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.
git clone --depth 1 https://github.com/kunalsuri/ai-fication-kitWrote 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/commands/kunalsuri/ai-fication-kit/create-feature-catalog)<a href="https://agentmods.dev/commands/kunalsuri/ai-fication-kit/create-feature-catalog"><img src="https://agentmods.dev/badge/commands/kunalsuri/ai-fication-kit/create-feature-catalog/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/commands/kunalsuri/ai-fication-kit/create-feature-catalog"><img src="https://agentmods.dev/badge/commands/kunalsuri/ai-fication-kit/create-feature-catalog.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 | $0.00021 | $0.00358 |
| Opus 5.5 | $0.00008 | $0.00143 |
| Sonnet 5.5 | $0.00004 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
create-feature-catalog 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 28d 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.
What it actually says
Build the feature catalog — the highest-value artifact for agents. Budget significant
exploration; if a repo-explorer chat mode is available, switch to it for the heavy
reading (it is read-only by design and protects this context window).
Method
- Start from user-visible surfaces: routes, UI entry points, CLI commands, public APIs. Each surface is a candidate feature.
- For each feature, trace the touch list across layers: UI, backend/services, persistence (tables/collections/files), and tests that exercise it.
- Cluster and name features the way a USER would name them, not by module names.
Output — ai/analysis/FEATURE_CATALOG.md
For every feature: name · business goal (one line) · touch list per layer · verifying tests · related features. End with two sections agents use most:
- "Where new code lives" — a decision tree from feature-type to target directories.
- The 3-file rule — for each feature, the 3 files to read first to understand it.
Rules
- Every entry
[inferred]. Where a layer can't be confirmed, write "UNSURE". - Do not modify source. Update
ai/guide/FEATURE_MAP.mdcandidate list to reference the catalog, nothing more. - Print a sampling guide at the end: the 5 entries a human should spot-check first (pick the ones you are least sure of).
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.
- 28d ago First seen · 30 lines · 21 tokens per session scan A f480be73999c
create-feature-catalog is a command published in the GitHub repository kunalsuri/ai-fication-kit (3 stars, last pushed today), licensed Apache-2.0. It adds 21 tokens to every session and 358 once invoked, about $0.0001 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 2026-09-18.
Other commands, from other repositories
terse
Switch reply compression level — /terse [lite|full|ultra|off]. Complements RTK (input savings) with output-side savings.
start
Start a session — load context + memory and brief yourself on current state.
update
Mid-session checkpoint — save progress without ending the session.
motion
Motion work is applied within whatever component/section/page workflow is already running. If motion is the entire ask with nothing else changing, this command's steps above are sufficient on their own.
typography
This command is a lookup + decision, not a multi-step build — it doesn't need its own workflow file. If the request grows into "and now build the page using this type system," hand off to page or redesign.
cluster-instincts
Group related instincts by tag overlap and batch-promote qualifying clusters to skills. Usage - /mindforge:cluster-instincts [--dry-run] [--min-cluster 3] [--min-confidence 0.7].