tagore

KO | EN

gitlite — search

#python #javascript #android #hacktoberfest #api #php #twitter #unity #pytorch #cpp #laravel #bitcoin

★ 51 Open GitHub ↗

Make AI-generated prose sound human. 29-pattern catalog + 8-rule operating system + 8-dimension scoring rubric. Works with Claude Code, OpenCode, Copilot CLI, Codex, Gemini, Goose and Hermes.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Tagore

![Tagore](https://raw.githubusercontent.com/apurvrdx1/tagore/main/assets/social-preview.png)

> *"The butterfly counts not months but moments, and has time enough."*
> — Rabindranath Tagore

A skill for Claude Code, OpenCode, GitHub Copilot CLI, OpenAI Codex CLI, Gemini CLI, Goose, and Hermes (NousResearch agent runtime). Run `/tagore` on the AI-drafted prose you just generated; it rewrites the text to sound human and scores how close it got. Named in homage to Rabindranath Tagore, whose prose carried what frontier models reach for and miss: a point of view, specificity over abstraction, and restraint over puffery.

[![Buy Me A Coffee](https://img.buymeacoffee.com/button-api/?text=Buy+me+a+coffee&emoji=&slug=apurvrdx&button_colour=FFDD00&font_colour=000000&font_family=Lato&outline_colour=000000&coffee_colour=ffffff)](https://www.buymeacoffee.com/apurvrdx)

## What it does

Tagore attacks slop in two directions at once:

- **Removes the tells.** 29 cataloged AI patterns get scrubbed: significance inflation, em dash overuse, vague attributions, AI vocabulary, rule of three, copula avoidance, and more.
- **Adds what's missing.** Point of view, stakes, specificity, restraint, varied rhythm, and trust in the reader.

The skill runs every rewrite through a six-stage pipeline ending in an 8-dimension scoring rubric (5 mechanics + 3 substance) with a 56/80 pass threshold and subtotal floors so a piece can't pass on mechanics alone.

## Why merge two skills

Tagore is a synthesis of two complementary skills:

| Source | Contribution |
|--------|--------------|
| [humanizer](https://github.com/blader/humanizer) by [blader](https://github.com/blader) (based on Wikipedia "Signs of AI writing") | 29-pattern catalog, voice calibration, personality/soul section, self-audit loop, full worked example |
| [stop-slop](https://github.com/hardikpandya/stop-slop) by [Hardik Pandya](https://hvpandya.com) | 8 core principles, 12-item pre-delivery checklist, scoring rubric |

humanizer is a deep catalog with a pipeline. stop-slop is a tight rulebook with a scoring gate. Tagore fuses them into a single workflow and adds three substance dimensions (Specificity, Restraint, Voice) that neither skill scored on its own.

## Install

### One-liner (auto-detect)

```bash
curl -fsSL https://raw.githubusercontent.com/apurvrdx1/tagore/main/install.sh | bash
```

The installer detects every supported harness on your system and installs Tagore into each. It also drops a universal symlink at `~/.agents/skills/tagore/` so any agent honoring the cross-tool convention can find it.

### Install for one harness only

```bash
curl -fsSL https://raw.githubusercontent.com/apurvrdx1/tagore/main/install.sh | bash -s -- --platform claude
```

Supported `--platform` values: `claude`, `opencode`, `copilot`, `codex`, `gemini`, `goose`, `hermes`, `agents` (universal).

### Install for every detected harness

```bash
git clone https://github.com/apurvrdx1/tagore.git && cd tagore && ./install.sh --all
```

### Manual install

Pick your harness and copy the `tagore/` directory (with `SKILL.md` inside) into the matching location:

| Harness | Global install path | Per-project alternative |
|---------|---------------------|-------------------------|
| Claude Code | `~/.claude/skills/tagore/` | `.claude/skills/tagore/` |
| OpenCode | `~/.config/opencode/skills/tagore/` | `.opencode/skills/tagore/` |
| GitHub Copilot CLI | `~/.copilot/skills/tagore/` | `.github/skills/tagore/` |
| OpenAI Codex CLI | `~/.codex/skills/tagore/` | `.agents/skills/tagore/` |
| Gemini CLI | `~/.gemini/skills/tagore/` | — |
| Goose | `~/.config/goose/skills/tagore/` | — |
| Hermes (NousResearch) | `~/.hermes/skills/writing/tagore/` (or `$HERMES_HOME/skills/writing/tagore/`) | `.hermes/skills/writing/tagore/` |
| Cursor | — | `.cursor/rules/tagore/` |
| Windsurf | — | `.windsurf/rules/tagore/` |
| Universal (cross-tool) | `~/.agents/skills/tagore/` | `.agents/skills/tagore/` |

The universal `~/.agents/skills/` path is recognized by OpenCode, Copilot CLI, and Codex, so installing there once covers three harnesses.

## Usage

### Claude Code

```
/tagore Humanize this draft: [paste text]
```

Or invoke the skill explicitly via the Skill tool. Provide a writing sample inline for voice calibration:

```
/tagore Humanize this. Use my style from this sample: [sample]. Now rewrite: [draft]
```

### OpenCode

```
@tagore Humanize this draft: [paste text]
```

Voice calibration works the same way: paste a sample with your prose alongside the draft.

