interview. It keeps detailed evidence and validation data in a local audit layer while presenting only a candidate overview, job-relevant resume uncertainties, and 12-18 directly readable questions in the offline HTML. It never authorizes automatic hiring, rejection, or ranking.
user-profile-keeper -> who is the user and how should we collaborate?
task-forest -> where does this task fit and is it still aligned?
pause-and-resume -> where should this AI conversation stop and continue later?
session-handoff-prompt -> what should the next AI conversation know to continue now?
task-clarifier -> what should the agent do now?
run-history-skill-builder -> how do we package this proven workflow as a new skill?
run-history-skill-upgrader -> how does a skill self-evolve safely from real session evidence?
academic-humanizer -> how do we remove AI-sounding prose without changing its claims?
assess-interview-candidate -> how do we prepare a focused, evidence-bounded human interview?
A vague request is turned into a checked requirement before the agent recommends anything.
Formatted from a live terminal run. Terminal status lines are omitted.