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goalsgoals 搜索

Agent Skill

goals 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:goals(goals 搜索)
来源仓库:https://github.com/boshu2/agentops
仓库路径:skills/goals
安装命令:
npx skills add https://github.com/boshu2/agentops --skill goals
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/boshu2/agentops --skill goals

简介

goals 维护 GOALS.yaml 与 GOALS.md 健身规格,跟踪目标进展与回归检测。

  • 支持添加新目标、导出 JSON 快照与历史趋势分析,便于 CI 集成。
  • 提供漂移比较与元目标运行,防止指标膨胀与优先级漂移。
  • 建议与 /evolve 联动,确保每个迭代周期聚焦关键成果度量。
  • goals 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

/goals — Fitness Goal Maintenance

Maintain GOALS.yaml and GOALS.md fitness specifications. Use ao goals CLI for all operations.

YOU MUST EXECUTE THIS WORKFLOW. Do not just describe it.

Quick Start

/goals                    # Measure fitness (default)
/goals init               # Bootstrap GOALS.md interactively
/goals steer              # Manage directives
/goals add                # Add a new goal
/goals drift              # Compare snapshots for regressions
/goals history            # Show measurement history
/goals export             # Export snapshot as JSON for CI
/goals meta               # Run meta-goals only
/goals validate           # Validate structure
/goals prune              # Remove stale gates
/goals migrate            # Migrate YAML to Markdown

Format Support

FormatFileVersionFeatures
YAMLGOALS.yaml1-3Goals with checks, weights, pillars
MarkdownGOALS.md4Goals + mission + north/anti stars + directives

When both files exist, GOALS.md takes precedence.

Mode Selection

Parse the user's input:

InputModeCLI Command
/goals, /goals measure, "goal status"measureao goals measure
/goals init, "bootstrap goals"initao goals init
/goals steer, "manage directives"steerao goals steer
/goals add, "add goal"addao goals add
/goals drift, "goal drift"driftao goals drift
/goals history, "goal history"historyao goals history
/goals export, "export goals"exportao goals export
/goals meta, "meta goals"metaao goals meta
/goals validate, "validate goals"validateao goals validate
/goals prune, "prune goals", "clean goals"pruneao goals prune
/goals migrate, "migrate goals"migrateao goals migrate

Measure Mode (default) — Observe

Step 1: Run Measurement

ao goals measure --json

Parse the JSON output. Extract per-goal pass/fail, overall fitness score.

Step 2: Directive Gap Assessment (GOALS.md only)

If the goals file is GOALS.md format:

ao goals measure --directives

For each directive, assess whether recent work has addressed it:

  • Check git log for commits mentioning the directive title
  • Check beads/issues related to the directive topic
  • Rate each directive: addressed / partially-addressed / gap

Step 3: Report

Present fitness dashboard:

Fitness: 5/7 passing (71%)

Gates:
  [PASS] build-passing (weight 8)
  [FAIL] test-passing (weight 7)
    └─ 3 test failures in pool_test.go

Directives:
  1. Expand Test Coverage — gap (no recent test additions)
  2. Reduce Complexity — partially-addressed (2 refactors this week)

Init Mode

ao goals init

Or with defaults:

ao goals init --non-interactive

Creates a new GOALS.md with mission, north/anti stars, first directive, and auto-detected gates. Error if file already exists.

Post-Init Enrichment

After ao goals init creates the scaffold, enrich it with product-aware content that the CLI cannot auto-detect:

Enrich North Stars with Outcomes

Review the generated north stars. If they are all feature-focused (e.g., "skills work across 4 runtimes"), nudge toward outcome-focused stars:

  • Feature-focused (weaker): "Skills work across 4 runtimes"
  • Outcome-focused (stronger): "A new user goes from install to first validated workflow in under 5 minutes"

Ask the user: "Your north stars describe features. What user outcome would tell you the product is actually working?" Add at least one outcome-focused star.

Enrich Anti-Stars from Failure Modes

Scan for proven failure patterns:

  1. Check .agents/retro/ — extract failure themes from retrospectives
  2. Check .agents/council/ or council index — look for FAIL verdicts and their root causes
  3. Check .agents/learnings/ — look for learnings tagged as anti-patterns

Convert the top 3 most common failure modes into anti-stars. Examples from real data:

  • "Product promises with no automated verification" (from council FAILs where claims had no gates)
  • "Goals that measure code metrics instead of user outcomes" (from retros where passing gates didn't improve product)
  • "Capture without compounding" (from flywheel analysis where knowledge was stored but never retrieved)

If no .agents/ data exists, use the defaults from ao goals init.

Add Product Directives

The CLI generates engineering-flavored directives (test coverage, complexity, lint). After init, also suggest product/growth directives by asking:

  1. "What's your biggest product gap right now?" → directive with steer: decrease
  2. "What user behavior do you want to increase?" → directive with steer: increase
  3. "What metric would tell you the product is working?" → directive with measurable target

Product directives sit alongside engineering ones in the same ## Directives section. See references/generation-heuristics.md for product directive patterns.

