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 sananthanarayan/skilldrop --skill ai-readiness-assessmentgit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/ai-readiness-assessment)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/ai-readiness-assessment"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-readiness-assessment/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/sananthanarayan/skilldrop/ai-readiness-assessment"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-readiness-assessment.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 | $0.00117 | $0.01530 |
| Opus 5 | $0.00059 | $0.00765 |
| Sonnet 5 | $0.00023 | $0.00306 |
| Haiku 4.5 | $0.00012 | $0.00153 |
Grade A, and why
ai-readiness-assessment 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 10d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-readiness-assessment
Produce a baseline a leadership team can act on: where the organisation actually stands on the six things that gate AI adoption, what the evidence is for each score, and which gaps block adoption first. The output is a scored table plus a ranked gap list — not a maturity label and not a slide.
Readiness assessments fail in one of two ways: a number with no evidence behind it, or a diagnosis with no next action. This skill refuses both.
How to respond
-
Establish scope and the decision it feeds. Whose readiness — one team, a function, the whole company? And what happens with the answer (a go/no-go, a budget request, a sequencing decision)? Scope changes what counts as evidence. Cap clarifying questions at 2.
-
Score the six dimensions. These are fixed — do not invent new ones for a single engagement, and do not drop one because it's awkward to assess. Score each 0–4 (0 absent · 1 ad hoc · 2 repeatable · 3 managed · 4 optimised):
Dimension The question it answers Data Is the material these tools need reachable, current, and permitted to be used? Tooling Are licences, access, and environments actually in people's hands? Skills Can people write a decent prompt, judge an output, and know when not to trust it? Governance Is there a rule for what may be put in, and a named owner for it? Process Does the workflow have a place for a machine draft plus a human review gate? Culture Is the incentive to use it, or to hide that it was used? -
Attach evidence to every score. One line naming what you observed — a system, a document, a stated practice, a number. A score with no evidence line is deleted, not softened. Where the input doesn't support a score, emit
[insufficient evidence: <what to collect>]rather than guessing a middle number. -
Rank the gaps by what blocks first, not by what scores lowest. A 1 in Governance that blocks every use case outranks a 0 in Culture that blocks nothing yet. Name the dependency: "Skills cannot move until Tooling ≥ 2 — people can't practise on access they don't have."
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 68 lines · 117 tokens per session scan A 5944a22e0922
ai-readiness-assessment is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 27d ago), licensed MIT. It adds 117 tokens to every session and 1,530 once invoked, about $0.0006 per session on Opus 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 2026-08-31.
Other skills, from other repositories
readme
This skill should be used to audit OR elevate the README of a public repository. Trigger with "improve the README", "make the readme high quality", "elevate this readme", "audit the readme", "add badges", "is our README good", or before publishing/releasing a repo. Elevate rebuilds a thin/generic README to a polished…
cairn-attention
Resolve Cairn's pending-attention queue inline (DEC drafts, baseline findings, drift events).
cairn-direction
Spec-tightener + subagent dispatcher. Engage on code-change asks — verbs, bug reports, observations. Pivot-aware on active tasks.
cairn-resync
Operator-initiated re-discovery — resolve config drift, re-cluster topics, re-curate grown areas into DEC/INV drafts.
ship
Execute an approved pitch/spec as a delegated, closed-scope cycle with verification and adversarial review. Invoke it yourself with /ship — a cycle spawns a writer and opens a PR, so it starts when you say so. Enforces the execution playbook: right-size gate, pre-spawn filter, doc-bundle, PR format, review.
adr-new
Create a new Architecture Decision Record with append-only, status-gated supersession, and update the ADR index. Invoke with /adr-new, or let /tdd-author invoke it on approval of an ADR action (this skill stays model-invocable for that reason).