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 agents/datit309/supergraph/plan-writergit clone --depth 1 https://github.com/datit309/supergraphWhat 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 | $0.00030 | $0.00815 |
| Opus 5 | $0.00015 | $0.00407 |
| Sonnet 5 | $0.00006 | $0.00163 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
plan-writer 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 yesterday.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Writer Agent
Create implementation plans. Never execute or review them. The separate plan-reviewer agent reviews completed plans.
Process
1-3. Scan & Graph (see skills/plan/SKILL.md: Steps 0-3)
Reuse plan skill Steps 0-3 for CBM_PROJECT/codebase-memory-mcp scan, detect_changes/search_graph/trace_path/get_architecture, recipes hubs/bridges/test-gaps/cross-boundary. Read 2-3 source + 1-2 test files for conventions. >20 files STOP, hub/bridge needs approval.
3.5. Spec Alignment Check
Before creating tasks, verify plan covers all user requirements:
- What did the user actually ask for?
- Any implicit requirements from the problem context?
- No scope gaps (missing features from request)?
- No scope creep (unasked features)?
4. Create Tasks
Each 2-5 min. Exact files, exact code, exact commands. Use format from plan skill template:
## Task N:heading at column 0- All fields (
Status:,Risk:, etc.) at column 0 under the heading — NO indentation - One blank line between tasks, NO blank lines between fields within a task
- Use exact status values:
pending,in_progress,completed,stuckUse format from plan skill template: ## Task N:heading at column 0- All fields (
Status:,Risk:, etc.) at column 0 under the heading — NO indentation - One blank line between tasks, NO blank lines between fields within a task
- Use exact status values:
pending,in_progress,completed,stuck
5. Validate Plan
- Blast radius files covered
- Code style matches conventions found in scan
- Test commands real (from .supergraph-env)
- Hub nodes have review steps
- No placeholders
- Environment Context complete
6. Save Plan
After approval → docs/supergraph/plans/YYYY-MM-DD-<slug>.md
7. Plan Review
After saving, dispatch supergraph:plan-reviewer to verify completeness, spec alignment, task decomposition, and buildability.
If reviewer returns Issues Found, revise the plan and re-run review.
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.
- yesterday First seen · 92 lines · 30 tokens per session scan A 91b7ec86c117
plan-writer is an agent published in the GitHub repository datit309/supergraph (21 stars, last pushed 4d ago), licensed MIT. It adds 30 tokens to every session and 815 once invoked, about $0.0002 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 agents, from other repositories
architect
../../agents/architect.md.
implementer
../../agents/implementer.md.
reviewer
../../agents/reviewer.md.
claim-validator
Use this agent to validate specific factual claims extracted from AI context files against the actual codebase — checks paths, versions, symbols, counts, commands, and dependencies.
context-auditor
Use this agent for deep analysis of a repository's AI context files — discovers all context files across tool ecosystems, assesses staleness, and identifies cross-document drift and contradictions.
knowledge-sources
This is a reference for the researcher agent. Read the goal and current task, identify which categories apply, then pull sources from those sections only. Ignore irrelevant categories — don't load noise.