goals

boshu2/agentops · updated May 23, 2026

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

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

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

Format File Version Features
YAML GOALS.yaml 1-3 Goals with checks, weights, pillars
Markdown GOALS.md 4 Goals + mission + north/anti stars + directives

When both files exist, GOALS.md takes precedence.

Mode Selection

Parse the user's input:

Input Mode CLI Command
/goals, /goals measure, "goal status" measure ao goals measure
/goals init, "bootstrap goals" init ao goals init
/goals steer, "manage directives" steer ao goals steer
/goals add, "add goal" add ao goals add
/goals drift, "goal drift" drift ao goals drift
/goals history, "goal history" history ao goals history
/goals export, "export goals" export ao goals export
/goals meta, "meta goals" meta ao goals meta
/goals validate, "validate goals" validate ao goals validate
/goals prune, "prune goals", "clean goals" prune ao goals prune
/goals migrate, "migrate goals" migrate ao 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:

Infrastructure Suggested Gate
.agents/learnings/ exists flywheel-compounding — knowledge above escape velocity
skills/quickstart/ exists quickstart-under-5min — onboarding time gate
docs/comparisons/ exists competitive-freshness — comparison docs updated within 45 days
PRODUCT.md exists product-gaps-tracked — Known Gaps section has entries
ao flywheel status works flywheel-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
Flag Default Description
--weight 5 Goal weight (1-10)
--description Human-readable description
--type Goal 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

Problem Cause Solution
"goals file already exists" Init called on existing project Use /goals to measure, or delete file to re-init
"directives require GOALS.md format" Tried steer on YAML file Run ao goals migrate --to-md first
No directives in measure output GOALS.yaml doesn't support directives Migrate to GOALS.md with ao goals migrate --to-md
Gates referencing deleted scripts Scripts were renamed or removed Run /goals prune to clean up
Drift shows no history No prior snapshots saved Run ao goals measure at least twice first
Export returns empty No snapshot file exists Run 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

how to use goals

How to use goals on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add goals
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches goals from GitHub repository boshu2/agentops and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/goals

Reload or restart Cursor to activate goals. Access the skill through slash commands (e.g., /goals) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.568 reviews
  • Chen Ndlovu· Dec 28, 2024

    Solid pick for teams standardizing on skills: goals is focused, and the summary matches what you get after install.

  • Chen Lopez· Dec 28, 2024

    goals fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chen Brown· Dec 24, 2024

    goals reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Nikhil Martinez· Dec 24, 2024

    Registry listing for goals matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Nikhil Robinson· Dec 8, 2024

    goals is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • James Reddy· Dec 8, 2024

    Keeps context tight: goals is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chen Mehta· Nov 27, 2024

    goals has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chen Khanna· Nov 19, 2024

    Registry listing for goals matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Arjun Dixit· Nov 19, 2024

    We added goals from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Zaid Smith· Nov 15, 2024

    Solid pick for teams standardizing on skills: goals is focused, and the summary matches what you get after install.

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