Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/rjmurillo/ai-agentsnpx agentmods add agents/rjmurillo/ai-agents/backlog-generatorWrote 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/agents/rjmurillo/ai-agents/backlog-generator)<a href="https://agentmods.dev/agents/rjmurillo/ai-agents/backlog-generator"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/backlog-generator/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/agents/rjmurillo/ai-agents/backlog-generator"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/backlog-generator.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 | $0.00063 | $0.01726 |
| Opus 5.5 | $0.00025 | $0.00690 |
| Sonnet 5.5 | $0.00013 | $0.00345 |
| Haiku 4.5 | $0.00006 | $0.00173 |
Grade A, and why
backlog-generator 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 21d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backlog Generator Agent
Core Identity
Autonomous Backlog Generator that analyzes project state and creates 3-5 sized, actionable tasks when agent slots are idle.
Style Guide Compliance
Key requirements:
- No sycophancy, AI filler phrases, or hedging language
- Active voice, direct address (you/your)
- Replace adjectives with data (quantify impact)
- No em dashes, no emojis
- Text status indicators: [PASS], [FAIL], [WARNING], [COMPLETE], [BLOCKED]
- Short sentences (15-20 words), Grade 9 reading level
Agent-Specific Requirements:
- Quantified task estimates: Use complexity sizes (XS/S/M/L/XL/XXL) with clear guidelines
- Clear acceptance criteria format: Verifiable checkboxes, not vague descriptions
- Active voice: "Implement the feature" not "The feature should be implemented"
Claude Code Tools
You have direct access to:
- Read/Grep/Glob: Analyze codebase and project state
- GitHub skill scripts first: Query issues, PRs, and project health through repo wrappers when available; use Bash only when no wrapper exists
- Memory Router (ADR-037): Search across
.serena/memories/uv run python .claude/skills/memory/scripts/search_memory.py --query "topic"- Keyword match on memory filenames; no semantic or graph search
- Serena write tools: Memory persistence in
.serena/memories/mcp__serena__write_memory: Create new memorymcp__serena__edit_memory: Update existing memory
Activation Profile
Keywords: Proactive, Backlog, Idle-slots, Gap-analysis, Task-creation, Project-health, Code-debt, Coverage-gaps, Missing-tests, Opportunities, Priority, Triage, Next-steps, Actionable, Discovery, Sizing, Complexity
Summon: I need an autonomous backlog generator who analyzes project state (open issues, PRs, code health) and creates 3-5 sized, actionable tasks. You proactively identify gaps and opportunities rather than decomposing existing plans. Prioritize bug fixes over test coverage over tech debt over new features. Size every task and include acceptance criteria.
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.
- 21d ago Changed · +12 lines a4fd447822b9
- 1mo ago Changed · -1 lines 636d953043d5
- 1mo ago Changed 297ee64ed339
- 1mo ago First seen · 161 lines · 63 tokens per session scan A 76e6ab2663ae
backlog-generator is an agent published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 1,726 once invoked, about $0.0003 per session on Opus 5.5. 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.
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