Add Product Gates

Check what product infrastructure exists and suggest appropriate gates:

InfrastructureSuggested Gate
.agents/learnings/ existsflywheel-compounding — knowledge above escape velocity
skills/quickstart/ existsquickstart-under-5min — onboarding time gate
docs/comparisons/ existscompetitive-freshness — comparison docs updated within 45 days
PRODUCT.md existsproduct-gaps-tracked — Known Gaps section has entries
ao flywheel status worksflywheel-promotion-rate — learnings promoted above threshold

Only suggest gates for infrastructure that actually exists. Don't create gates for aspirational features.

Steer Mode — Orient/Decide

Step 1: Show Current State

Run measure mode first to show current fitness and directive status.

Step 2: Propose Adjustments

Based on measurement:

  • If a directive is fully addressed → suggest removing or replacing
  • If fitness is declining → suggest new gates
  • If idle rate is high → suggest new directives

Product-aware steering: Also check for product dimension gaps:

  • If all directives are engineering-flavored (test, lint, build, refactor) → suggest at least one product/growth directive
  • If no directive cites a specific metric → flag: "Vague directives are a smell. Can any of these reference a specific number?"
  • If .agents/retro/ has new failure patterns not represented in anti-stars → suggest adding them
  • If PRODUCT.md has Known Gaps not covered by any directive → suggest a directive to close the gap

Step 3: Execute Changes

Use CLI commands:

ao goals steer add "Title" --description="..." --steer=increase
ao goals steer remove 3
ao goals steer prioritize 2 1

Add Mode

Add a single goal to the goals file. Format-aware — writes to GOALS.yaml or GOALS.md depending on which format is detected.

ao goals add <id> <check-command> --weight=5 --description="..." --type=health
FlagDefaultDescription
--weight5Goal weight (1-10)
--descriptionHuman-readable description
--typeGoal type (health, architecture, quality, meta)

Example:

ao goals add go-coverage-floor "bash scripts/check-coverage.sh" --weight=3 --description="Go test coverage above 60%"

Drift Mode

Compare the latest measurement snapshot against a previous one to detect regressions.

ao goals drift                    # Compare latest vs previous snapshot

Reports which goals improved, regressed, or stayed unchanged.

History Mode

Show measurement history over time for all goals or a specific goal.

ao goals history                        # All goals, all time
ao goals history --goal go-coverage     # Single goal
ao goals history --since 2026-02-01     # Since a specific date
ao goals history --goal go-coverage --since 2026-02-01  # Combined

Useful for spotting trends and identifying oscillating goals.

Export Mode

Export the latest fitness snapshot as JSON for CI consumption or external tooling.

ao goals export

Outputs the snapshot to stdout in the fitness snapshot schema (see references/goals-schema.md).

Meta Mode

Run only meta-goals (goals that validate the validation system itself). Useful for checking allowlist hygiene, skip-list freshness, and other self-referential checks.

ao goals meta --json

See references/goals-schema.md for the meta-goal pattern.

Validate Mode

ao goals validate --json

Reports: goal count, version, format, directive count, any structural errors or warnings.

Prune Mode

ao goals prune --dry-run    # List stale gates
ao goals prune              # Remove stale gates

Identifies gates whose check commands reference nonexistent paths. Removes them and re-renders the file.

Migrate Mode

Convert between goal file formats.

ao goals migrate --to-md      # Convert GOALS.yaml → GOALS.md
ao goals migrate               # Migrate GOALS.yaml to latest YAML version

The --to-md flag creates a GOALS.md with mission, north/anti stars sections, and converts existing goals into the Gates table format. The original YAML file is backed up.

Examples

Checking fitness and directive gaps

User says: /goals

What happens:

  1. Runs ao goals measure --json to get gate results
  2. If GOALS.md format, runs ao goals measure --directives to get directive list
  3. Assesses each directive against recent work
  4. Reports combined fitness + directive gap dashboard

Result: Dashboard showing gate pass rates and directive progress.

Bootstrapping goals for a new project

User says: /goals init

What happens:

  1. Runs ao goals init which prompts for mission, stars, directives, and auto-detects gates
  2. Creates GOALS.md in the project root

Result: New GOALS.md ready for /evolve consumption.

Adding a new goal after a post-mortem

User says: /goals add go-parser-fuzz "cd cli && go test -fuzz=../internal/goals/ -fuzztime=10s" --weight=3 --description="Markdown parser survives fuzz testing"

What happens:

  1. Runs ao goals add with the provided arguments
  2. Writes the new goal in the correct format (YAML or Markdown)

Result: New goal added, measurable on next /goals run.

Troubleshooting

ProblemCauseSolution
"goals file already exists"Init called on existing projectUse /goals to measure, or delete file to re-init
"directives require GOALS.md format"Tried steer on YAML fileRun ao goals migrate --to-md first
No directives in measure outputGOALS.yaml doesn't support directivesMigrate to GOALS.md with ao goals migrate --to-md
Gates referencing deleted scriptsScripts were renamed or removedRun /goals prune to clean up
Drift shows no historyNo prior snapshots savedRun ao goals measure at least twice first
Export returns emptyNo snapshot file existsRun ao goals measure to create initial snapshot

See Also

  • /evolve — consumes goals for fitness-scored improvement loops
  • references/goals-schema.md — schema definition for both formats
  • references/generation-heuristics.md — goal quality criteria

Reference Documents

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.46%
按下载量换算1,195

Claude

30.86%
按下载量换算985

Cursor

16.48%
按下载量换算526

Gemini CLI

10.02%
按下载量换算320

安全审计

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通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/boshu2/agentops --skill goals 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

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