Registry indexed

longform-to-shorts

Price unconfirmed★ 20 GitHub starsRegistry updated · Sep 30, 2026agent-skill

Overview

Cut standalone vertical Shorts/Reels (1080x1920) out of ONE finished long-form talking-head + screen-share video (a "N tools" walkthrough, tutorial, review, demo), one Short per topic/tool, then write per-Short YouTube + Instagram metadata. Use this whenever the user hands over a finished long-form video and wants short-form clips from it, even if they don't say the word "shorts": "cut shorts/reels from this video", "trim the short-form videos out of my long-form", "make reels from my youtube video", "turn each tool in my video into its own clip", "clip this up for tiktok/reels". Uses ffmpeg + a transcription skill for word timings. Do NOT use for raw multi-take footage (use shorts-from-takes) or a single freeform edit; this is specifically finished-long-form -> many standalone verticals.

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longform-to-shorts

Turn one finished long-form video into several standalone vertical Shorts, then write their metadata. Each Short must stand fully alone (no "first/next", no cross-reference), open on a clean full sentence, end clean, and look native to Reels: face zoomed, screen zoomed + scrolling, burned subtitles, a hook, a whoosh, a small speed-up.

This was distilled from a long real edit. The exact ffmpeg commands, the two-phase build, and the gotchas that each cost hours live in references/ffmpeg-recipes.md — read it before building any clip. The phases below are the plan and the judgment calls.

Setup

Source = one finished .mp4 (talking-head + screen-share, ~1080p). Needs ffmpeg/ffprobe and a transcription skill that can return word-level timings (this pairs with the watch/claude-video skill + a Groq/OpenAI Whisper key in ~/.config/watch/.env; line-level transcripts are too coarse to cut cleanly). Provide a short whoosh sound effect for transitions. Work in a shorts/ folder next to the video; keep _ref/ for frames + words.json.

The pipeline

1. Transcribe twice. Full clean transcript for reading/segmenting; word-level JSON for exact cut points. Commands in the reference.

2. Segment + trim (editorial). One Short per topic; target ~30–45s (pre-speedup; see Configuration). Every Short is standalone — cut all sequencing ("first/next", "moving on", "second one") and cross-references. Never open on a dangling connective ("but/so/and/that/okay"); start on a clean full sentence (an earlier sentence start often reads best). End on a complete sentence without clipping the last word. Mid-cuts to drop a redundant clause are fine — pick boundaries with a real gap, else remove the whole clause rather than leave a leftover fragment.

3. Face vs screen. Sample frames (~every 8s) to map talking-head vs screen-share spans. The critical rule: switch to the screen crop only when the screen content actually appears, not when the speaker starts mentioning it — otherwise you crop an empty room for a few seconds while they lean to bring the window up. Confirm the real appearance time with 2s-interval frames at the boundary.

4. Reframe to fill 1080x1920. Face → center-crop on the face. Screen → zoom + slow vertical scroll (a static screen under voiceover looks dead). Screen-only (no face) → split-screen: screen scroll + a full-face PIP bubble. Transition dead-zone → freeze the first clean target frame over the audio. Filters in the reference.

5. Captions. Top hook (short, viewer-workflow tension). Burned subtitles: generate an SRT with scripts/gen_subs.py (maps word timings onto the edited timeline incl. gaps), then HAND-REVIEW and correct every SRT — raw Whisper drops words, mis-hears names, and duplicates; it is not postable. Chunk into natural phrases. Burn recipe in the reference.

6. Polish. Whoosh SFX at each face→screen transition; a small speed-up with pitch preserved (as a final pass so subtitles stay synced); fade in/out.

7. Verify before saying done (non-negotiable). Re-transcribe each Short: clean opening, clean end, no sequencing words, mid-cuts read naturally. Sample frames: face in-frame everywhere (no empty chair), subtitles positioned + readable, screen scrolls, no leftover tag. Confirm the sped audio is intelligible.

8. Per-Short metadata. For each Short: 3–4 tension/curiosity title options (Title Case, no emoji, no overclaim — don't credit a tool with a capability it doesn't have), a YouTube description that complements the clip (never restates the spoken lines) + link + hashtags + #Shorts, and a platform caption in the creator's voice. Write to shorts/METADATA.md.

