catalogify

Turn a repository into a knowledge catalog your AI agent can afford to read.

catalogify generates an Open Knowledge Format (OKF v0.1) bundle: a directory of cross-linked markdown concepts with YAML frontmatter describing a codebase's services, modules, APIs, data models and operations. Where git is available it mines the history for the reasoning behind the code, and whatever it cannot establish it parks as an open question rather than inventing an answer.

• Monorepo or single repo. One Service concept per deployable unit, scoped by folder — a repo with hundreds of services produces the same directory layout as a single-service repo, just wider.
• Cheap on large codebases. Scanning 25,917 files and 500,022 lines of Kubernetes takes 2.1 seconds. A service entry is ~676 tokens whether the service is 10k lines or 100k.
• Git optional. History mining and incremental updates need it; everything else does not.

It installs as a portable Agent Skill, so it works in Claude Code, Cursor, OpenAI Codex, and anything else that reads the open .agents/skills standard. For Spec Kit projects the same workflows ship as slash commands in speckit_okf.

uv tool install catalogify
catalogify install

Then ask, in plain language:

"generate a knowledge catalog for this repo"

Why it exists

Giving an agent "context on the codebase" is two problems.

Routing — which of our 90 services does this spec touch? Wide and shallow. You need a little about everything.

Reaching — inside that service, what changes? Narrow and deep. You need everything about a little.

A code graph such as Graphify — which parses your code with tree-sitter and builds a queryable graph of every symbol and call edge — is excellent at reaching and will beat prose every time. Ask it "what breaks if I change this function" and it answers precisely. catalogify targets routing instead, where the winning property is being small enough that an agent can read the whole estate in one call.

The two compose well: route with catalogify to pick the services, then run Graphify inside the one you picked. They are not alternatives.

Measured on pkg/kubelet from kubernetes/kubernetes (108,648 lines of Go), with Graphify run over the same directory:

Artifact	Size	Tokens
Graphify graph.json	14.5 MB	3,813,486
Graphify wiki/ (446 articles)	1,004 KB	256,968
catalogify bundle (9 concepts)	21.9 KB	5,596
catalogify service entry	2.6 KB	676

At 676 tokens per service, a 90-service catalog is roughly 61,000 tokens and fits in one call alongside the specification. See Benchmark to reproduce these numbers.

What it runs on

Monorepos and single repos alike. Concepts are scoped by folder, so a repository holding hundreds of services gets one Service concept per deployable unit and its own modules/, apis/ and data/ concepts underneath — the same directory layout a single-service repo produces, just wider. Point it at the whole tree or at one subdirectory.

Large codebases stay cheap, because the catalog describes the repo rather than reproducing it. Scanning all of kubernetes/kubernetes — 25,917 files and 500,022 lines of Go — takes 2.1 seconds and yields a 56 KB inventory (~14,000 tokens) for the agent to plan from. Cost scales with the number of things worth naming, not with lines of code: a service entry is ~676 tokens whether the service is 10k lines or 100k.

With or without git. History mining is a bonus, not a requirement:

	With git	Without git
Inventory, concepts, indexes, validation	yes	yes
Churn ranking, the "why" from reverts and hotfixes	yes	—
Commit citations, resource: URLs from the remote	yes	—
Incremental update	yes	— (re-run generate)
Conformant bundle	yes	yes

Outside a repository the inventory reports git.is_git_repo: false, history prints a notice and exits cleanly, timestamps come from file modification times, and log.md records Commit: `none` , which the validator accepts. The agent is told to raise more open_questions in that case, since without history the reasoning behind the code can only come from you.

