# Okf Generator

> >

- **Type:** Skill
- **Install:** `agentstack add skill-umairbaig8-okf-generator-okf-generator`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [UmairBaig8](https://agentstack.voostack.com/s/umairbaig8)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [UmairBaig8](https://github.com/UmairBaig8)
- **Source:** https://github.com/UmairBaig8/okf-generator

## Install

```sh
agentstack add skill-umairbaig8-okf-generator-okf-generator
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# OKF Generator & Lookup Skill

Generates structured OKF v0.2 knowledge bundles from codebases (Python, JS/TS,
Go, Java, Rust, Ruby via tree-sitter AST), and provides fast concept lookup for
AI agents like OpenCode.

## Pipeline Overview

```
codebase
   |
   v
okf generate  -->  okf_bundle/          (domain/resource-path layout)
                       |
                okf lookup               (zero-LLM concept search)
                       |
                okf pairs          -->  okf_pairs.jsonl  (training data)
```

## CLI Reference

All features via single `okf` CLI (installed from PyPI).

| Command | Purpose |
|---------|---------|
| `okf generate` | Scan codebase and write OKF bundle |
| `okf lookup` | Search bundle and return exact concept |
| `okf pairs` | Convert bundle to JSONL training pairs |
| `okf summarize` | Regenerate SUMMARY.md from existing bundle |

## Dependencies

```bash
pip install okf-generator
# With LLM enrichment:
pip install "okf-generator[llm]"
```

---

## Task: Generate OKF Bundle

**When**: user says "index my codebase", "generate OKF bundle", "extract knowledge from code"

### Static extraction (no LLM — always run this first)
```bash
okf generate  
```

### With LLM enrichment (fills missing docstrings and descriptions)
```bash
OKF_ENRICH=1 \
OKF_BASE_URL="http://localhost:8080/v1" \
OKF_API_KEY="llamabarn" \
OKF_MODEL="ggml-org/gemma-3-4b-it-qat-GGUF:Q4_0" \
OKF_MAX_WORKERS=2 \
okf generate  
```

Enrichment is **resumable** — rerun safely if interrupted. Already-enriched
concepts are skipped automatically (checks disk on every run).

### Key env vars (enrichment only)

| Var | Default | Purpose |
|-----|---------|---------|
| `OKF_ENRICH` | `0` | Set `1` to enable LLM enrichment |
| `OKF_BASE_URL` | `https://api.anthropic.com/v1` | OpenAI-compat endpoint |
| `OKF_API_KEY` | `` | API key |
| `OKF_MODEL` | `claude-sonnet-4-6` | Enrichment model |
| `OKF_MAX_WORKERS` | `2` | Parallel workers (keep low for local LLMs) |

### Output layout (mirrors source tree)
```
okf_bundle/
├── SUMMARY.md              /
    └── /
        ├── index.md        .md     .md   output.jsonl

# With LLM (QA, doc, summarize pairs)
SYNTH_BASE_URL="http://localhost:8080/v1" \
SYNTH_API_KEY="llamabarn" \
SYNTH_MODEL="ggml-org/gemma-3-4b-it-qat-GGUF:Q4_0" \
MAX_WORKERS=2 \
QA_PER_CONCEPT=3 \
okf pairs  output.jsonl

# Specific pair types only
PAIR_TYPES="codegen,qa" okf pairs  output.jsonl
```

### Pair types

| Type | Static | LLM | Covers |
|------|--------|-----|--------|
| `codegen` | yes | yes | Functions, Classes |
| `qa` | no | yes | All (purpose/params/return/edge) |
| `doc` | no | yes | Functions, Classes |
| `summarize` | yes | yes | Modules, Classes |
| `crosslink` | yes | no | All with related concepts |

---

## Task: Regenerate SUMMARY.md Only

```bash
okf summarize 
```

Use after enrichment finishes to refresh the summary without re-scanning.

---

## OpenCode Integration

See `references/opencode-integration.md` for full setup.

Quick setup:
```bash
# 1. Add to AGENTS.md (auto-loaded by OpenCode)
echo "OKF bundle at ./okf_bundle — use: okf lookup " >> AGENTS.md

# 2. Add lookup command
mkdir -p .opencode/commands
echo "RUN okf lookup --bundle ./okf_bundle \$NAME" \
  > .opencode/commands/lookup.md
```

---

## Supported Languages

| Language | Parser | Extracts |
|----------|--------|---------|
| Python | stdlib ast | functions, classes, params, return types, docstrings |
| JS / TS | tree-sitter | functions, arrow fns, classes, JSDoc |
| Go | tree-sitter | funcs, methods, structs, interfaces, GoDoc |
| Java | tree-sitter | classes, methods, constructors, Javadoc |
| Rust | tree-sitter | fns, structs, enums, traits, impl blocks, doc comments |
| Ruby | tree-sitter | defs, classes, modules, hash comments |

---

## Troubleshooting

**No concepts found**: Check that source dir is not inside a SKIP_DIRS name
(node_modules, .venv, dist, etc). Leading path components like `/tmp` are
no longer skipped (fixed in v0.1.3).

**Enrichment slow**: Use `OKF_MAX_WORKERS=1`. Local models process ~1 request
at a time. At 32 tok/sec expect ~3-5s per concept.

**Enrichment interrupted**: Rerun same command. Enriched files are skipped.

**JS/TS concepts missing**: Ensure tree-sitter-typescript is installed.
TypeScript uses a separate grammar from JavaScript.

**Lookup wrong result**: Add --type or --file to narrow the search scope.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [UmairBaig8](https://github.com/UmairBaig8)
- **Source:** [UmairBaig8/okf-generator](https://github.com/UmairBaig8/okf-generator)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-umairbaig8-okf-generator-okf-generator
- Seller: https://agentstack.voostack.com/s/umairbaig8
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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