Skip to content
Agentmart
Submit a tool

agentops-audit

Audits and improves agent/skill specs with a scored rubric and routing rationale.

Skill by ivegamsft

IndexedMIT★ 4
Write a review

Chalk Labs

Need help deploying agentops-audit?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/ivegamsft-agentops-audit

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Audits and improves agent/skill specs with a scored rubric and routing rationale. USE FOR: scoring spec quality, identifying concrete fixes, producing revised specs, validating cost/latency fit, generating routing profiles. DO NOT USE FOR: implementing product features, unrelated code review, infrastructure deployment.

Use cases: devops, security, agent-building

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/agentops-audit

What's inside

Skill name
agentops-audit
Description
Audits and improves agent/skill specs with a scored rubric and routing rationale. USE FOR: scoring spec quality, identifying concrete fixes, producing revised specs, validating cost/latency fit, generating routing profiles. DO NOT USE FOR: implementing product features, unrelated code review, infrastructure deployment.
License
MIT
Metadata
domain: agent-development · category: agent-development · maturity: production
Instructions
65 lines in SKILL.md · view on GitHub

Agent Skills spec issues

  • compatibility: must be text of at most 500 characters
  • allowed-tools: must be a space-separated string

Files (3)

  • SKILL.md
  • agentops-audit.eval.yaml
  • eval.yaml

SKILL.md (first lines)

# Agent Operations Audit Skill

Audit agent or skill specifications and produce an actionable scorecard, risk list, and revised spec.

## USE FOR

- Scoring existing specs using a 0-5 rubric
- Finding ambiguity, safety gaps, and tool-policy issues
- Recommending concrete, prioritized fixes
- Producing a revised spec with measurable success criteria
- Generating routing rationale and estimated turn profile
- Auditing model IDs and reasoning-effort compatibility against the generated capability catalog
- Supporting `audit` and `create_and_audit` flows in `agent-designer`

## DO NOT USE FOR

- Writing product application code
- Infrastructure deployment and operations
- General-purpose code review outside agent/skill definitions

## Required Scoring Dimensions (0-5)

- `clarity`
- `safety_compliance`
- `tool_correctness`
- `ambiguity_handling`
- `cost_latency_fit`
- `eval_readiness`

## Audit Deliverables

- `scorecard`
- `risks`
- `concrete_fixes`
- `revised_spec`

## Output Contract

Always include:

1. **Task-shaping classification** (`execution_mode`, `estimated_turns`, `tool_profile`, `uncertainty`)
2. **Routing profile**:
   - `recommended_class`: `Fast | Balanced | Deep | Tool-Strict`
   - `rationale`
   - `estimated_turns`: `1 | 2-3 | 4+`
   - `risk_mitigations`
3. **Artifacts**:
   - `audit_report`
   - `agent_spec` when a revised or regenerated spec is produced

## Guardrails

- Do not assume model family names are portable across providers.
- Prefer measurable success criteria over subjective language.
- Escalate when constraints conflict (for example: fast + deep reasoning + lowest cost).
- Read `docs/reference/model-capabilities.json` before recommending a model.
- Treat `reasoning_depth` as task metadata; do not emit `reasoning_effort` unless
  the selected model advertises configurable reasoning.
- Report unknown model IDs and fixed-effort models separately. Effective
  organization and user entitlement remains a runtime check.

## Related Skills

- `agent-design` — authoring and scaffolding agent/skill assets

From ivegamsft/basecoat, MIT.

More from ivegamsft/basecoat

Signals

Stars
4
Forks
1
Open issues
10
Last commit (this folder)
Aug 9, 2026 (2 months ago)
Last release
Oct 11, 2026 (20 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from ivegamsft

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get agentops-audit running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying agentops-audit?

CallGet help
Cite this page

agentops-audit skill. Agent Mart. https://agentmart.dev/skills/ivegamsft-agentops-audit (last updated 2026-10-24)