skillify

A process for turning a rough feature into a complete, tested capability for a coding agent. It defines the instructions, code, tests, evaluation steps, and activation rules the capability needs.

In plain words
What is it for?
Use it to create or improve agent skills, add routing triggers, build supporting scripts, write unit and integration tests, and evaluate steps involving language models.
Why use it?
It helps prevent agent capabilities from being incomplete, hard to trigger, or insufficiently tested. It also uses reviews and evaluations to improve the result before tests preserve it.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/timurgaleev/memex/skillify
Any agent
npx skills add timurgaleev/memex --skill skillify
Clone the repo
git clone --depth 1 https://github.com/timurgaleev/memex

Made for: Claude Code, Codex.

Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,195 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00069 $0.03195
Opus 5 $0.00034 $0.01597
Sonnet 5 $0.00014 $0.00639
Haiku 4.5 $0.00007 $0.00319

Measured 2d ago against content hash 1dae9d1bab15, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

deploy/skills/skillify/SKILL.md · 324 lines

How it starts

The opening of the file, as written. The whole thing — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skillify — The Meta Skill

Relationship to /cross-modal-review: That skill is the manual mid-flow "second opinion" gate (one model reviews work product before commit). This skill's Phase 3 below uses memex eval cross-modal instead — three frontier models from different model families score-and-iterate on a documented dimension list before tests cement behavior. Use /cross-modal-review for ad-hoc second opinions; use Phase 3 here when skillifying a feature.

Contract

A feature is "properly skilled" when all 11 checklist items pass. Item 3 (cross-modal eval) is informational in v1.1.0 — it does not gate the skillpack-check audit, but a missing or stale receipt is surfaced so the user knows where the gate stands.

The Checklist

□ 1.  SKILL.md           — skill file with frontmatter + contract + phases
□ 2.  Code               — deterministic script if applicable
□ 3.  Cross-modal eval   — 3 frontier models from 3 families; informational
□ 4.  Unit tests         — cover every branch of deterministic logic
□ 5.  Integration tests  — exercise live endpoints
□ 6.  LLM evals          — quality/correctness cases for LLM-involving steps
□ 7.  Routing triggers   — frontmatter `triggers:` with real user trigger phrases
□ 8.  Resolver eval      — test that triggers route to this skill
□ 9.  Check-resolvable   — DRY + MECE audit, no orphans
□ 10. E2E test           — smoke test: trigger → side effect
□ 11. Brain filing       — if it writes pages, filing entry per _brain-filing-rules.md

Phase 0: Should This Be a Skill?

Before skillifying, check:

  • Will this be invoked 2+ times? (One-off work ≠ skill)
  • Is there >20 lines of logic? (Trivial helpers don't need full infrastructure)
  • Does it have a clear trigger phrase a user would actually say?

If ANY answer is no, it's a script, not a skill — stop here. Do not scaffold, write a SKILL.md, run evals, or write tests for it. Tell the user why and move on.

Scope check (upper bound): one skill = one capability = one coherent trigger family. If the target spans multiple distinct intents users would invoke separately ("run the build" / "roll back the deploy" / "notify the team" are three intents, not one), do NOT build one skill covering them all. Stop, propose splitting into separate skillify targets, and ask the user which one to skillify first.

Read the full file on GitHub · 324 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. 2d ago First seen · 324 lines · 69 tokens per session scan A 1dae9d1bab15

Subscribe to this mod's changes

skillify is a skill published in the GitHub repository timurgaleev/memex (8 stars, last pushed 8d ago), licensed MIT. It adds 69 tokens to every session and 3,195 once invoked, about $0.0003 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.

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