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 skills/saolalab/clawforce/roadmapnpx skills add saolalab/clawforce --skill roadmapgit clone --depth 1 https://github.com/saolalab/clawforceWhat 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 | $0.00037 | $0.00990 |
| Opus 5 | $0.00018 | $0.00495 |
| Sonnet 5 | $0.00007 | $0.00198 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
roadmap 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 2d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Roadmap Management
Roadmap Template (Now/Next/Later)
# Product Roadmap — Q{N} {YEAR}
## Now (This Quarter)
- **Feature 1**: {Brief description} — {Target launch date}
- **Feature 2**: {Brief description} — {Target launch date}
- **Feature 3**: {Brief description} — {Target launch date}
## Next (Next Quarter)
- **Feature 4**: {Brief description} — {Rationale}
- **Feature 5**: {Brief description} — {Rationale}
## Later (Future Exploration)
- **Feature 6**: {Brief description} — {Research needed}
- **Feature 7**: {Brief description} — {Research needed}
Feature Specification Template (PRD)
# Feature: {Feature Name}
## Problem Statement
- **User Problem**: {What problem are users facing?}
- **Business Problem**: {Why does this matter to the business?}
- **Evidence**: {Data, user quotes, research that supports this}
## Hypothesis
If we {build this feature}, then {expected outcome}, because {reasoning}.
## Solution
- **Overview**: {High-level description of the solution}
- **User Stories**:
- As a {user type}, I want {action}, so that {benefit}
- As a {user type}, I want {action}, so that {benefit}
- **Key Features**: (bulleted list)
- **Design Considerations**: {Notes for design team}
## Success Metrics
- **Primary Metric**: {Metric name} — Target: {target value}
- **Secondary Metrics**:
- {Metric name}: {target}
- {Metric name}: {target}
- **How we'll measure**: {Data sources, analytics events}
## Edge Cases & Considerations
- {Edge case 1}: {How we'll handle it}
- {Edge case 2}: {How we'll handle it}
- **Technical Constraints**: {Any known limitations}
- **Dependencies**: {Other features or systems needed}
## Launch Plan
- **Beta**: {Date and user group}
- **Full Launch**: {Date}
- **Rollout Strategy**: {Gradual rollout? Feature flag?}
- **Marketing**: {Key messages and channels}
- **Support**: {Documentation, training needed}
RICE Scoring Template
## Feature Prioritization — RICE Scoring
| Feature | Reach | Impact | Confidence | Effort | Score | Rank |
|---------|-------|--------|------------|--------|-------|------|
| Feature A | {users/quarter} | {0.25-3} | {50-100%} | {person-months} | {calculated} | 1 |
| Feature B | {users/quarter} | {0.25-3} | {50-100%} | {person-months} | {calculated} | 2 |
**Scoring Guide**:
- **Reach**: Users affected per quarter
- **Impact**: 0.25 = minimal, 0.5 = low, 1 = medium, 2 = high, 3 = massive
- **Confidence**: 50% = low, 80% = medium, 100% = high
- **Effort**: Person-months (minimum 0.5)
- **Score**: (Reach × Impact × Confidence) / Effort
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.
- 2d ago First seen · 110 lines · 37 tokens per session scan A b44b1c389d9a
roadmap is a skill published in the GitHub repository saolalab/clawforce (38 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 990 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
agenticmail
🎀 AgenticMail — Full email, SMS, storage & multi-agent coordination for AI agents. 63 tools.
agentic-paper-digest-skill
Fetches and summarizes recent arXiv and Hugging Face papers with Agentic Paper Digest. Use when the user wants a paper digest, a JSON feed of recent papers, or to run the arXiv/HF pipeline.
ai-meeting-scheduling
Booking links fail for groups. SkipUp schedules meetings with 2-50 participants via email — one API call coordinates across timezones automatically. Also: check status, pause, resume, or cancel requests. Async only — does not instant-book, access calendars, or do free/busy lookups.
arxiv-summarizer-orchestrator
End-to-end orchestration skill for periodic arXiv collection and reporting using three sub-skills: arxiv-search-collector, arxiv-paper-processor, and arxiv-batch-reporter. Supports manual language control across all markdown outputs and Stage-B processing strategy (subagentparallel default max 5, or serial).
eachlabs-voice-audio
Text-to-speech, speech-to-text, voice conversion, and audio processing using EachLabs AI models. Supports ElevenLabs TTS, Whisper transcription with diarization, and RVC voice conversion. Use when the user needs TTS, transcription, or voice conversion.
academic-literature-search
这是一个专注于学术文献检索的专业工具,集成了多个权威学术数据库,提供全面、快速、准确的文献检索服务。支持多数据库并发检索、高级过滤、智能排序和多种输出格式。.