What a concept looks like

---
type: Module
title: Container Manager (cm)
description: Owns cgroup hierarchy, CPU/memory/device allocation, and the
  on-disk checkpoints that let those allocations survive a kubelet restart.
tags: [cgroups, cpumanager, checkpoint, qos]
source_files: [pkg/kubelet/cm]
open_questions:
  - "After the V2→V3 migration fix was reverted, is the V3→V2 hybrid-state
     hazard still live, or was it addressed another way?"
---

# Gotchas

Checkpoint format changes are the highest-risk edit in this module, and the
hazard is the fallback path. When a V3 checkpoint has an invalid checksum,
restore falls back to V2, but the V3 fields already read stay in the struct,
producing a hybrid of V2 and V3 data (`83f1cae9656`, reverted by
`76100602564`).

That gotcha is not in a comment, a docstring, or an ADR. It was recovered from the commit log by following a revert back to the commit it reverted.

source_files maps the concept to code, which is what makes incremental updates possible. open_questions is where uncertainty goes instead of into prose. Both are producer extension fields permitted by OKF §4.1.

Why mine history at all

The gotcha in that example exists in no comment, no docstring, and no design document — in Kubernetes, a project with KEPs, a design-proposals archive, and reviewers who demand rationale. It survived only as a revert.

That is not an oversight, it is the normal condition. Michael Polanyi called it tacit knowledge in 1966: "we know more than we can tell." Peter Naur applied it to software in Programming as Theory Building (1985), arguing that the real product of programming is the theory of the system held in the developers' heads, and that program text and documentation are insufficient carriers of it. A program whose original team has dispersed is, in his terms, dead — and a new team patching it produces characteristically wrong fixes that erode the system's conceptual integrity.

That is precisely the position an AI agent is in on first contact with your repository. It arrives after the team has dispersed, holding the artefacts and none of the theory.

Nor does writing a specification escape it. Fred Brooks, in No Silver Bullet: "the complexity of software is an essential property, not an accidental one," so "descriptions of a software entity that abstract away its complexity often abstract away its essence." That ceiling applies whether a human or a model wrote the spec.

Commit history is a narrow exception. Nobody writes a revert as documentation; they write it because production broke, leaving a dated, attributed, immutable record of a place where the theory and the code disagreed. catalogify history goes looking for exactly those.

This recovers fragments, not the theory. For everything still missing, the generator raises an open_question and the clarify workflow asks a human while there is still a human to ask.

The four workflows

Ask in plain language and the skill picks one:

Workflow	What it does
generate	Inventory scan, git-history mining, concept plan, concept documents, index.md files, log.md, validation.
update	Diffs since the last logged commit; refreshes only stale concepts, deprecates orphans, adds new ones, preserves human curation.
clarify	Asks you about the open_questions the other workflows parked, then folds the answers in as cited, curation-protected knowledge.
validate	OKF §9 conformance check plus a quality spot-check.

Commands

The deterministic work is three subcommands you can run yourself, with or without an agent. Each accepts --help.

catalogify inventory                        # repo facts + git churn, as JSON
catalogify history pkg/foo --limit 5        # the reverts and hotfixes behind a path
catalogify validate knowledge/              # OKF v0.1 §9 conformance
catalogify install [--list] [--uninstall]   # manage the agent skill

• inventory writes JSON: file tree, languages, entry points, dependency manifests, API definitions, schemas, CI/CD, docs, ADRs, plus per-file commit churn. On the full Kubernetes tree (500k lines, 25,917 files) it takes 2.1 seconds and produces 56 KB.
• history returns the creation commit, recent subjects, and the revert / hotfix / risk-flagged commits where invariants hide. Diff-free by default so historical secrets do not leak; --patch opts in.
• validate enforces OKF §9: four error classes, nine warning classes.

okf-inventory, okf-history and okf-validate remain as aliases from the package's previous life as okf_skill.

Requirements

• Python 3.9+
• bash — present on Linux and macOS; on Windows the wrapper finds the bash.exe that ships with Git for Windows.
• git — optional. Used for churn ranking, history mining and incremental updates. Everything else works without it; see What it runs on.

