Youtube Claims

youtube-claims — claim inventory tool for YouTube videos

You are the host agent running this skill. The user has asked for the claims a video makes — concrete or vague — and the evidence the creator shows. Your job is to produce a chronological inventory at ~/youtube-reports/{date}-{slug}-{video_id}-claims.md.

This skill is inventory only, not verification. It captures what was said with timestamps and verbatim quotes. It does not check whether claims are true. The report header states this explicitly so readers don't mistake a high concrete-claim count for "the video is accurate".

You make all LLM calls yourself using your own model and your existing auth — there is no Python orchestrator, no vendor SDK in this repo, and no API key required from the user. The only system requirement is Python 3.11+ with yt-dlp and youtube-transcript-api installed (Step 1.5 verifies this).

Workflow — follow these steps in order

Step 0 — Resolve skill paths

All scripts/… and prompts/… references in this document are relative to the directory containing this SKILL.md file — not the user's working directory. Before your first subprocess call, capture the absolute path to that directory (you already know it: it's the path you loaded this SKILL.md from). Use it as <SKILL_DIR> for every script and prompt path below.

In every shell call, pass quoted absolute paths:

python3 "<SKILL_DIR>/scripts/fetch.py" "<url>" --cache

Do not assume the user's working directory is the repo root. Do not rely on a .venv being activated.

Step 1 — Extract the video URL or 11-char ID from the user's input

Same as the other skills. Plain ID, youtube.com/watch?v=…, youtu.be/…, /shorts/…, /embed/…, /live/… are all accepted. Reject playlist URLs. If no URL is found, ask the user and stop.

Always pass the URL inside double quotes when shelling out — zsh and other shells will treat the ? in ?v=… as a glob otherwise.

Step 1.5 — Pre-flight dependency check

Run once, before the first fetch:

python3 "<SKILL_DIR>/scripts/doctor.py"

If it exits non-zero, surface the printed pip3 install command verbatim to the user (doctor.py tailors it to the user's Python — adding --break-system-packages for PEP 668 environments like Homebrew Python on macOS), ask them to run it, and stop.

Step 2 — Fetch transcript and metadata

python3 "<SKILL_DIR>/scripts/fetch.py" "<url-or-id>" --cache

Standard exit-code interpretation: 0 success, 2 documented rejection (INVALID_URL, PLAYLIST, LIVE_STREAM, TOO_SHORT, NO_TRANSCRIPT, NON_ENGLISH), 1 unexpected error. Surface rejections verbatim and stop.

Step 3 — Pass 1: Structure extraction (shared with verdict, summary, extract)

Cache file: ~/youtube-reports/.cache/{video_id}-pass1.json. Pass 1 is shared infrastructure — same prompt, same input, same output across all four skills. Likely a free cache hit if any sister skill ran first.

1. Try a cache read:

   echo '{"transcript": <full Step 2 fetch JSON>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" read 1 <video_id> "<SKILL_DIR>/prompts/extract_structure.md"
   

   Exit 0 = HIT (parse stdout JSON). Exit 1 = MISS.

2. On MISS, apply prompts/extract_structure.md as a single LLM pass and write the wrapper:

   echo '{"inputs": {"transcript": <full fetch JSON>}, "output": <Pass 1 JSON>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" write 1 <video_id> "<SKILL_DIR>/prompts/extract_structure.md"
   

Tell the user: Pass 1: cache hit or Pass 1: ran (N sections extracted).

Step 4 — Pass 2: Claim & evidence inventory (shared with youtube-verdict)

Cache file: ~/youtube-reports/.cache/{video_id}-pass2.json. Shared with youtube-verdict — same prompt (prompts/inventory_claims.md), same canonical inputs, same cache file. Running verdict first then claims (or vice versa) yields a free Pass 2 cache hit.

The Pass 1 timestamps (section.start, section.end) are authoritative — pass them straight to segments.py. Do not search the transcript to "verify" or "snap" boundaries.

1. Try a cache read:

   echo '{"pass1": <Pass 1>, "transcript": <fetch.py output>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" read 2 <video_id> "<SKILL_DIR>/prompts/inventory_claims.md"
   

   Exit 0 = HIT (parse stdout JSON, skip to "Tell the user"). Exit 1 = MISS, run per-section processing.

On a cache miss — per-section execution

For each section in Pass 1's sections[], in order:

1. python3 "<SKILL_DIR>/scripts/segments.py" <video_id> <section.start> <section.end> — capture stdout JSON.
2. Apply prompts/inventory_claims.md as a single LLM pass with input {"pass1": {"video_id":"<id>","sections":[<just this one section>]}, "transcript": <stdout>}. The model returns {"video_id":"<id>","by_section":{"<this section's id>":{...}}}.
3. Merge the one by_section entry into a running merged dict. Drop the section's transcript slice from your context.

After all sections processed, write the wrapper:

echo '{"inputs": {"pass1": <Pass 1>, "transcript": <fetch.py output>}, "output": <merged Pass 2>}' | \
  python3 "<SKILL_DIR>/scripts/cache.py" write 2 <video_id> "<SKILL_DIR>/prompts/inventory_claims.md"

Optionally verify every quote substring-matches the transcript:

echo '<merged Pass 2 output>' | \
  python3 "<SKILL_DIR>/scripts/cache.py" verify-quotes <video_id>

Exit 0 = clean. Exit 1 = at least one quote isn't verbatim; stderr lists each mismatch.

