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/rshade/finfocus/speckit.plangit clone --depth 1 https://github.com/rshade/finfocusWrote 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/commands/rshade/finfocus/speckit.plan)<a href="https://agentmods.dev/commands/rshade/finfocus/speckit.plan"><img src="https://agentmods.dev/badge/commands/rshade/finfocus/speckit.plan.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 | $0.00736 |
| Opus 5 | $0.00007 | $0.00368 |
| Sonnet 5 | $0.00003 | $0.00147 |
| Haiku 4.5 | $0.00001 | $0.00074 |
Grade A, and why
speckit.plan 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 3d 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.
This is a copy
98% identical to speckit.plan — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
-
Setup: Run
.specify/scripts/bash/setup-plan.sh --jsonfrom repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot"). -
Load context: Read FEATURE_SPEC and
.specify/memory/constitution.md. Load IMPL_PLAN template (already copied). -
Execute plan workflow: Follow the structure in IMPL_PLAN template to:
- Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
- Fill Constitution Check section from constitution
- Evaluate gates (ERROR if violations unjustified)
- Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
- Phase 1: Generate data-model.md, contracts/, quickstart.md
- Phase 1: Update agent context by running the agent script
- Re-evaluate Constitution Check post-design
-
Stop and report: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.
Phases
Phase 0: Outline & Research
-
Extract unknowns from Technical Context above:
- For each NEEDS CLARIFICATION → research task
- For each dependency → best practices task
- For each integration → patterns task
-
Generate and dispatch research agents:
For each unknown in Technical Context: Task: "Research {unknown} for {feature context}" For each technology choice: Task: "Find best practices for {tech} in {domain}" -
Consolidate findings in
research.mdusing format:- Decision: [what was chosen]
- Rationale: [why chosen]
- Alternatives considered: [what else evaluated]
Output: research.md with all NEEDS CLARIFICATION resolved
Phase 1: Design & Contracts
Prerequisites: research.md complete
- Extract entities from feature spec →
data-model.md:- Entity name, fields, relationships
- Validation rules from requirements
- State transitions if applicable
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.
- 3d ago First seen · 90 lines · 14 tokens per session scan A 4c713f8f1704
speckit.plan is a command published in the GitHub repository rshade/finfocus (5 stars, last pushed 17d ago), licensed Apache-2.0. It adds 14 tokens to every session and 736 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to speckit.plan, differing in 2 lines, and is treated as a copy.
Other commands, from other repositories
pr-review
Review a pull request — gather metadata, diff, and existing feedback, then give a verdict.
scan
Scan AWS account for cost optimization.
streaming-api-patterns
Real-time data streaming with SSE, WebSockets, and ReadableStream. Use when implementing token streaming, Server-Sent Events, backpressure handling, or reconnection strategies. Triggers on streaming, SSE, Server-Sent Events, WebSocket, ReadableStream, backpressure, reconnection, token streaming.
langgraph-checkpoints
LangGraph checkpointing and persistence. Use when implementing fault-tolerant workflows, resuming interrupted executions, or debugging with state history. Triggers on LangGraph checkpoint, persistence, fault tolerance, resume workflow, state history, checkpointer, thread state.
ollama-local
Local LLM inference with Ollama. Use when setting up local models for development, running models in CI pipelines, or reducing inference cost. Triggers on Ollama, local LLM, local inference, offline model, self-hosted model, LangChain Ollama, model quantization.
archive-ledger
../../../shared/commands/archive-ledger.md.