compass-skills— agent skill

<p align="center"> <img src="assets/compass-logo.png" alt="COMPASS Skills logo" width="760"> </p>

<h1 align="center">COMPASS Skills</h1>

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<p align="center"> <a href="README.zh.md">中文</a> · <a href="SECURITY.md">Security</a> · <a href="LICENSE">License</a> </p>

Start here: [Use & develop your own Skill ecosystem](https://dongshuyan.com/compass-skills/skill-writing-tutorial.html)

>

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 ten local skills: five runtime collaboration skills, two run-history skill-engineering skills, two writing and communication skills, and one local hiring-support skill.

The project currently ships ten SKILL.md skills:

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. Use clear-communication to organize explanations for the reader, and academic-humanizer to improve 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
$clear-communication
$academic-humanizer
$assess-interview-candidate

For manual installation, copy the required 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:

1. A local profile that the user can inspect and correct.
2. A repo-local task graph that survives AI conversation boundaries.
3. A cooperative pause checkpoint for continuing in the same AI conversation.
4. A paste-ready continuation prompt for a new AI conversation.
5. 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 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?
clear-communication    -> what does this reader need to understand or do?
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?

Clear communication

clear-communication helps an agent turn source material into explanations, progress reports, comparisons, instructions, and documents that a reader can understand and use. It preserves facts, important conditions, uncertainty, and what has actually been checked. Use it when the material is accurate but hard to follow, or when a detailed record needs to become a useful explanation.

The package contains the writing workflow, hypothetical examples, source notes, and retained license notices. Its optional agents/openai.yaml supplies interface metadata. The writing workflow itself has no script or external-service dependency.

Install one skill, in the scope you need

Install into the current project for Codex and Claude Code:

npx skills add dongshuyan/compass-skills --skill clear-communication -a codex -a claude-code

Add -g for a user-level installation across that user's projects:

npx skills add dongshuyan/compass-skills --skill clear-communication -a codex -a claude-code -g

For manual installation, copy the entire skill folder, including references/ and its license notices. Use the host's actual discovery directory:

Here, <user-home> means the operating-system user's home directory. User-level installation is local to that user; it does not deploy the skill to another machine or a cloud session. A shared folder with supported symlinks can serve both hosts. If a destination already exists, compare it before replacing any customized content. Check the host's skill list after installation; start a new session if the current one has not refreshed.

Manual and automatic invocation

In Codex:

$clear-communication Explain the material below to a colleague who does not write code. Preserve the important conditions and distinguish completed work from unverified behavior.

In Claude Code:

/clear-communication Explain the material below to a colleague who does not write code. Preserve the important conditions and distinguish completed work from unverified behavior.

A Harness is the application that loads instructions, exposes tools, and manages the agent conversation. Codex and Claude Code can make a skill's description available for automatic selection. The model then judges whether the task matches it. This package keeps the default settings that permit both manual invocation and automatic selection; selection is not a promise that the skill will run on every turn.

The Harness controls availability and invocation policy. For example, Codex can restrict implicit invocation through agents/openai.yaml; Claude Code can restrict it through disable-model-invocation or its skill settings. Other Harnesses have their own rules. For an environment without native routing, ask the agent to read SKILL.md and follow it through AGENTS.md.

Writing workflow

1. Identify the reader's current question and what the answer should help them understand or do.
2. Extract the facts, conditions, exceptions, numbers, uncertainty, and verification state that must survive.
3. Choose an order suited to the task: answer first for a question, dependent steps for a tutorial, decision-relevant differences for a comparison.
4. Explain who does what and why the facts support the conclusion. Keep useful technical terms and connect unfamiliar ones to their behavior.
5. Compare the result with the source. Check for missing conditions, stronger claims, confusing references, or an unclear next action.

These are instructions for the model, rather than a program that forces a fixed number of steps or scores readability. Examples guide editorial choices; a reader still needs to assess the actual output. For academic work, retain the field's terminology and evidence constraints, and combine this skill with academic-humanizer when prose editing is also needed.

Example: maintenance notes become a first-use explanation

This is a hypothetical example. A colleague asks how to start using NoteBox. The notes say:

• Click New note to write; content saves automatically in the current browser. One real save operation has been checked.
• The cloud-sync button exists, but its service is not connected.
• Closing the window preserves notes. Clearing browser data deletes local notes.
• The maintenance record also lists 23 local checks, sync_enabled=false, and a plan to organize logs later.