Configuration (opinionated defaults, change to taste)

Baked into the recipes as defaults; edit for your style:

Subtitles: color/size/position via force_style (default white, bottom). See the reference.
Speed-up: default atempo=1.1 (pitch preserved). Set to 1.0 to disable.
Clip length: ~30–45s target.
Hook font: default Impact; change the hardcoded fontfile= (macOS path by default — swap for your OS).
Whoosh SFX: set the path in the phase-2 command.

Files

references/ffmpeg-recipes.md — every ffmpeg command + the gotchas. Read before building.
scripts/gen_subs.py — word-timings → per-Short SRT (edit the CLIPS dict; always hand-review output).

View source ↗

File metadata

name: longform-to-shorts
description: >
  Cut standalone vertical Shorts/Reels (1080x1920) out of ONE finished long-form talking-head + screen-share
  video (a "N tools" walkthrough, tutorial, review, demo), one Short per topic/tool, then write per-Short
  YouTube + Instagram metadata. Use this whenever the user hands over a finished long-form video and wants
  short-form clips from it, even if they don't say the word "shorts": "cut shorts/reels from this video",
  "trim the short-form videos out of my long-form", "make reels from my youtube video", "turn each tool in my
  video into its own clip", "clip this up for tiktok/reels". Uses ffmpeg + a transcription skill for word
  timings. Do NOT use for raw multi-take footage (use shorts-from-takes) or a single freeform edit; this is
  specifically finished-long-form -> many standalone verticals.
license: MIT
compatibility: Requires ffmpeg, ffprobe, and Python 3, plus a transcription skill that returns word-level timings (e.g. Whisper).
allowed-tools: Bash, Read, Write, Edit

View original text

---
name: longform-to-shorts
description: >
Cut standalone vertical Shorts/Reels (1080x1920) out of ONE finished long-form talking-head + screen-share
video (a "N tools" walkthrough, tutorial, review, demo), one Short per topic/tool, then write per-Short
YouTube + Instagram metadata. Use this whenever the user hands over a finished long-form video and wants
short-form clips from it, even if they don't say the word "shorts": "cut shorts/reels from this video",
"trim the short-form videos out of my long-form", "make reels from my youtube video", "turn each tool in my
video into its own clip", "clip this up for tiktok/reels". Uses ffmpeg + a transcription skill for word
timings. Do NOT use for raw multi-take footage (use shorts-from-takes) or a single freeform edit; this is
specifically finished-long-form -> many standalone verticals.
license: MIT
compatibility: Requires ffmpeg, ffprobe, and Python 3, plus a transcription skill that returns word-level timings (e.g. Whisper).
allowed-tools: Bash, Read, Write, Edit
---

# longform-to-shorts

Turn one finished long-form video into several **standalone** vertical Shorts, then write their metadata. Each Short must stand fully alone (no "first/next", no cross-reference), open on a clean full sentence, end clean, and look native to Reels: face zoomed, screen zoomed + scrolling, burned subtitles, a hook, a whoosh, a small speed-up.

This was distilled from a long real edit. The exact ffmpeg commands, the two-phase build, and the gotchas that each cost hours live in **`references/ffmpeg-recipes.md`** — read it before building any clip. The phases below are the plan and the judgment calls.

## Setup
Source = one finished `.mp4` (talking-head + screen-share, ~1080p). Needs `ffmpeg`/`ffprobe` and a transcription skill that can return **word-level timings** (this pairs with the `watch`/claude-video skill + a Groq/OpenAI Whisper key in `~/.config/watch/.env`; line-level transcripts are too coarse to cut cleanly). Provide a short **whoosh** sound effect for transitions. Work in a `shorts/` folder next to the video; keep `_ref/` for frames + `words.json`.

## The pipeline

**1. Transcribe twice.** Full clean transcript for reading/segmenting; word-level JSON for exact cut points. Commands in the reference.

**2. Segment + trim (editorial).** One Short per topic; target ~30–45s (pre-speedup; see Configuration). Every Short is standalone — cut all sequencing ("first/next", "moving on", "second one") and cross-references. Never open on a dangling connective ("but/so/and/that/okay"); start on a clean full sentence (an earlier sentence start often reads best). End on a complete sentence without clipping the last word. Mid-cuts to drop a redundant clause are fine — pick boundaries with a real gap, else remove the whole clause rather than leave a leftover fragment.

