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/x22x22/code-cli-any-llm/plangit clone --depth 1 https://github.com/x22x22/code-cli-any-llmWhat 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 | $0.00358 |
| Opus 5 | $0.00007 | $0.00179 |
| Sonnet 5 | $0.00003 | $0.00072 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
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 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.
What it actually says
Given the implementation details provided as an argument, do this:
-
Run
.specify/scripts/bash/setup-plan.sh --jsonfrom the repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. All future file paths must be absolute. -
Read and analyze the feature specification to understand:
- The feature requirements and user stories
- Functional and non-functional requirements
- Success criteria and acceptance criteria
- Any technical constraints or dependencies mentioned
-
Read the constitution at
.specify/memory/constitution.mdto understand constitutional requirements. -
Execute the implementation plan template:
- Load
.specify/templates/plan-template.md(already copied to IMPL_PLAN path) - Set Input path to FEATURE_SPEC
- Run the Execution Flow (main) function steps 1-10
- The template is self-contained and executable
- Follow error handling and gate checks as specified
- Let the template guide artifact generation in $SPECS_DIR:
- Phase 0 generates research.md
- Phase 1 generates data-model.md, contracts/, quickstart.md
- Phase 2 generates tasks.md
- Incorporate user-provided details from arguments into Technical Context: $ARGUMENTS
- Update Progress Tracking as you complete each phase
- Load
-
Verify execution completed:
- Check Progress Tracking shows all phases complete
- Ensure all required artifacts were generated
- Confirm no ERROR states in execution
-
Report results with branch name, file paths, and generated artifacts.
Use absolute paths with the repository root for all file operations to avoid path issues.
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 · 37 lines · 14 tokens per session scan A bedb8c4dccc1
plan is a command published in the GitHub repository x22x22/code-cli-any-llm (5 stars, last pushed 10mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 358 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.