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/recommend-assigneenpx skills add pilotspace/pilot-space --skill recommend-assigneegit clone --depth 1 https://github.com/pilotspace/pilot-spaceWrote 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/skills/pilotspace/pilot-space/recommend-assignee)<a href="https://agentmods.dev/skills/pilotspace/pilot-space/recommend-assignee"><img src="https://agentmods.dev/badge/skills/pilotspace/pilot-space/recommend-assignee.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.01454 |
| Opus 5 | $0.00007 | $0.00727 |
| Sonnet 5 | $0.00003 | $0.00291 |
| Haiku 4.5 | $0.00001 | $0.00145 |
Grade A, and why
recommend-assignee 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 4d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recommend Assignee Skill
Suggest optimal assignee for issue using expertise matching, past work analysis, and workload balancing.
Quick Start
Use this skill when:
- New issue created without assignee
- User requests assignee recommendation (
/recommend-assignee) - Issue reassignment needed due to skill mismatch
Example:
Issue: "Implement GraphQL API for user queries"
AI recommends:
- [email protected] (RECOMMENDED)
- Expertise: GraphQL, API design (15 prior issues)
- Workload: 3 open issues (below team average of 5)
- Recent: Completed "GraphQL schema design" 2 weeks ago
Workflow
-
Analyze Issue Requirements
- Extract technical keywords from title/description
- Identify domain (backend, frontend, infra, design)
- Classify complexity (low, medium, high)
-
Build Team Expertise Profile
- For each workspace member:
- Past issues worked on (completed, in-progress)
- Labels/technologies from those issues
- Success rate (completion time, quality)
- Load expertise data from
user_expertisetable
- For each workspace member:
-
Score Candidates
- Expertise Match (0-100): Keyword overlap with past work
- Workload (0-100): Inverse of current open issues
- Recency (0-100): Recently worked on similar issues
- Domain Familiarity (0-100): Experience in issue domain
- Weighted score: Expertise (40%) + Workload (30%) + Recency (20%) + Domain (10%)
-
Apply Business Rules
- Exclude members on PTO or unavailable
- Consider role permissions (e.g., only admins for security issues)
- Respect team preferences (preferred domains)
-
Tag Confidence
- RECOMMENDED: High score (>80), clear expertise match
- DEFAULT: Medium score (50-80), general fit
- ALTERNATIVE: Multiple candidates with similar scores
Output Format
{
"recommendations": [
{
"user_id": "user-abc123",
"email": "[email protected]",
"name": "Alice Johnson",
"confidence": "RECOMMENDED",
"score": 92,
"rationale": "Strong GraphQL expertise (15 prior issues), below workload average",
"expertise_match": {
"graphql": 95,
"api_design": 88,
"python": 82
},
"workload": {
"open_issues": 3,
"team_average": 5
},
"recent_work": [
{"title": "GraphQL schema design", "completed": "2024-01-10"}
]
},
{
"user_id": "user-def456",
"email": "[email protected]",
"name": "Bob Smith",
"confidence": "DEFAULT",
"score": 68,
"rationale": "API experience but no GraphQL history, workload is high",
"expertise_match": {
"api_design": 75,
"python": 88
},
"workload": {
"open_issues": 7,
"team_average": 5
}
}
],
"summary": "2 candidates found, 1 RECOMMENDED"
}
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.
- 4d ago First seen · 211 lines · 14 tokens per session scan A 7b7257103d21
recommend-assignee 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 1,454 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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