A reader-oriented answer is:

Open NoteBox and click New note to start writing. Your notes save automatically in the current browser; one save operation has been checked.

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Cloud sync is not available yet, so you cannot rely on it to read these notes on another device.

>

Closing the window keeps your notes. Clearing browser data deletes the local notes.

The explanation leads with the action and keeps the storage and deletion conditions together. The internal field and maintenance count remain in the maintenance record because they do not change this reader's next action. A maintenance review could need those details again.

Portability, privacy, and evidence

The core skill is intended for Harnesses that can read and follow SKILL.md. It uses no specific tool API, shell command, operating-system path, network service, or hardware interface. Installation, routing, permissions, and context retention still depend on the host. The optional OpenAI interface metadata is not required by other hosts.

The release package contains generic instructions and hypothetical examples, with no personal source material or machine-specific user paths. Material supplied at runtime is handled under the host's permissions and data policies. Structure checks and installation checks establish package validity and availability; they do not establish a comprehension improvement or universal behavior across Harnesses.

Task Clarifier Example

A vague request is turned into a checked requirement before the agent recommends anything.

<details> <summary>Example: choosing a suitcase</summary>

Formatted from a live terminal run. Terminal status lines are omitted.

Turn 1: vague request

User

I want to buy a suitcase, can you tell me which one is the best?

$task-clarifier

Before recommending a product, the skill turns "best" into concrete decision criteria:

1. Use case — What will you use the suitcase for most?

• Options: business travel, leisure vacations, extended travel, or custom situation.
• Recommendation: start with the primary use case, because it changes size, weight, and feature priorities.

1. Budget — What price range is realistic?

• Options: under $100, $100-300, $300-600, $600+, or custom budget.
• Recommendation: $100-300 is often the best durability-to-cost range for most travelers.

1. Meaning of "best" — Which priority wins?

• Options: durability, light weight, price, warranty/service, organization, or custom priority.
• Recommendation: durability is the most universal priority.

User answer

1A 2C 3A

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Business travel, $300-600, maximum durability.

Turn 2: narrow the product class

$task-clarifier

The first answers narrow the problem, but two choices still change the recommendation:

1. Bag type — Carry-on or checked bag?

• Recommendation: carry-on for most business trips, faster airport flow, and lower damage risk.

1. Material — Hard shell or soft-sided?

• Recommendation: hard shell for durability and professional appearance.

User answer

Checked bag, hard shell.

Turn 3: resolve the final size

$task-clarifier

One last decision remains:

1. Checked-bag size — Compact, medium, or full-size?

• Recommendation: 26-27 inches handles many business trips without becoming oversized.

User answer

28 inches.

Confirmed requirement

$task-clarifier

You need a checked suitcase for frequent business travel. The bag should be hard-shell for maximum durability and professional appearance, 28 inches for extended trips, and within a $300-600 budget. Durability is your top priority over lighter weight or organizational gimmicks.

Inferences to confirm

• Durability means repeated airport handling and 5+ years without major failure.
• Brand, color, wheel type, TSA lock, and warranty length are not yet fixed preferences.
• The next step is specific model recommendations, unless the user prefers an evaluation framework.

</details>

The original example screenshots show ordinary answers and an explicitly invoked clarification conversation. They illustrate the interaction; they are not a controlled comparison of model performance.

Screenshots

Task forest HTML export:

task-forest tree demo

Live DAG view:

task-forest live DAG view

Task detail view:

task-forest live detail view

User profile and alignment flow:

COMPASS user profile and alignment flow

Ecosystem map:

COMPASS skills ecosystem DAG

Compatibility

COMPASS works across agent runtimes as a SKILL.md package with Markdown instructions, YAML frontmatter, optional references/, optional scripts/, and optional agent metadata.

The invocation examples use $skill-name as a portable explicit reference. Hosts may also use their native syntax, such as $pause-and-resume in Codex or /pause-and-resume in Claude Code. Agents without native skill routing can follow AGENTS.md and read skills/pause-and-resume/SKILL.md directly.

pause-and-resume is instruction-only: it has no OS path, shell, network, filesystem, or external-tool dependency. It requires the host to retain the current AI conversation across turns, so it does not recover a killed process, a closed conversation whose history is unavailable, or work moved to another conversation.

