A prompt-flow skill designed to transform raw, pasted text transcripts and chat logs into clean, structured, and auditable action registers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-transcript-action-extractor
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-transcript-action-extractor using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AI Transcript Action Extractor?
The AI Transcript Action Extractor is a specialized prompt-flow skill tailored for developers, project managers, and teams who need to extract clear, actionable data from dense conversational text. Operating strictly within a local and document-only context, this skill ensures zero reliance on external APIs, meeting bots, or invasive system integrations. Instead, it works purely with user-provided text to separate commitments, tasks, and deadlines from casual discussion.
By leveraging this component of Openclaw Skills, users can reliably parse meeting notes, support threads, or project standup logs. The skill enforces rigid boundaries around privacy and accuracy, making it an indispensable asset for turning unstructured conversational chaos into high-fidelity, reviewable tables without sacrificing sensitive data.
AI Transcript Action Extractor Use Cases
- Extracting actionable tasks, assigned owners, and explicit deadlines from long chat logs or interview transcripts.
- Converting raw, automated AI meeting transcriptions into structured registers and clean tables.
- Isolating finalized decisions, unresolved questions, and dependencies from general discussion threads.
- Preparing safe, redacted text variants for external AI analysis while preserving context placeholders.
- Building verification checklists for standup reviews before sharing summaries with stakeholders.
How AI Transcript Action Extractor Works
- Confirm Source and Scope: Assess the provided input (transcript, chat log, or note dump) to verify completeness and flag missing context.
- Redact or Minimize Sensitive Text: Strip out credentials, personal identifiers, financial data, or proprietary information, replacing them with structured tokens.
- Extract Candidate Actions: Scan the text for verb-led statements that imply work, commitments, handoffs, or delivery steps.
- Assign Evidence and Confidence: Match every identified task with a transcript cue or paraphrase, assigning a confidence score (confirmed, inferred, or needs verification).
- Capture Owners and Dates: Extract timelines and owners strictly from explicit textual evidence to prevent arbitrary assumptions.
- Separate Decisions From Tasks: Document confirmed consensus independently from action items, isolating active suggestions from concrete choices.
- Identify Questions and Risks: Group unresolved blockers, conflicting statements, and missing details into a distinct risk log.
- Build the Action Register: Compile a structured, reviewable table mapping out the action, owner, due date, source cue, dependency, status, and validation needs.
- Generate Manual Verification Checklist: Provide a concluding checklist to enforce human review of sensitive metadata, dates, and ownership records.
AI Transcript Action Extractor Setup
To integrate this configuration into your local environment, save the prompt-flow skill definition to your project's skills directory.
# Create the skills directory if it does not exist
mkdir -p ~/.config/openclaw/skills
# Save the skill definition to your local configuration
cat << \'EOF\' > ~/.config/openclaw/skills/transcript-action-extractor.md
# AI Transcript Action Extractor configuration details go here
EOF
No external API keys, OAuth tokens, or background daemons are required to execute this prompt-flow skill within Openclaw Skills.
AI Transcript Action Extractor Data Schema & Taxonomy
The skill organizes its extracted findings into a well-defined markdown schema optimized for cross-platform rendering and clear scannability:
Output Architecture
| Section | Description | Data Type |
|---|---|---|
| Transcript Source Snapshot | High-level metadata regarding the text origin and parsed state. | String |
| Privacy and Redaction Notes | Log of removed sensitive fields or placeholder mapping tokens used. | String / List |
| Action Register | The main structured table detailing work items and accountability matrices. | Markdown Table |
| Confirmed Decisions | List of finalized agreements distinct from standalone tasks. | Unordered List |
| Open Questions | Tabulation of unresolved blockers and missing text data points. | Unordered List |
| Risks and Dependencies | Log of structural delivery risks and inter-task dependencies. | Unordered List |
| Items Needing Manual Verification | Highlights where metadata was inferred rather than explicitly stated. | Unordered List |
| Verification Checklist | Pre-flight manual checklist for human operators before sharing. | Checklist (- [ ]) |
Action Register Column Layout
- Action: Verb-led, discrete task description.
- Owner: Specifically assigned individual or role (marked as "needs verification" if ambiguous).
- Due Date: Explicit target deadline or timeframe derived from text.
- Source Cue: Verbatim text snippet or short paraphrase serving as evidence.
- Dependency: Blockers or prerequisite tasks.
- Confidence: Explicit certainty rating (
confirmed|inferred|needs verification). - Verification Needed: Precise human review task required to validate the entry.
AI Transcript Action Extractor Advanced Features
- Context-Preserving Anonymization: Automatically maps sensitive entities into clear structural placeholders like
[CLIENT_A]or[PERSON_1]to retain task logic while guaranteeing data privacy. - Evidence-Based Auditing: Enforces direct trace-back mechanics by linking every generated task directly to a recorded source cue or textual statement.
- Reusable External AI Templates: Generates highly rigid, payload-ready system prompts engineered to keep third-party LLMs bounded strictly to redacted inputs.
- Confidence Grading Engine: Categorizes assignments as confirmed or inferred based on sentence structure, mitigating false consensus hallucinations.
SKILL.md
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