**3. Face vs screen.** Sample frames (~every 8s) to map talking-head vs screen-share spans. **The critical rule:** switch to the screen crop only when the screen content *actually appears*, not when the speaker starts mentioning it — otherwise you crop an empty room for a few seconds while they lean to bring the window up. Confirm the real appearance time with 2s-interval frames at the boundary.

**4. Reframe to fill 1080x1920.** Face → center-crop on the face. Screen → zoom + **slow vertical scroll** (a static screen under voiceover looks dead). Screen-only (no face) → split-screen: screen scroll + a full-face PIP bubble. Transition dead-zone → freeze the first clean target frame over the audio. Filters in the reference.

**5. Captions.** Top hook (short, viewer-workflow tension). Burned subtitles: generate an SRT with `scripts/gen_subs.py` (maps word timings onto the edited timeline incl. gaps), then **HAND-REVIEW and correct every SRT** — raw Whisper drops words, mis-hears names, and duplicates; it is not postable. Chunk into natural phrases. Burn recipe in the reference.

**6. Polish.** Whoosh SFX at each face→screen transition; a small **speed-up with pitch preserved** (as a final pass so subtitles stay synced); fade in/out.

**7. Verify before saying done (non-negotiable).** Re-transcribe each Short: clean opening, clean end, no sequencing words, mid-cuts read naturally. Sample frames: face in-frame everywhere (no empty chair), subtitles positioned + readable, screen scrolls, no leftover tag. Confirm the sped audio is intelligible.

**8. Per-Short metadata.** For each Short: **3–4 tension/curiosity title options** (Title Case, no emoji, no overclaim — don't credit a tool with a capability it doesn't have), a YouTube description that *complements* the clip (never restates the spoken lines) + link + hashtags + `#Shorts`, and a platform caption in the creator's voice. Write to `shorts/METADATA.md`.

## Configuration (opinionated defaults, change to taste)
Baked into the recipes as defaults; edit for your style:
- **Subtitles**: color/size/position via `force_style` (default white, bottom). See the reference.
- **Speed-up**: default `atempo=1.1` (pitch preserved). Set to 1.0 to disable.
- **Clip length**: ~30–45s target.
- **Hook font**: default Impact; change the hardcoded `fontfile=` (macOS path by default — swap for your OS).
- **Whoosh SFX**: set the path in the phase-2 command.

## Files
- `references/ffmpeg-recipes.md` — every ffmpeg command + the gotchas. Read before building.
- `scripts/gen_subs.py` — word-timings → per-Short SRT (edit the CLIPS dict; always hand-review output).
</content>

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Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access

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Source & usage notes

IndexedAI Reviewed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: nidhi-singh02/skills
License: MIT
Version: Unknown
Last GitHub push: Sep 30, 2026
Registry updated: Sep 30, 2026
Instruction path: skills/longform-to-shorts/SKILL.md @ c49f40308620

Version reported in registry metadata; check source releases before relying on it.

Quality

60/100

Promising

Trust

61/100

Sandbox only

Audit

75/100

Risky

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

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More details

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Creator: nidhi-singh02
Source: nidhi-singh02/skills
Indexed by: OpenAgentSkill community index

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nidhi-singh02

Registry indexed

longform-to-shorts

Review the source View on GitHub

Price unconfirmed★ 20 GitHub starsRegistry updated · Sep 30, 2026agent-skill

Overview

Read full documentation

Source documentation, not instructions for this website. Review permissions before running any commands.

longform-to-shorts

Setup

The pipeline

1. Transcribe twice. Full clean transcript for reading/segmenting; word-level JSON for exact cut points. Commands in the reference.

6. Polish. Whoosh SFX at each face→screen transition; a small speed-up with pitch preserved (as a final pass so subtitles stay synced); fade in/out.

Configuration (opinionated defaults, change to taste)

Baked into the recipes as defaults; edit for your style:

Subtitles: color/size/position via force_style (default white, bottom). See the reference.
Speed-up: default atempo=1.1 (pitch preserved). Set to 1.0 to disable.
Clip length: ~30–45s target.
Hook font: default Impact; change the hardcoded fontfile= (macOS path by default — swap for your OS).
Whoosh SFX: set the path in the phase-2 command.

Files

references/ffmpeg-recipes.md — every ffmpeg command + the gotchas. Read before building.
scripts/gen_subs.py — word-timings → per-Short SRT (edit the CLIPS dict; always hand-review output).