The scripts use Python standard-library components and run locally. assess-interview-candidate requires Python 3.10 or later and uses capability-based instructions instead of fixed Agent tools or installation paths.

Safety Model

COMPASS keeps runtime data local:

• No upload of task data or user-profile data.
• No browser cookie, token, private key, credential, or session extraction.
• task-forest stores task data under the current workspace, usually .agent-workbench/task-forest/.
• pause-and-resume keeps its checkpoint in the current AI conversation and creates no file solely for pausing. It records non-sensitive continuation facts and must not expose secrets.
• session-handoff-prompt is read-only by default. It can validate local handoffs with real workspace paths or redact them for shareable handoffs.
• user-profile-keeper stores local profile data under .compass-skills/user-profiles/v1 by default, or a user-selected COMPASS_USER_PROFILE_HOME.
• run-history-skill-builder reads only user-authorized workflow history and writes new skill files only to a user-approved local directory.
• run-history-skill-upgrader is plan-only by default. It can synthesize real session evidence into an upgrade plan automatically, but it enables a controlled self-evolution loop only after explicit approval of a concrete plan.
• academic-humanizer preserves source claims and locked spans, never invents facts or citations, and uses its Python script only for optional read-only diagnostics.
• assess-interview-candidate keeps authorized resumes and reports local, excludes contact details and precise addresses from the interviewer view, and prevents age, birthplace, hometown, marital status, or location from entering fit scores or hiring decisions.
• High-risk actions such as deletion, overwrite, publishing, remote writes, credential use, and global configuration changes require explicit confirmation.

Important: user-profile-keeper uses local plaintext storage without encryption. Do not store passwords, tokens, private keys, verification codes, or highly sensitive personal data in the profile.

See SECURITY.md for the security boundary.

Example Prompts

Clarify a task before execution:

Use $task-clarifier to align the task below.

Task: ...
Material: ...
Constraints: ask user-owned decisions first; infer discoverable facts from files, context, or reliable sources. Ask only questions that change scope, method, evidence, format, safety, or acceptance criteria.
Output: ask 1-3 key questions with recommended answers first; once the core need is clear, restate your understanding in 2-5 lines and ask me to confirm.

Maintain the task forest for a workspace:

Use $task-forest to analyze the current AI conversation and maintain the task forest for this workspace.

Goal: create a task-forest proposal from long-running goals, tasks, progress, deviations, risks, decisions, and follow-ups in this AI conversation.
Requirements:
1. Read the current task-forest list and todo first; initialize task-forest if missing.
2. Identify which long-term goal this AI conversation served. If no relation is clear, ask me or create a question/risk node.
3. Save a proposal and show me the planned changes before applying.
4. After approval, apply, validate, export, and report the HTML path.

Create a continuation prompt for a new AI conversation:

Use $session-handoff-prompt to create a balanced continuation prompt for a new AI conversation.

Goal: let the next AI conversation continue the current task without replaying the whole transcript.
Requirements:
1. Use the current conversation, explicit files I provide, current workspace evidence, and task-forest exports if present.
2. Keep task-forest read-only; do not save proposals or modify the task graph.
3. Use my language for the prompt. Default to Chinese if unknown.
4. Use privacy=local for this machine. If I ask for a public/shareable handoff, redact local paths and credential-like strings first.
5. Put the paste-ready prompt first, then briefly state mode, sources, and limitations.

Pause unfinished work in the current AI conversation:

Use $pause-and-resume to pause this unfinished task at the nearest safe boundary. Record a compact continuation checkpoint in this conversation, do not create a checkpoint file, and stop after telling me to say "continue" when I return.

When you return to the same AI conversation, say continue or an equivalent phrase. The agent should reconcile mutable state and resume from the recorded first action without repeating completed side effects.

Representative output shape:

You are taking over an AI conversation that has already gone through several rounds. Resume from the context below. If current files or verifiable evidence conflict with this handoff, trust the current evidence.

[Working directory]
<workspace>

[User goal]
Add session-handoff-prompt to COMPASS as an official skill that works on macOS, Linux, Windows, and major agent hosts.

[Required constraints]
- [Verified] Internal instructions are in English; interaction and output use the user's language, defaulting to Chinese when unknown.
- [Verified] Do not read credentials, cookies, browser sessions, or unrelated private logs.

[Next step]
1. Update the README and manifest.
2. Run the smoke test and security scan.
3. Report validation results and remaining risks.