Tell the user: Pass 2: cache hit or Pass 2: ran (N items inventoried) (where N is the total of concrete_claims/vague_claims/evidence_shown/pitches across sections).

Step 5 — Pass 3: Synthesis into the claim inventory report

Cache file: ~/youtube-reports/.cache/{video_id}-claims-pass3.json.

• Prompt: prompts/generate_claims.md.
• Canonical inputs: {"metadata": {title,channel,duration_seconds,view_count,upload_date}, "pass1": <Pass 1>, "pass2": <Pass 2>}.
• Pass 3 does not need the transcript at all — every quote in the report comes from Pass 2 (which already substring-matches the transcript).

1. Try a cache read:

   echo '{"metadata": {...}, "pass1": <Pass 1>, "pass2": <Pass 2>}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" read 3 <video_id> "<SKILL_DIR>/prompts/generate_claims.md"
   

   Exit 0 = HIT (stdout is the report markdown). Exit 1 = MISS.

2. On MISS, apply prompts/generate_claims.md as a single LLM pass. Strip the outer ``` fence; the inner text is the report. Then write the cache wrapper:

   echo '{"inputs": <same canonical inputs>, "output": "<stripped report markdown>"}' | \
     python3 "<SKILL_DIR>/scripts/cache.py" write 3 <video_id> "<SKILL_DIR>/prompts/generate_claims.md"
   

Tell the user: Pass 3: cache hit or Pass 3: ran.

Step 6 — Write the final report

Filename: {date}-{slug}-{video_id}-claims.md (date = first 10 chars of fetched_at, slug = slug field, video_id = 11-char ID).

~/youtube-reports/{date}-{slug}-{video_id}-claims.md

Always overwrite. Don't print the full report inline — the dashboard in Step 7 is the terminal output.

Step 7 — Show the inventory dashboard inline

Print this dashboard. Borders are exactly 54 box-drawing characters ━. Two-space indent on content lines.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  🔬 Claims  ·  {title_truncated}  ·  {duration_human}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  Inventory only — no external verification performed.

  📊 Substance density
     Concrete claims  {N_concrete}
     Vague claims     {N_vague}
     Evidence shown   {N_evidence}
     Pitches/CTAs     {N_pitches}

  🔍 Top concrete claims
     [{ts1}] {paraphrase 1, ≤ 70 chars}
     [{ts2}] {paraphrase 2, ≤ 70 chars}
     [{ts3}] {paraphrase 3, ≤ 70 chars}

  📄 ~/youtube-reports/{date}-{slug}-{video_id}-claims.md

Field extraction

• title_truncated: first 50 chars of title, suffixed with … if longer.
• duration_human: M:SS if duration_seconds < 3600, else H:MM:SS.
• Counts: parse from the Pass 3 report's OVERVIEW block (one line per category).
• Top concrete claims: the first three [ts] paraphrase entries from the Pass 3 report's CONCRETE CLAIMS section. Truncate each paraphrase at 70 chars + … if longer. If fewer than 3 concrete claims exist, show what's there. If N_concrete == 0, replace the entire 🔍 Top concrete claims block with No concrete claims inventoried.
• File path footer: literal 📄 + the path written in Step 6.

The user gets a one-glance picture of substance density and the top concrete claims, then opens the file for the full chronological inventory across all four categories.

Cache protocol

Identical to youtube-verdict's. See skills/youtube-verdict/SKILL.md → "Cache protocol — exact contract" for the full spec.

Always use cache.py read and cache.py write (Steps 3–5) — they handle wrapper construction, hashing, hit detection, and atomic writes. Inline shell or python3 -c snippets drift across host agents and produce spurious cache misses.

Skill-specific cache files:

Per-pass canonical inputs:

Cross-platform notes

• All LLM calls use the host's own model and auth. No ANTHROPIC_API_KEY / OPENAI_API_KEY / vendor key required.
• Subprocess calls: doctor.py (Step 1.5), fetch.py (Step 2), cache.py read / cache.py write / cache.py verify-quotes (Steps 3–5), segments.py (Step 4 inside per-section loop). Importing each module from <SKILL_DIR>/scripts/ is equivalent if shelling out is not available.
• All cache wrapper construction goes through cache.py write so per-host JSON quirks cannot produce a spurious miss.
• If python3 "<SKILL_DIR>/scripts/fetch.py" ever fails with ModuleNotFoundError, run python3 "<SKILL_DIR>/scripts/doctor.py" for the exact pip3 install command for the user's Python.

Output format reminder

• Pass 1 output: shared with verdict/summary/extract, see prompts/extract_structure.md.
• Pass 2 output: shared with verdict, see prompts/inventory_claims.md (4 arrays per section: concrete_claims, vague_claims, evidence_shown, pitches, every entry timestamp + verbatim quote).
• Pass 3 output: a single fenced markdown block following the chronological-inventory layout in prompts/generate_claims.md. Inventory only — no truth verdicts, no fact-checks, no recommendations.

Scope reminder

V1 lists what was claimed, not whether claims are true. Web verification, citation lookup, and fact-checking against external sources are V2 work — not in this skill's scope. Readers evaluating accuracy should treat this report as a starting point: each entry is the verbatim quote and timestamp where they can verify their own conclusion.