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/pilotspace/pilot-space/extract-issuesnpx skills add pilotspace/pilot-space --skill extract-issuesgit clone --depth 1 https://github.com/pilotspace/pilot-spaceWhat 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 | $0.00014 | $0.01335 |
| Opus 5 | $0.00007 | $0.00668 |
| Sonnet 5 | $0.00003 | $0.00267 |
| Haiku 4.5 | $0.00001 | $0.00134 |
Grade A, and why
extract-issues 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Issues Skill
Extract actionable issues from note canvas content using semantic analysis and confidence scoring.
Quick Start
Use this skill when:
- User finishes brainstorming in note canvas
- AI detects potential work items in note content
- User explicitly requests issue extraction (
/extract-issues)
Example:
User writes in note:
"We need to implement JWT authentication and add rate limiting
to the API. Also fix the login bug where users can't log in with email."
AI extracts:
- Issue 1: "Implement JWT authentication" (RECOMMENDED)
- Issue 2: "Add API rate limiting" (RECOMMENDED)
- Issue 3: "Fix email login bug" (RECOMMENDED - explicit bug mention)
Workflow
-
Analyze Note Content
- Parse note blocks (paragraphs, lists, headings)
- Identify action items using verb patterns (implement, fix, add, create)
- Detect explicit issue references ("bug", "feature", "task")
-
Extract Candidate Issues
- For each potential issue:
- Extract name (concise title, ≤100 chars)
- Extract description (context from surrounding text)
- Identify block_id for source linking
- Detect labels from keywords (backend, frontend, security, etc.)
- Infer priority from urgency markers (critical, urgent, nice-to-have)
- For each potential issue:
-
Score Confidence
- RECOMMENDED: Clear action item with explicit verb and context
- Example: "Implement user authentication with JWT tokens"
- DEFAULT: Implied action from discussion context
- Example: "Authentication should use JWT" (implies implementation)
- ALTERNATIVE: Possible interpretation, needs clarification
- Example: "Maybe we should consider caching?" (tentative)
- RECOMMENDED: Clear action item with explicit verb and context
-
Return Structured Output
- JSON array of issues with confidence tags
- Preserve source block links
- Include rationale for each confidence score
Output Format
{
"issues": [
{
"name": "Implement JWT authentication",
"description": "Add JWT-based authentication with refresh tokens to replace session cookies",
"confidence": "RECOMMENDED",
"source_block_id": "block-abc123",
"labels": ["backend", "security"],
"priority": "high",
"rationale": "Clear implementation task with specific requirements (JWT, refresh tokens)"
},
{
"name": "Add API rate limiting",
"description": "Implement rate limiting middleware to prevent API abuse",
"confidence": "RECOMMENDED",
"source_block_id": "block-def456",
"labels": ["backend", "infrastructure"],
"priority": "medium",
"rationale": "Explicit action item, standard security practice"
},
{
"name": "Fix email login bug",
"description": "Users cannot log in using email addresses, investigate validation logic",
"confidence": "RECOMMENDED",
"source_block_id": "block-ghi789",
"labels": ["backend", "bug"],
"priority": "critical",
"rationale": "Explicit bug mention, blocks user access"
}
],
"summary": "Extracted 3 issues from note content",
"total_blocks_analyzed": 8
}
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 · 194 lines · 14 tokens per session scan A 3baa85ed4fd4
extract-issues is a skill published in the GitHub repository pilotspace/pilot-space (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 1,335 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.
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