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 rules/kunalsuri/ai-fication-kit/cold-startgit clone --depth 1 https://github.com/kunalsuri/ai-fication-kitWhat 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 | $0.00028 | $0.01571 |
| Opus 5 | $0.00014 | $0.00785 |
| Sonnet 5 | $0.00006 | $0.00314 |
| Haiku 4.5 | $0.00003 | $0.00157 |
Grade A, and why
cold-start 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the cold-start bootstrap. This is a one-time, read-and-write-docs-only pass. You will draft the AI metadata; a human will audit it afterward. Do NOT touch source code.
Step 0 — load the facts
Read ai/repo-profile.json (deterministic output of the kit's orient step). Treat its
stack facts as given; VERIFY its build/test commands against real config files before
writing them anywhere as confirmed. If the profile says this repo is a fork,
distinguishing OUR code from FROZEN upstream code is the single most important
output of this pass.
If the profile contains a humanContext block (captured by the first-run wizard), use
it — these are the user's own answers, not detection:
developer.skillLevel/developer.codebaseFamiliarity— calibrate your output. For a junior or someone new to this codebase, explain more, and lean MORE conservative on Stability (prefer?/frozenwhen unsure). For an expert who knows the repo, be terse.stack— ifkindissplit, expect distinct frontend/backend trees and map them separately; ifsingle, don't invent a split; ifunknown, flag the stack as a thing to resolve. The user'sdescription/frontend/backendstrings override loose guesses.branch/firstRunAt— provenance; record the first-run date inPROJECT_OVERVIEW.md.
Step 0.5 — absorb prior knowledge (Process 2 repos only)
Check repo-profile.json → maturity.process. If it is 2, backup files exist.
If CLAUDE_bkp_*.md or AGENTS_bkp_*.md exist at the repo root, they contain the
previous AI configuration written by the team or another tool. Extract and reuse:
- Read the backup files (glob
*_bkp_*.mdat the repo root). - Extract any information that is USEFUL for the
ai/knowledge layer:- Build/test commands (cross-reference with
repo-profile.json) - Project description and purpose
- Coding conventions and style rules
- Architecture notes (layers, boundaries, patterns)
- Known gotchas, forbidden patterns, or "never do X" rules
- Module or directory descriptions
- References to external systems or APIs
- Build/test commands (cross-reference with
- Merge extracted knowledge into the appropriate
ai/guide/documents:- Conventions →
ai/guide/CONVENTIONS.md - Architecture notes →
ai/guide/ARCHITECTURE.md - Module info →
ai/guide/MODULE_MAP.md(as seed rows) - Project purpose →
ai/guide/PROJECT_OVERVIEW.md
- Conventions →
- Enrich the new
CLAUDE.mdandAGENTS.mdwith any project-specific hard rules from the backups that are NOT already covered by the kit's templates (e.g., "never use ORM X", "always use Y pattern"). - Tag everything extracted as
[inferred — from prior config]so the human auditor knows the provenance.
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 · 110 lines · 28 tokens per session scan A 6d59a9bf3bb8
cold-start is a cursor rule published in the GitHub repository kunalsuri/ai-fication-kit (3 stars, last pushed 4d ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,571 once invoked, about $0.0001 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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