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 agentmods add skills/timurgaleev/memex/skillifynpx skills add timurgaleev/memex --skill skillifygit clone --depth 1 https://github.com/timurgaleev/memexWhat 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 | $0.00069 | $0.03195 |
| Opus 5 | $0.00034 | $0.01597 |
| Sonnet 5 | $0.00014 | $0.00639 |
| Haiku 4.5 | $0.00007 | $0.00319 |
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.
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 usesmemex eval cross-modalinstead — three frontier models from different model families score-and-iterate on a documented dimension list before tests cement behavior. Use/cross-modal-reviewfor 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.
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.
- 2d ago First seen · 324 lines · 69 tokens per session scan A 1dae9d1bab15
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.
Other skills, from other repositories
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