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/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/commands/shaheerkhawaja/productionos/self-eval"><img src="https://agentmods.dev/badge/commands/shaheerkhawaja/productionos/self-eval.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 | $0.00043 | $0.01229 |
| Opus 5.5 | $0.00017 | $0.00492 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo 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 6mo ago), licensed MIT. It adds 43 tokens to every session and 1,229 once invoked, about $0.0002 per session on Opus 5.5. 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.
self-eval compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| self-evalShaheerKhawaja/ProductionOS | Command | 8 | 6mo ago | MIT | A | 43 | Claude Code |
| rekindlejustinjdev/fellowship | Command | 5 | 1mo ago | Apache-2.0 | A | 43 | Claude Code |
| settingsjustinjdev/fellowship | Command | 5 | 1mo ago | Apache-2.0 | A | 30 | Claude Code |
| validate-docsjustinjdev/fellowship | Command | 5 | 1mo ago | Apache-2.0 | A | 18 | Claude Code |
| chroniclejustinjdev/fellowship | Command | 5 | 1mo ago | Apache-2.0 | A | 38 | Claude Code |
| guidejustinjdev/fellowship | Command | 5 | 1mo ago | Apache-2.0 | A | 27 | Claude Code |
| red-bookjustinjdev/fellowship | Command | 5 | 1mo ago | Apache-2.0 | A | 31 | Claude Code |