goals▌
boshu2/agentops · updated May 23, 2026
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Maintain GOALS.yaml and GOALS.md fitness specifications. Use ao goals CLI for all operations.
/goals — Fitness Goal Maintenance
Maintain GOALS.yaml and GOALS.md fitness specifications. Use
ao goalsCLI 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:
- Check
.agents/retro/— extract failure themes from retrospectives - Check
.agents/council/or council index — look for FAIL verdicts and their root causes - 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:
- "What's your biggest product gap right now?" → directive with
steer: decrease - "What user behavior do you want to increase?" → directive with
steer: increase - "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:
- Runs
ao goals measure --jsonto get gate results - If GOALS.md format, runs
ao goals measure --directivesto get directive list - Assesses each directive against recent work
- 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:
- Runs
ao goals initwhich prompts for mission, stars, directives, and auto-detects gates - 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:
- Runs
ao goals addwith the provided arguments - 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 loopsreferences/goals-schema.md— schema definition for both formatsreferences/generation-heuristics.md— goal quality criteria
Reference Documents
How to use goals on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches goals from GitHub repository boshu2/agentops and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★68 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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