View source ↗

File metadata

name: longform-to-shorts
description: >
  Cut standalone vertical Shorts/Reels (1080x1920) out of ONE finished long-form talking-head + screen-share
  video (a "N tools" walkthrough, tutorial, review, demo), one Short per topic/tool, then write per-Short
  YouTube + Instagram metadata. Use this whenever the user hands over a finished long-form video and wants
  short-form clips from it, even if they don't say the word "shorts": "cut shorts/reels from this video",
  "trim the short-form videos out of my long-form", "make reels from my youtube video", "turn each tool in my
  video into its own clip", "clip this up for tiktok/reels". Uses ffmpeg + a transcription skill for word
  timings. Do NOT use for raw multi-take footage (use shorts-from-takes) or a single freeform edit; this is
  specifically finished-long-form -> many standalone verticals.
license: MIT
compatibility: Requires ffmpeg, ffprobe, and Python 3, plus a transcription skill that returns word-level timings (e.g. Whisper).
allowed-tools: Bash, Read, Write, Edit

View original text

# longform-to-shorts

## The pipeline

**1. Transcribe twice.** Full clean transcript for reading/segmenting; word-level JSON for exact cut points. Commands in the reference.

**6. Polish.** Whoosh SFX at each face→screen transition; a small **speed-up with pitch preserved** (as a final pass so subtitles stay synced); fade in/out.

Review the source

Price & running costs

Get the skill: Price unconfirmed
Run it: Requirements have not been confirmed. Check the source for agent, API and service charges.
License: MIT
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

IndexedAI Reviewed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: nidhi-singh02/skills
License: MIT
Version: Unknown
Last GitHub push: Sep 30, 2026
Registry updated: Sep 30, 2026
Instruction path: skills/longform-to-shorts/SKILL.md @ c49f40308620

Version reported in registry metadata; check source releases before relying on it.

Quality

60/100

Promising

Trust

61/100

Sandbox only

Audit

75/100

Risky

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

Agent access

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More details

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      "successfulOutcomes": 0,
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      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "Low GitHub adoption signal",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 60,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "3d since push",
    "risk": "Risky"
  },
  "alternative_skills": [
    {
      "slug": "latent-spaces-brag-slim",
      "name": "brag-slim",
      "url": "https://www.openagentskill.com/skills/latent-spaces-brag-slim",
      "stars": 13021,
      "install_command": "npx skills add latent-spaces/brag --skill brag-slim",
      "trust_score": 81,
      "audit_score": 84
    },
    {
      "slug": "greensock-gsap-frameworks",
      "name": "gsap-frameworks",
      "url": "https://www.openagentskill.com/skills/greensock-gsap-frameworks",
      "stars": 15899,
      "install_command": "npx skills add greensock/gsap-skills --skill gsap-frameworks",
      "trust_score": 83,
      "audit_score": 83
    },
    {
      "slug": "greensock-gsap-react",
      "name": "gsap-react",
      "url": "https://www.openagentskill.com/skills/greensock-gsap-react",
      "stars": 15899,
      "install_command": "npx skills add greensock/gsap-skills --skill gsap-react",
      "trust_score": 79,
      "audit_score": 81
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision"
  ],
  "agent_contract": {
    "task_input": "Use longform-to-shorts in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 69/100 Manual review",
      "Audit: 75/100 Risky",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "nidhi-singh02-longform-to-shorts (longform-to-shorts)",
      "install_command": "npx skills add nidhi-singh02/skills --skill longform-to-shorts",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "nidhi-singh02-longform-to-shorts",
      "task": "Use longform-to-shorts in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/nidhi-singh02-longform-to-shorts",
    "api": "https://www.openagentskill.com/api/agent/skills/nidhi-singh02-longform-to-shorts",
    "audit": "https://www.openagentskill.com/skills/nidhi-singh02-longform-to-shorts/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=nidhi-singh02-longform-to-shorts&task=Use%20longform-to-shorts%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20longform-to-shorts%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20longform-to-shorts%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/nidhi-singh02-longform-to-shorts/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/nidhi-singh02-longform-to-shorts"
  }
}

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Creator: nidhi-singh02
Source: nidhi-singh02/skills
Indexed by: OpenAgentSkill community index

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Overview

longform-to-shorts

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Overview

longform-to-shorts

Setup

The pipeline

Configuration (opinionated defaults, change to taste)

Files

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

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