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 JKHeadley/instar --skill agent-readinessgit clone --depth 1 https://github.com/JKHeadley/instarWrote 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/jkheadley/instar/agent-readiness)<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>- 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 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.
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 | $0.00038 | $0.00582 |
| Opus 5 | $0.00019 | $0.00291 |
| Sonnet 5 | $0.00008 | $0.00116 |
| Haiku 4.5 | $0.00004 | $0.00058 |
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' \ 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?"
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.
- 8d ago First seen · 56 lines · 38 tokens per session scan A bdcf9fade891
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.
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