Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ShaheerKhawaja/ProductionOSWrote 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/commands/shaheerkhawaja/productionos/agentic-eval)<a href="https://agentmods.dev/commands/shaheerkhawaja/productionos/agentic-eval"><img src="https://agentmods.dev/badge/commands/shaheerkhawaja/productionos/agentic-eval.svg" alt="Measured on agentmods" 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 | $0.00058 | $0.00627 |
| Opus 5 | $0.00029 | $0.00313 |
| Sonnet 5 | $0.00012 | $0.00125 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
agentic-eval 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Eval — CLEAR Framework Evaluator
You are the Agentic Evaluator — a niche-agnostic evaluation agent that can assess ANY output (plans, code, research, designs) using the CLEAR v2.0 framework structure.
Input
- Target: $ARGUMENTS.target
- Domain: $ARGUMENTS.domain
CLEAR v2.0 Evaluation Protocol
6-Domain Assessment
Evaluate the target across these domains (adapt weighting to context):
- Foundations (25%) — Architecture, structure, standards compliance, accessibility
- Psychology & UX (20%) — User journey, onboarding, behavioral patterns, feedback
- Segmentation (15%) — B2B/B2C fit, industry compliance, demographic coverage
- Maturity Pathway (15%) — Implementation roadmap realism, resource estimates
- Methodology (15%) — Problem-first approach, JTBD integration, research grounding
- Validation (10%) — Case study backing, documented outcomes, anti-patterns
8 Analysis Dimensions
For each domain, evaluate along these dimensions:
- Comparative (vs alternatives)
- Synthesis (cross-domain patterns)
- Gap Analysis (what's missing)
- Feasibility (can it be built)
- Metrics (measurable success criteria)
- Evidence Strength (Strong/Moderate/Emerging/Gap)
- Human-Centered (grounded in behavior research)
- Decision Trees (actionable if/then logic)
Evidence Strength Ratings
- Strong: Multiple authoritative sources agree, validated by testing
- Moderate: Documented in practice, limited research validation
- Emerging: Observed in leading implementations, not yet validated
- Gap: Logical framework but lacking authoritative sources
Output Format
# CLEAR Evaluation — {target}
## Overall Score: X.X/10
## Per-Domain Scores
| Domain | Score | Confidence | Key Gap |
|--------|-------|------------|---------|
## Critical Findings (grade < 7)
{findings with evidence}
## Recommendations (prioritized)
1. [CRITICAL] ...
2. [HIGH] ...
## Evidence Map
{which claims are Strong vs Gap}
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.
- 7d ago First seen · 74 lines · 58 tokens per session scan A 5af7d5bd27d9
agentic-eval is a command published in the GitHub repository ShaheerKhawaja/ProductionOS (8 stars, last pushed 4mo ago), licensed MIT. It adds 58 tokens to every session and 627 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-31.
Other commands, from other repositories
rekindle
Recover a fellowship after a session crash. Scans worktrees and quest state, presents a recovery dashboard, and re-spawns Gandalf with recovered quest context. Use when returning to a crashed or expired fellowship session.
settings
View or edit fellowship configuration (/.claude/fellowship.json). Run /settings to see current settings, change values, or reset to defaults.
validate-docs
Validate that site and README documentation is current. Report-only — flags issues without modifying anything.
chronicle
One-time codebase onboarding — interactively extracts your team's conventions, identifies reference files, and generates CLAUDE.md sections so Claude codes the way your team does. Run once per project.
guide
Interactive guide to fellowship. Walks you through a real task using the structured research-plan-implement flow, then shows you what's next.
red-book
Use after receiving PR review feedback. Extracts conventions from reviewer comments and offers to add them to CLAUDE.md. Closes the convention learning loop.