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/synaptic-labs-ai/pact-plugin/plan-modegit clone --depth 1 https://github.com/Synaptic-Labs-AI/PACT-PluginWhat 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 | $0.00008 | $0.06758 |
| Opus 5 | $0.00004 | $0.03379 |
| Sonnet 5 | $0.00002 | $0.01352 |
| Haiku 4.5 | $0.00001 | $0.00676 |
Grade A, and why
plan-mode 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.
How it starts
The opening of the file, as written. The whole thing — 688 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create a comprehensive implementation plan for: $ARGUMENTS
This is PLANNING ONLY — no code changes, no git branches, no implementation.
Task Hierarchy
Create a planning Task hierarchy:
1. `TaskCreate`: Planning task "Plan: {feature}"
2. `TaskUpdate`: Planning task status = "in_progress"
3. Analyze: Which specialists to consult?
4. `TaskCreate`: Consultation task(s) — one per specialist
5. `TaskUpdate`: Consultation tasks status = "in_progress"
6. `TaskUpdate`: Planning task addBlockedBy = [consultation IDs]
7. Dispatch specialists in parallel (planning-only mode)
8. Monitor until consultations complete
9. `TaskUpdate`: Consultation tasks status = "completed" (as each completes)
10. Synthesize → write plan document
11. `TaskUpdate`: Planning task status = "completed", metadata.artifact = plan path
Example structure:
[Planning] "Plan: user authentication" (blockedBy: consult1, consult2, consult3)
├── [Consult] "preparer: research auth patterns"
├── [Consult] "architect: design auth service"
└── [Consult] "test-engineer: testing strategy"
S4 Intelligence Function
This command is the primary S4 (Intelligence) activity in PACT. While /PACT:orchestrate operates mainly in S3 mode (execution), plan-mode operates entirely in S4 mode:
- Outside focus: What does the environment require? What are the constraints?
- Future focus: What approach will lead to long-term success?
- Strategic thinking: What are the risks? What could change?
The output—an approved plan—bridges S4 intelligence to S3 execution. When /PACT:orchestrate runs, it shifts to S3 mode while referencing the S4 plan.
S4 Questions to Hold Throughout:
- Are we solving the right problem?
- What could invalidate this approach?
- What are we assuming that might be wrong?
- How might requirements change?
Variety Context
plan-mode is the recommended entry point for high-variety tasks (variety score 11-14). It builds understanding before committing to execution.
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 · 688 lines · 8 tokens per session scan A 01e7744792ca
plan-mode is a command published in the GitHub repository Synaptic-Labs-AI/PACT-Plugin (71 stars, last pushed 3d ago), licensed MIT. It adds 8 tokens to every session and 6,758 once invoked, about $0.0000 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 2026-08-30.
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.