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 phuonghx/aim-cli --skill skillifygit clone --depth 1 https://github.com/phuonghx/aim-cliWrote 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/phuonghx/aim-cli/skillify)<a href="https://agentmods.dev/skills/phuonghx/aim-cli/skillify"><img src="https://agentmods.dev/badge/skills/phuonghx/aim-cli/skillify/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/phuonghx/aim-cli/skillify"><img src="https://agentmods.dev/badge/skills/phuonghx/aim-cli/skillify.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 | $0.00092 | $0.00872 |
| Opus 5.5 | $0.00037 | $0.00349 |
| Sonnet 5.5 | $0.00018 | $0.00174 |
| Haiku 4.5 | $0.00009 | $0.00087 |
Grade A, and why
skillify 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skillify
A procedure you repeat is a procedure worth recording once. The third time you walk the same path, pave it — write it down as a skill so the path exists for everyone after you.
Decide whether it deserves a skill
Capture it when all of these hold:
- The same flavor of task has shown up roughly three or more times.
- It has a recognizable shape: several steps that stay constant from run to run.
- It would still make sense in a different repository, not just this one.
- A fresh agent with no prior context could act on it.
Skip it when any of these apply:
- It is a single occurrence — just do the task.
- It is glue specific to one codebase — that belongs in project memory, not a shared skill.
- A skill already covers the ground — search the skills directory before adding a duplicate.
How to extract one
1. Name the invariants
Pull the pattern apart into three buckets:
- Trigger — what kicks it off (a phrase, a file type, a domain).
- Constant steps — the parts that never change between runs.
- Variable parts — what differs each time, and the shape of the result.
2. Draft the file
Keep the frontmatter to just two keys. The description does the heavy lifting: it is
how the skill gets matched later, so write it in the third person and state both what
the skill does and when to reach for it (and when not to).
---
name: kebab-case-name
description: One to a few sentences, third person, describing what the skill does and the
situations that call for it, plus what it is not for. No first person.
---
# Skill Name
A short framing line — the principle behind the skill.
## When it applies
- Conditions that should trigger it
- Conditions that should rule it out
## Procedure
### 1. First action
Concrete, do-this instructions.
### 2. Next action
...
### N. Confirm it worked
How to tell the procedure succeeded.
## Guidance
A handful of rules worth remembering.
3. Put it where skills live
.aim-agents/skills/<skill-name>/SKILL.md
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 · 106 lines · 92 tokens per session scan A b542f9b09a9e
skillify is a skill published in the GitHub repository phuonghx/aim-cli (1 stars, last pushed 3mo ago), licensed MIT. It adds 92 tokens to every session and 872 once invoked, about $0.0004 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 2026-08-31.
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todos
Use when the user asks for a shared plan, a task needs at least three substantive execution steps, or a loose TODO is pending. Without an explicit plan request, not for one-shot answers or checks, or one- to two-step work that is not already in the list.
writing-workflow-skills
Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
web-platform-baseline
Use this skill before asserting that a CSS, HTML or accessibility capability is available, unavailable, or the right tool — it carries dated, refreshable platform facts and refuses to let stale knowledge be stated as current. Triggers: user says "container query", "can I use", "browser support", "modern CSS", ":has"…
doc-coauthoring
Collaboratively author technical specifications, RFCs, PRDs, ADRs, and decision documents through structured context gathering, iterative refinement, and blind reader testing. Use when drafting substantial documentation, architecture proposals, or engineering design records.