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/coleam00/context-engineering-intro/plan-featuregit clone --depth 1 https://github.com/coleam00/context-engineering-introWhat 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 | $0.00010 | $0.01292 |
| Opus 5 | $0.00005 | $0.00646 |
| Sonnet 5 | $0.00002 | $0.00258 |
| Haiku 4.5 | $0.00001 | $0.00129 |
Grade A, and why
plan-feature scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
5. Manual test: {specific curl command or UI steps to verify the feature} This is a copy
94% identical to plan-feature — 4 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Feature: Comprehensive Archon Implementation Planning
Objective
Produce a detailed, actionable implementation plan for: $ARGUMENTS
The plan will be saved to .claude/archon/plans/{kebab-case-name}.md and is designed to be
consumed by the /execute command.
Phase 1: Feature Understanding
Restate the feature request in your own words. Identify:
- Problem being solved — What user pain point or capability gap does this address?
- Success criteria — What does "done" look like? How will we verify it works?
- Scope boundaries — What is explicitly in scope vs. out of scope?
- Package impact — Which of the 8 packages are affected? (
paths,git,isolation,workflows,core,adapters,server,web) - Interface changes — Does this touch
IPlatformAdapter,IAssistantClient,IDatabase, orIWorkflowStore? New interfaces needed?
Phase 2: Codebase Intelligence
Use subagents to perform targeted codebase research in parallel. Spawn separate subagents for:
Subagent A — Affected package deep-dive: Read all relevant source files in the affected packages. Map the current data flow. Identify every file that will need to change.
Subagent B — Interface and type contracts:
Read packages/core/src/types/ and relevant index.ts exports. Understand what interfaces
exist and how they're consumed across packages.
Subagent C — Test patterns: Find existing test files similar to the area of change:
find packages/ -name "*.test.ts" | head -30
Read 2-3 representative test files to understand mocking patterns, assertion style, and
mock.module() isolation requirements per package.
Subagent D — Related prior work:
git log --oneline --all | head -20
Read recent commits touching relevant files to understand change patterns.
Synthesize findings: current state, gaps, constraints.
Phase 3: External Research (if needed)
If the feature involves external APIs, new libraries, or unfamiliar patterns, use web search to research:
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 · 171 lines · 10 tokens per session scan A 530f83cd9cf9
plan-feature is a command published in the GitHub repository coleam00/context-engineering-intro (13,819 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 1,292 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 94% identical to plan-feature, differing in 4 lines, and is treated as a copy.
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.