Borrowing it
Nothing to install: this file belongs to tomdwipo/claude-soul. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tomdwipo/claude-soul/main/.claude/commands/feature-report.mdgit clone --depth 1 https://github.com/tomdwipo/claude-soulWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/tomdwipo/claude-soul/feature-report)<a href="https://agentmods.dev/commands/tomdwipo/claude-soul/feature-report"><img src="https://agentmods.dev/badge/commands/tomdwipo/claude-soul/feature-report/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/tomdwipo/claude-soul/feature-report"><img src="https://agentmods.dev/badge/commands/tomdwipo/claude-soul/feature-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00000 | $0.02019 |
| Opus 5 | $0.00000 | $0.01009 |
| Sonnet 5 | $0.00000 | $0.00404 |
| Haiku 4.5 | $0.00000 | $0.00202 |
Grade A, and why
feature-report 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 9d ago.
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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Report Command (Phase 2: Post-Implementation)
Generate a compliance feature report for: $ARGUMENTS
Instructions
Step 1: Gather PRD & Design Inputs
Primary inputs for feature reports are PRD (Confluence), Figma design, and actual implemented codebase — NOT Jira tickets.
1a. PRD from Confluence (Primary Source of Truth)
If $ARGUMENTS contains a Confluence URL or page ID:
- Use
mcp__atlassian__getConfluencePageto fetch PRD content - Extract: requirements, acceptance criteria, business rules — this is what implementation is compared against
If $ARGUMENTS describes a feature name (no URL):
- Use
mcp__atlassian__searchConfluenceUsingCqlto search for the PRD in Confluence - Fetch the matching page with
mcp__atlassian__getConfluencePage
1b. Figma Design
If $ARGUMENTS contains a Figma URL:
- Use
mcp__figma__get_file_nodesto fetch design details
If user provides screenshot images directly:
- Analyze the screenshots for UI compliance verification
1c. QA TRD
If $ARGUMENTS contains --qa-trd or a QA TRD Confluence URL:
- Fetch QA TRD from Confluence to enrich QA_ALIGNMENT.md
- Map QA test scenarios against implementation
1d. Jira (Optional — for ticketing/tracking only)
If $ARGUMENTS contains a Jira ticket ID (e.g., PROJ-1234):
- Use
mcp__atlassian__getJiraIssueto fetch ticket summary, epic, linked issues - Use
mcp__jira-attachment__list_attachments+download_all_images+download_all_videosfor attachments - If videos found, use
mcp__video-to-image__extract_frames_by_count(count: 5) to extract key frames - Note: Jira is used for tracking context and ticket references only, not as primary requirement source
Step 2: Analyze Implemented Code
- Read the existing TRD at
.docs/trd/TRD-{Feature-Name}.mdif it exists - Search codebase for all files mentioned in the TRD or related to the feature
- Read actual implemented files to verify what was built
- Compare implementation against PRD requirements from Confluence
- Identify any gaps or deviations from the original spec
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.
- 9d ago First seen · 296 lines · 0 tokens per session scan A 3c7ccb76a1a8
feature-report is a command published in the GitHub repository tomdwipo/claude-soul (24 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,019 tokens. 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.