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 commands/agentsea/flashbacker/debt-huntergit clone --depth 1 https://github.com/agentsea/flashbackerWhat 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 | $0.00000 | $0.00693 |
| Opus 5 | $0.00000 | $0.00347 |
| Sonnet 5 | $0.00000 | $0.00139 |
| Haiku 4.5 | $0.00000 | $0.00069 |
Grade A, and why
debt-hunter 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Debt Hunter
Ruthlessly hunt down technical debt, code quality issues, and lazy implementations using the hybrid AI+Computer operations pattern.
Description
Scans the codebase for technical debt patterns using programmatic detection (ripgrep), then provides intelligent analysis and prioritized recommendations for cleanup.
Hybrid Pattern: CLI handles consistent pattern scanning, AI provides expert analysis and cleanup strategies.
Detection Patterns
Basic Technical Debt
- TODO/FIXME comments - Incomplete work markers
- Console debug logs - Debug artifacts left in code
- Not implemented functions - Empty or placeholder implementations
- Commented code blocks - Dead code that should be removed
- Generic variable names - Lazy naming (data, item, thing, stuff)
- Empty functions - Functions with no implementation
- Debugger statements - Breakpoints left in production code
- AI naming patterns - Similar function names (handle*, process*, manage*)
Duplicate Function Detection (Limited Heuristics)
- Exact duplicates - MD5 hash comparison of function signatures
- Similar names - AI-generated patterns like handleData/processData/manageData
- Near-duplicates - 80%+ text similarity in function bodies
- ⚠️ Limitations: Cannot detect semantic similarity or refactored duplicates
Usage
/fb:debt-hunter [scan_focus]
Examples:
/fb:debt-hunter- Full technical debt scan and analysis/fb:debt-hunter high-priority- Focus on high-severity issues only/fb:debt-hunter duplicates- Focus on duplicate function detection/fb:debt-hunter comprehensive- Run both basic patterns and duplicate detection
CLI Commands Available:
flashback debt-hunter --scan- Basic technical debt patternsflashback debt-hunter --duplicates- Duplicate/similar function detection (realistic heuristics only)flashback debt-hunter --context- Output structured context for AI analysis
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 · 64 lines · 0 tokens per session scan A ba0a488c4566
debt-hunter is a command published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 693 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.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.