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/create-issuegit 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 | $0.01770 |
| Opus 5 | $0.00000 | $0.00885 |
| Sonnet 5 | $0.00000 | $0.00354 |
| Haiku 4.5 | $0.00000 | $0.00177 |
Grade A, and why
create-issue 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Issue from Working Plan
Generate comprehensive issue documentation from working plan insights and session analysis following SoftMachine issue methodology.
Directory Structure
docs/issues/ # Issue files location (write to here)
├── ISSUE-XXX-title.md # Individual issue files to create
└── README.md # Issue tracking reference
.claude/flashback/memory/ # Source files (read from here)
├── WORKING_PLAN.md # Current development priorities and context
├── REMEMBER.md # Project knowledge and patterns
└── CURRENT_SESSION.md # Session summaries (if exists)
Usage
/fb:create-issue <title> [priority] [effort]
Examples:
/fb:create-issue auto-context-management- Generate issue for current working plan focus/fb:create-issue security-hardening high 4-weeks- Create high priority security issue/fb:create-issue performance-optimization critical 2-weeks- Critical performance issue
Command Implementation
Parse the command arguments:
- Argument 1 (required): Issue title (kebab-case, becomes filename)
- Argument 2 (optional): Priority level (critical, high, medium, low)
- Argument 3 (optional): Estimated effort (1-week, 2-weeks, 1-month, etc.)
Argument Parsing Logic
- Extract Title: First argument becomes issue filename and header title
- Parse Priority: Second argument sets priority level (default: high)
- Parse Effort: Third argument sets estimated effort (default: based on complexity)
- Generate Issue Number: Auto-increment from existing issues in docs/issues/
How It Works
Step 1: Context Analysis
- Read Working Plan: Analyze current priorities and session context
- Read Project Memory: Extract relevant background from REMEMBER.md
- Parse Session Insights: Include recent session accomplishments if available
- Identify Scope: Determine if issue is feature, bug fix, architecture, or strategic
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 · 204 lines · 0 tokens per session scan A bb565524e7a7
create-issue 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 1,770 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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.