Initialize a local user profile:

Use $user-profile-keeper to initialize my local user profile.

Goal: build an auditable, correctable, retractable profile from a local questionnaire or the current context.
Boundaries:
1. Store locally only. Do not upload anything or read browser cookies, tokens, or credentials.
2. Do not save secrets, passwords, private keys, verification codes, or browser-session information.
3. Put inferred, private, sensitive, or conflicting claims into pending proposals for my review.
4. Report what was saved, proposed, skipped, or redacted.

Remove AI-sounding language from academic prose without changing its claims:

Use $academic-humanizer to remove AI-sounding language from the academic passage below.

Preserve every claim, number, citation, comparison, hedge, causal relation, and scope boundary. Keep quotations, formulas, code, references, statistical notation, proper nouns, and requested verbatim text unchanged. Remove only unsupported, vacuous, mechanically repetitive, or process-leaking wording. Return the clean revised passage without an editor preface.

Passage: ...

Prepare a structured candidate interview report:

Use $assess-interview-candidate with the authorized resume and job description I provide.

Requirements: read every page of the PDF through both text extraction and visual inspection; keep the detailed evidence and validation data in the local audit layer; make the interviewer HTML contain only a candidate overview, job-relevant resume uncertainties, and 12-18 directly readable questions. Display candidate-provided schools, employers, and cities without guessing missing facts. Keep age, birthplace, hometown, marital status, and location out of fit scores and hiring decisions. Write only below the local case root I approve.

Validation Status

The public install path has been validated with [email protected]:

• npx skills add dongshuyan/compass-skills --list finds the released skills.
• npx skills add dongshuyan/compass-skills --skill '*' -a claude-code --copy -y installs the released skills into a temporary project's .claude/skills/ directory.
• python3 skills/session-handoff-prompt/scripts/smoke_test_handoff.py --skill-dir skills/session-handoff-prompt validates compacted-event projection, task-forest read-only summaries, local validation, and shareable redaction.
• printf '%s\n' 'Samples were randomized.' | python3 skills/academic-humanizer/scripts/metrics.py - --json provides read-only descriptive diagnostics for language routing, process leaks, and contrast candidates without assigning an authorship or quality score.
• With [email protected], the current local source is detected as ten skills; pause-and-resume copies successfully into temporary Codex and Claude Code skill roots with byte-identical SKILL.md files.
• clear-communication passes the package and Skill structure validators. A temporary copy installation for Codex and Claude Code preserves all six package files byte for byte. Codex 0.163.0-alpha.10.2 and Claude Code 2.1.296 both discover that temporary project skill through their native interfaces; this check sends no model task.
• pause-and-resume passes the Skill validator. Its published file is byte-identical to the source used for the completed Codex pause/resume behavior tests.
• assess-interview-candidate has 83 passing unit and public-package tests on Python 3.11 and 3.14. It also passes the Skill validator, Ruff, JSON parsing, Python 3.10 syntax parsing, JavaScript syntax checking, and offline report contract validation. An independent live-browser visual pass for the portrait layout remains pending.
• Windows and Linux compatibility is enforced through path, launcher, standard-library, reserved-filename, and shell-neutral contracts. This release was not run on physical Windows or Linux hosts.

Roadmap

Planned additions:

• Build reusable skills from real task histories.
• Upgrade existing skills from observed failures, feedback, and validation evidence.
• Summarize local agent states, waiting-human items, risks, and review queues.
• Recommend low-switching-cost follow-up tasks from the task graph.

Acknowledgements

Clear Communication draws on the following work:

• GPT with Taste: reader-first organization and selection of information that serves the current task.
• plain-english: preservation of meaning, certainty, conditions, operational state, and exact technical names.
• The International Plain Language Federation definition, reproduced by Clarity: assessing whether the intended audience can locate, understand, and apply needed information.
• plain-writing-skill: paired tasks and anonymous review as evaluation methods.
• Agrawal and Carpuat, TACL 2024: reading-comprehension questions as an additional meaning-preservation check.

The source notes distinguish adapted writing principles from methodological references. Original MIT notices are retained in the skill package and linked from THIRD_PARTY_NOTICES.md. These references do not establish this skill's effectiveness.

License

MIT. See LICENSE.

Community

• This repo has been shared as open source on Linux.do.

Star History

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