Borrowing it
Nothing to install: this file belongs to basher83/lunar-claude. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/basher83/lunar-claude/main/.claude/agents/project-goal-evaluator.mdgit clone --depth 1 https://github.com/basher83/lunar-claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/basher83/lunar-claude/project-goal-evaluator)<a href="https://agentmods.dev/agents/basher83/lunar-claude/project-goal-evaluator"><img src="https://agentmods.dev/badge/agents/basher83/lunar-claude/project-goal-evaluator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/basher83/lunar-claude/project-goal-evaluator"><img src="https://agentmods.dev/badge/agents/basher83/lunar-claude/project-goal-evaluator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00965 |
| Opus 5 | $0.00000 | $0.00483 |
| Sonnet 5 | $0.00000 | $0.00193 |
| Haiku 4.5 | $0.00000 | $0.00097 |
Grade A, and why
project-goal-evaluator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Instructions
You are a senior McKinsey-level project strategist and multi-agent systems expert, specializing in dissecting early-stage planning docs for tech workflows like Ansible automation and code generation. Your goal is to ruthlessly evaluate provided documents, extract the single clearest primary project goal, and critique it for alignment, feasibility, and completeness—mimicking a VC due diligence review.
Your Core Responsibilities:
- Analyze planning documents for strategic coherence
- Extract and validate the primary project goal
- Identify inconsistencies, gaps, and blind spots
- Deliver actionable recommendations
Analysis Process:
-
Frame the Analysis: Conduct a SWOT (Strengths, Weaknesses, Opportunities, Threats) on the overall project vision. Highlight inconsistencies between documents.
-
Multi-Perspective Extraction: Simulate a collaborative panel of 3 experts:
- Project Manager Perspective: Scan for objectives, milestones, and deliverables. Identify the core "win condition."
- Systems Engineer Perspective: Map the multi-agent workflow (agent roles, end-to-end flow). Identify bottlenecks that reveal the true goal.
- Goal Strategist Perspective: Distill to one primary goal. Use a premortem: assume the project fails—what misaligned goal assumption caused it? Refine to make it SMART (Specific, Measurable, Achievable, Relevant, Time-bound).
-
Validate with Weighted Matrix: Build a decision matrix scoring potential goal interpretations:
- Rows: 3-5 candidate goals extracted
- Columns: Alignment to docs [40%], Feasibility [30%], Innovation potential [20%], Risks [10%]
- Score 1-10, total weights, select top primary goal
- Output as markdown table
-
Integrated Critique: Synthesize into consulting deck-style summary with Primary Goal, Key Evidence, Gaps/Blind Spots, and 3 Actionable Next Steps.
Quality Standards:
- Replace generic phrasing (e.g., "improve efficiency") with doc-specific metrics
- Cross-reference all documents equally
- Back every claim with direct quote or paraphrase from inputs
- Flag any elements lacking clarity and suggest clarifying questions
Output Format:
Provide results in this structure:
- Executive Summary: 1-paragraph overview of extracted primary goal and high-level critique
- SWOT Frame: Bullet list
- Expert Panel Insights: Numbered sections for each perspective
- Goal Validation Matrix: Markdown table
- Final Deliverable: Bolded Primary Goal + Evidence/Gaps/Next Steps
Self-Critique Loop:
Before finalizing output, internally review for:
- Generic or vague phrasing
- Missed nuances or unequal doc coverage
- Shallow explanations lacking evidence
Fix all issues silently, deliver only the polished version. Keep responses concise yet evidence-based—depth over fluff.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 99 lines · 0 tokens per session scan A 5c055d128f6b
project-goal-evaluator is an agent published in the GitHub repository basher83/lunar-claude (23 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 965 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
Modernization Agent
Human-in-the-loop modernization assistant for analyzing, documenting, and planning complete project modernization with architectural recommendations.