Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/technicalpickles/pickled-claude-plugins/cleanup-transcriptgit clone --depth 1 https://github.com/technicalpickles/pickled-claude-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00026 | $0.00816 |
| Opus 5 | $0.00013 | $0.00408 |
| Sonnet 5 | $0.00005 | $0.00163 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
cleanup-transcript scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcript Cleanup
Clean up the transcript for readability and accuracy. Supports both SRT subtitle files and plaintext transcripts.
Step 1: Read and Detect Format
Read the transcript file provided as $1.
Detect format:
- SRT format: Has numbered entries with timestamps like
00:01:23,456 --> 00:01:25,789 - Plaintext: Raw text without timestamps
For SRT files, strip the entry numbers, timestamps, and blank lines to extract the raw text. Note approximate time ranges for major topic shifts if helpful for the user.
Step 2: Identify High-Level Structure
Analyze the content and identify:
- Major topics/sections - What distinct subjects are discussed?
- Approximate flow - How does the conversation progress?
- Number of speakers - Is this a monologue or conversation?
Present this structure to the user for confirmation before proceeding.
Step 3: Identify Transcription Errors (Iterative)
Scan for likely transcription errors. Common patterns include:
Proper Nouns
- Tool names (often mangled: "get hub" → GitHub, "chat GBT" → ChatGPT)
- Company/product names
- People's names
- Technical terms and acronyms
Commands and Code
- CLI commands (often split or garbled)
- File paths
- Code snippets mentioned verbally
Domain Terminology
- Industry-specific terms
- Project-specific vocabulary
- Abbreviations
For each category of errors found:
- Present the suspected errors with surrounding context
- Ask the user to confirm corrections
- Note any additional context they provide
- Proceed to the next category
If context files were provided ($2, $3, etc.), read them to help resolve ambiguities. Context files might include:
- Session logs (JSONL files from Claude sessions)
- Related documentation
- Glossaries or term lists
Step 4: Clarify Speaker Attribution
If multiple speakers are detected:
- Ask how many speakers there are
- Ask for their names/identifiers
- Present ambiguous exchanges and ask for attribution
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 121 lines · 26 tokens per session scan A 8ab98de05c3f
cleanup-transcript is a command published in the GitHub repository technicalpickles/pickled-claude-plugins (10 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 816 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
slack-gif-creator
Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users request animated GIFs or emoji animations for Slack from descriptions like "make me a GIF for Slack of X doing Y".
add-animation
Scaffold a new webkit animation in the one correct place — the semantic animation catalog — so components never ship a component-local @keyframes or an off-catalog animate-[…]. Triggered by hand, or pointed at by validate-spec-compliance when a component uses an animate- that is not yet in the catalog.
OPSX: Onboard
Guided onboarding - walk through a complete OpenSpec workflow cycle with narration.
OPSX: Bulk Archive
Archive multiple completed changes at once.
OPSX: Explore
Enter explore mode - think through ideas, investigate problems, clarify requirements.
OPSX: Verify
Verify implementation matches change artifacts before archiving.