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/self-eval)<a href="https://agentmods.dev/commands/shaheerkhawaja/productionos/self-eval"><img src="https://agentmods.dev/badge/commands/shaheerkhawaja/productionos/self-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 | $0.00043 | $0.01229 |
| Opus 5 | $0.00022 | $0.00615 |
| Sonnet 5 | $0.00009 | $0.00246 |
| Haiku 4.5 | $0.00004 | $0.00123 |
Grade A, and why
self-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 6d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/self-eval — Self-Evaluation Command
You are the self-evaluation orchestrator. You evaluate the quality, necessity, and correctness of recent work using the ProductionOS Self-Eval Protocol.
Step 0: Preamble
Before executing, run the shared ProductionOS preamble (templates/PREAMBLE.md).
Step 1: Determine Evaluation Target
Based on $ARGUMENTS.target:
target = "last"
# Find the most recent agent output
ls -t .productionos/*.md .productionos/**/*.md 2>/dev/null | head -5
Read the most recent artifact. This is your evaluation target.
target = "session"
# Find all work done this session
ls -t .productionos/self-eval/ 2>/dev/null | head -20
git log --oneline --since="4 hours ago" 2>/dev/null | head -20
git diff --stat HEAD~10 2>/dev/null | tail -5
Evaluate ALL work produced this session. Produce a session-level summary.
target = "diff"
# Evaluate the current git diff
git diff --stat 2>/dev/null
git diff --name-only 2>/dev/null
Evaluate all uncommitted changes against the self-eval protocol.
target = specific path
Read the specified file and evaluate it.
Step 2: Dispatch Self-Evaluator Agent
Read agents/self-evaluator.md and dispatch:
Agent tool:
description: "self-evaluator: Evaluate {target description}"
prompt: "{self-evaluator role + instructions}\n\nTASK: Evaluate {target}\nDEPTH: $ARGUMENTS.depth\nOUTPUT: .productionos/self-eval/{timestamp}-eval.md"
Step 3: Process Results
Read the evaluation output. Based on score:
Score >= 8.0 — PASS
✅ Self-Eval PASS (X.X/10)
{summary of findings}
Logged to .productionos/self-eval/{file}
Score 6.0-7.9 — CONDITIONAL (self-heal if enabled)
If $ARGUMENTS.heal is "on":
- Read the lowest-scoring questions
- Generate targeted fix instructions
- Dispatch the original agent (or self-healer) to address issues
- Re-run self-eval (max 3 loops)
- Report final result
If $ARGUMENTS.heal is "off":
⚠️ Self-Eval CONDITIONAL (X.X/10)
{issues that need attention}
Run /self-eval --heal on to attempt self-fix
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.
- 6d ago First seen · 159 lines · 43 tokens per session scan A da1165908eca
self-eval is a command published in the GitHub repository ShaheerKhawaja/ProductionOS (8 stars, last pushed 4mo ago), licensed MIT. It adds 43 tokens to every session and 1,229 once invoked, about $0.0002 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.