Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/yonatangross/orchestkitnpx agentmods add commands/yonatangross/orchestkit/assessWrote 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/yonatangross/orchestkit/assess)<a href="https://agentmods.dev/commands/yonatangross/orchestkit/assess"><img src="https://agentmods.dev/badge/commands/yonatangross/orchestkit/assess/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/yonatangross/orchestkit/assess"><img src="https://agentmods.dev/badge/commands/yonatangross/orchestkit/assess.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 | $0.00072 | $0.06220 |
| Opus 5.5 | $0.00029 | $0.02488 |
| Sonnet 5.5 | $0.00014 | $0.01244 |
| Haiku 4.5 | $0.00007 | $0.00622 |
Grade A, and why
assess 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 — 443 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-generated from skills/assess/SKILL.md
Source: https://github.com/yonatangross/orchestkit
Assess
Host-neutral workflow. Invoke by skill name (assess). Claude Code slash routing, YAML hook loaders, and .claude/chain live in skills/assess/references/claude-code.md.
Comprehensive assessment skill for answering "is this good?" with structured evaluation, scoring, and actionable recommendations.
🎯 Quick Start
assess backend/app/services/auth.py
assess our caching strategy
assess --model=opus the current database schema
assess frontend/src/components/Dashboard
Effort levels (CC 2.1.111+ adds xhigh)
| Effort | Behavior |
|---|---|
low / medium |
Subset of dimensions, faster turnaround |
high (default) |
All six dimensions with pros/cons |
xhigh |
All six dimensions + one additional assessor pass focused on uncertainty/caveats; emits confidence per dimension |
xhighsilently falls back tohighon a model that does not implement it: no error, no log line.doctorCategory 14 reports this, and only when it can positively prove the active model lacks the tier.
Argument Resolution
Step 0: resolve a pronoun target first
$ARGUMENTS is often not a path. Load Read("../../shared/rules/target-resolution.md")
and follow its inline half: a bare pronoun or deictic (them, this, the above) or an
empty target is read back to the nearest concrete subject in the conversation, announced
in one line, and assessed. Ask only when the conversation is empty.
TARGET = "$ARGUMENTS" # Full argument string, e.g., "backend/app/services/auth.py"
# $ARGUMENTS[0] is the first token (CC 2.1.59 indexed access)
# Model override detection (CC 2.1.72)
MODEL_OVERRIDE = None
for token in "$ARGUMENTS".split():
if token.startswith("--model="):
MODEL_OVERRIDE = token.split("=", 1)[1] # "opus", "sonnet", "haiku", "fable"
TARGET = TARGET.replace(token, "").strip()
Pass MODEL_OVERRIDE to all Agent() calls via model=MODEL_OVERRIDE when set. Accepts symbolic names (opus, sonnet, haiku, fable on harnesses whose Agent tool lists it; note fable is premium API spend after 2026-07-12) or full IDs (claude-opus-5-5) per CC 2.1.74.
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 Changed · -15 lines ca0076b286f1
- 18d ago Changed 3f0892a6bdc9
- 19d ago Changed · +3 lines ff8f5ae49bd6
- 22d ago Changed d8f0513d5746
- 1mo ago Changed · +2 lines d8d04f04ae36
- 1mo ago Changed 954e3e164082
- 1mo ago First seen · 453 lines · 72 tokens per session scan A 6fadd5b3f9cb
assess is a command published in the GitHub repository yonatangross/orchestkit (296 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 6,220 once invoked, about $0.0003 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 2026-08-30.
Other commands, from other repositories
gc
On-demand code-drift / entropy scan (garbage collection) via a fresh-context drift-analyst subagent. Checks changed/active code against golden-rules.md + generic drift heuristics (dead code, duplication, doc-drift) and reports findings with an evidence path. Read-only — recommends, never auto-refactors. Not git gc.
pn-audit-api
Surgical API design review — REST conventions, status codes, response shapes, validation, and schema leaks. Use standalone or as part of pn-backend-audit. Outputs a scored fix roadmap.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.