### GitHub Copilot CLI

```
/tagore [paste text]
```

After copying the skill into the directory, run `/skills reload` (or restart the CLI) so Copilot picks it up. Verify with `/skills info tagore`.

### OpenAI Codex CLI

```
codex
> /tagore Humanize this draft: [paste text]
```

Codex auto-loads everything under `~/.codex/skills/`; no reload step required.

### Hermes (NousResearch)

Hermes scans `$HERMES_HOME/skills/` (default `~/.hermes/skills/`) for category-grouped skills as `<category>/<name>/SKILL.md`. The installer drops Tagore under the `writing/` category so it's discoverable alongside other writing skills.

Invoke by name in an agent task:

```
Use the tagore skill to humanize this draft: [paste text]
```

For containerized Hermes deployments (e.g. NousResearch reference image, MRTek's PaperClip/Hermes stack), set `HERMES_HOME` before running the installer so it targets the in-container path:

```bash
HERMES_HOME=/paperclip/.hermes ./install.sh --platform hermes
```

If the host has the parent-repo skill-overlay convention (e.g. MRTek's `apps/hermes/overrides/skills/playbooks/`), copy the `tagore/` directory there instead — the container entrypoint syncs overlays into `$HERMES_HOME/skills/` on each restart with `cp -rn`, so the agent picks up the skill across deploys.

### Gemini CLI / Goose

Invoke by name and paste the text. Same six-stage pipeline runs regardless of the harness.

## The pipeline

```
0. (Optional) Voice calibration from sample
1. Draft rewrite — apply the 8 core principles, scrub the 29 patterns
2. Pre-delivery checklist — 12 mechanical yes/no checks
3. Score 1–10 on eight dimensions (5 mechanics + 3 substance, revise if < 56/80)
4. Self-audit — "What makes this still obviously AI generated?"
5. Final rewrite incorporating the audit
6. (Optional) Brief change summary
```

## The scoring rubric

### Mechanics (carried from stop-slop)

| Dimension | Question |
|-----------|----------|
| Directness | Statements or announcements? |
| Rhythm | Varied or metronomic? |
| Trust | Respects reader intelligence? |
| Authenticity | Sounds human? |
| Density | Anything cuttable? |

### Substance (extracted from humanizer)

| Dimension | Question | Catches |
|-----------|----------|---------|
| Specificity | Names the actual thing, or gestures at categories? | Vague attributions, knowledge-cutoff hedging, generic positive conclusions |
| Restraint | States things at their actual size, or puffs them up? | Significance inflation, notability puffery, promotional language |
| Voice | Has a point of view, or neutral wire-copy? | Failure of personality: opinions, stakes, mixed feelings, first-person where appropriate |

**Pass threshold:** 56/80 (70%), with no subtotal failing on its own (Mechanics ≥ 35/50, Substance ≥ 21/30).

A piece can pass mechanics and fail substance. That's the "clean but soulless" failure mode the substance dimensions specifically catch.

## What sets Tagore apart

Plenty of "anti-slop" prompts exist. Tagore's distinguishing features:

1. **Quantitative gate.** 8 dimensions, 80 points, 56 to pass. Forces a numerical decision instead of vibes.
2. **Two failure modes named explicitly.** Inflated slop (puffery) vs. flattened slop (passive narrator-from-a-distance). The rubric tests both.
3. **Self-audit loop built into the process.** "What makes this still obviously AI generated?" → revise. Catches what the catalog scrub missed.
4. **Voice calibration from a sample.** Match the user's actual writing instead of producing generic "human" output.
5. **Adds, not just removes.** The Personality and Soul section prescribes what to put in (opinions, stakes, mixed feelings), not just what to take out.

## Examples

See [examples/essays-with-scores.md](examples/essays-with-scores.md) for full before/after rewrites with scoring breakdowns.

## License

MIT. See [LICENSE](LICENSE).

## Credits

Built from two excellent prior works:
- **[humanizer](https://github.com/blader/humanizer)** by [blader](https://github.com/blader), based on [Wikipedia:Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) (WikiProject AI Cleanup).
- **[stop-slop](https://github.com/hardikpandya/stop-slop)** by [Hardik Pandya](https://hvpandya.com).

Both are MIT-licensed; this merge is too.

## Contributing

Issues and PRs welcome. The most valuable contributions:
- New AI tells the catalog misses (with before/after examples)
- Edge cases where the rubric scores wrong
- Voice calibration improvements for non-English text
- Translations of the skill into other languages

## Support

If tagore saved you from sounding like a chatbot in a doc that mattered, drop a coffee. The catalog grows from real submissions, and your support buys time to read them, score them, and ship them in the next release.

[![Buy Me A Coffee](https://img.buymeacoffee.com/button-api/?text=Buy+me+a+coffee&emoji=&slug=apurvrdx&button_colour=FFDD00&font_colour=000000&font_family=Lato&outline_colour=000000&coffee_colour=ffffff)](https://www.buymeacoffee.com/apurvrdx)

## Reference

> "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."
> — WikiProject AI Cleanup

Good writing is rarely the most statistically likely thing. That gap is what Tagore tries to close.

tagore

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README