Install

uv tool install catalogify     # or: pip install catalogify
catalogify install

Restart your agent afterwards so it picks up the skill. catalogify install copies it into every agent's user-global skills directory (~/.claude/skills, ~/.cursor/skills, ~/.codex/skills, ~/.agents/skills). Use --agents claude,cursor to target specific ones and --scope project to install into ./.<agent>/skills instead.

The bundled install.sh / install.ps1 do both steps, install uv if it is missing, and fall back to pip install --user.

Using it with Spec Kit

The same four workflows are also packaged as a Spec Kit extension at alexcpn/speckit_okf, for teams who prefer explicit slash commands over asking in plain language:

specify extension add okf --from \
  https://github.com/alexcpn/speckit_okf/archive/refs/tags/v0.3.0.zip

Spec Kit command	Equivalent here
/speckit.okf.generate	"generate a knowledge catalog for this repo"
/speckit.okf.update	"refresh the catalog"
/speckit.okf.clarify	"resolve the open questions"
/speckit.okf.validate	catalogify validate knowledge/

Both produce the same OKF v0.1 bundle. Use catalogify if you want the standalone CLI and an agent skill that loads on its own; use the extension if your project already runs Spec Kit and you want the workflows as commands alongside your other /speckit.* ones. Installing both would register two skills covering the same ground, so pick one.

Usage

agent "generate a knowledge catalog for this repo"
agent "refresh the catalog, the code has moved on"
agent "resolve the open questions in the catalog"
agent "validate the catalog"

Output lands in knowledge/ (configurable), ready to commit next to the code:

knowledge/
├── index.md            # okf_version: "0.1" + directory of everything
├── log.md              # dated history, each block records a commit SHA
├── architecture/
│   └── overview.md     # type: Reference — the "start here" concept
├── services/…          # type: Service
├── modules/…           # type: Module
├── apis/…              # type: API Endpoint / API Resource
├── data/…              # type: Data Model / Database Table
└── operations/…        # type: Pipeline / Configuration / Playbook

On a monorepo, start with granularity: coarse and raise OKF_INVENTORY_CAP above its default of 150. Raw churn skews toward generated files and build config, so invest in the exclude list.

Configuration

Everything works with no config. To change the bundle directory, resource URI base, excludes, type mappings, layout, granularity, or the clarify question budget, copy the annotated template into your repo root:

cp ~/.claude/skills/catalogify/okf-config.template.yml .okf-config.yml

catalogify inventory --config .okf-config.yml and catalogify validate <dir> --config .okf-config.yml honor its exclude list; the agent passes it through automatically once the file exists.

Safety properties

• Never guesses. Unverifiable facts become open_questions, resolved by the clarify workflow and marked with <!-- clarified: ... --> sentinels that later updates will not overwrite. Your answer outranks the machine's inference permanently.
• Never deletes curation. Removed code marks a concept status: deprecated rather than deleting it. Human prose survives every refresh.
• Never emits secrets. Config values are described by shape, never value, including from history. The validator flags anything that slips through (W5).
• Never touches source code. All writes stay inside the bundle directory.

Benchmark

To reproduce the table above:

git clone --filter=blob:none --no-tags \
  https://github.com/kubernetes/kubernetes.git k8s

# Graphify, for comparison
pip install graphifyy
graphify update k8s/pkg/kubelet
cd k8s/pkg/kubelet && graphify export wiki
wc -c graphify-out/graph.json          # 15,253,944
cat graphify-out/wiki/*.md | wc -c     # 1,027,874

# catalogify
cd ../..                               # back to the k8s repo root
catalogify inventory                   # 2.1s, 56 KB of JSON
catalogify history pkg/kubelet/cm --limit 3

# then ask your agent to generate the catalog, and check it:
catalogify validate knowledge/

Token counts are bytes ÷ 4. Measured against kubernetes/kubernetes at commit d5ccf7968e5. The structural graph was built AST-only (no API key), so its wiki lacks LLM community labels.

Uninstall

catalogify install --uninstall    # remove from every agent's skills dir
uv tool uninstall catalogify

Run catalogify install --list first to see where it is installed.

License

MIT