agent-readiness

agent-readiness is a skill for Claude Code, Codex from JKHeadley/instar. It costs 38 tokens per session (582 once invoked), scanned A, original, MIT.

A tool that rates whether a task or workflow is suitable for an AI agent by comparing routine coordination with human judgment. Coordination includes routing information and tracking status, while judgment involves ambiguity and exceptions.

In plain words
What is it for?
Use it to assess individual tasks or multi-step workflows and choose between automation, agent assistance, or human-led work.
Why use it?
It helps decide which work can be delegated safely and which work needs a person to make decisions.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to assess individual tasks or multi-step workflows and choose between automation, agent assistance, or human-led work.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jkheadley/instar/agent-readiness
Install

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.

Any agent
npx skills add JKHeadley/instar --skill agent-readiness
Clone the repo
git clone --depth 1 https://github.com/JKHeadley/instar

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agent-readiness

README.md
[![agentmods](https://agentmods.dev/badge/skills/jkheadley/instar/agent-readiness.svg)](https://agentmods.dev/skills/jkheadley/instar/agent-readiness)
Your own site
<a href="https://agentmods.dev/skills/jkheadley/instar/agent-readiness"><img src="https://agentmods.dev/badge/skills/jkheadley/instar/agent-readiness.svg" alt="Measured on agentmods" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 582 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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 Data Exfiltration · line 29
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.00582
Opus 5 $0.00019 $0.00291
Sonnet 5 $0.00008 $0.00116
Haiku 4.5 $0.00004 $0.00058

Measured 8d ago against content hash bdcf9fade891, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

agent-readiness scanned grade A with 1 finding 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 8d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X POST -H "Authorization: Bearer $AUTH" -H 'Content-Type: application/json' \
skills/agent-readiness/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/agent-readiness

Salim Ismail's EXO 3.0 diagnostic, made runnable: score a piece of work on its coordination-vs-judgment ratio. Coordination work — routing information, approvals, scheduling, status tracking, prescriptive/standardized steps — is what AI agents do best, so it's agent-ready. Judgment work — resolving ambiguity, handling exceptions, navigating relationships, making a call with no playbook — should stay with (or escalate to) humans.

When to use

  • Before delegating a task/workflow to an agent — is it actually a good candidate?
  • When deciding whether a process should be fully automated, agent-with-oversight, hybrid, or kept human-led.
  • When mapping which of your workflows a small team + agents could rebuild first (Salim: "every task that scores high on coordination has agent readiness").

How

Score a task:

curl -X POST -H "Authorization: Bearer $AUTH" -H 'Content-Type: application/json' \
  -d '{"task":{"name":"Invoice intake","description":"Route invoices, schedule approvals, track status, compile a weekly report, notify owners."}}' \
  http://localhost:${INSTAR_PORT:-4042}/agent-readiness/score

Score a workflow (by its steps):

curl -X POST -H "Authorization: Bearer $AUTH" -H 'Content-Type: application/json' \
  -d '{"workflow":{"steps":["Fetch the record","Assign accounts","Schedule orientation","Update the tracker"]}}' \
  http://localhost:${INSTAR_PORT:-4042}/agent-readiness/score

Returns:

{
  "coordinationSignals": 5, "judgmentSignals": 0,
  "coordinationRatio": 1.0, "overallReadiness": 100,
  "recommendation": "deploy-agent",
  "reason": "...",
  "matched": { "coordination": ["route","schedule",...], "judgment": [] }
}

recommendation is one of: deploy-agent (75+), agent-with-oversight (55–74), hybrid (40–54), human-led (<40). Deterministic + advisory — it answers a question; it never blocks. Pair it with the MTP Protocol (/intent/org/test-action) to check both "is this agent-ready?" and "does our purpose endorse it?"

Read the full file on GitHub · 56 lines

Changes

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.

  1. 8d ago First seen · 56 lines · 38 tokens per session scan A bdcf9fade891

Subscribe to this mod's changes

agent-readiness is a skill published in the GitHub repository JKHeadley/instar (79 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 582 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.