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.
npx skills add deepklarity/harness-kit --skill hk-autonomy-auditgit clone --depth 1 https://github.com/deepklarity/harness-kitWrote 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/skills/deepklarity/harness-kit/hk-autonomy-audit)<a href="https://agentmods.dev/skills/deepklarity/harness-kit/hk-autonomy-audit"><img src="https://agentmods.dev/badge/skills/deepklarity/harness-kit/hk-autonomy-audit/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/skills/deepklarity/harness-kit/hk-autonomy-audit"><img src="https://agentmods.dev/badge/skills/deepklarity/harness-kit/hk-autonomy-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 161 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00155 | $0.02276 |
| Opus 5.5 | $0.00062 | $0.00910 |
| Sonnet 5.5 | $0.00031 | $0.00455 |
| Haiku 4.5 | $0.00015 | $0.00228 |
Grade A, and why
hk-autonomy-audit 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 1mo 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/hk-autonomy-audit — Autonomous Loop-Closing Readiness Audit
You're auditing whether the tooling, docs, commands, and flows in a given area are sufficient for an AI agent to autonomously solve problems — from first symptom to verified fix — without stopping to ask a human.
This is not a documentation quality check. It's an operational readiness assessment. The question isn't "do docs exist?" but "if an agent hit a wall here at 3am, could it get itself unstuck?"
Target
<audit_target> $ARGUMENTS </audit_target>
If the target is empty or vague, ask the user:
- What area or flow should be audited? (e.g., "odin task execution", "taskit API debugging", "reflection quality issues")
- Is there a specific scenario that prompted this? (a recent failure where an agent got stuck is the best input)
If the user provides a doc path, start there but don't stop there — trace outward to the commands, tools, and flows the doc references.
The Mental Model
An AI agent closing the loop on a problem goes through six stages. A gap at any stage breaks the chain:
DISCOVER → DIAGNOSE → HYPOTHESIZE → FIX → VERIFY → DOCUMENT
↓ ↓ ↓ ↓ ↓ ↓
"Something "The root "Changing "Apply "Confirm "Record what
is wrong" cause is X should the it works happened and
Y because fix it change end-to- why"
Z" because W" itself end"
Each stage needs specific resources. The audit checks whether those resources exist, are discoverable, and are actually usable by an agent (not just by a human who knows where to look).
Process
Step 1: Scope the audit
Read the target area's CLAUDE.md, AGENTS.md, and any referenced docs. Build a mental map of:
- What problems can occur here? (error types, failure modes, misconfigurations)
- What tools exist for this area? (diagnostic scripts, CLI commands, log files)
- What docs cover this area? (guides, patterns, solutions)
Don't read everything — scan headings and structure first. Depth comes in Step 2 when you know where to look.
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.
- 1mo ago First seen · 180 lines · 155 tokens per session scan A 130dc8653ed5
hk-autonomy-audit is a skill published in the GitHub repository deepklarity/harness-kit (102 stars, last pushed 3mo ago), licensed MIT. It adds 155 tokens to every session and 2,276 once invoked, about $0.0006 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.
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