make-plan
Create a detailed, phased implementation plan with documentation discovery.
Skill by longhang2004
Chalk Labs
Need help deploying make-plan?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/longhang2004-make-plan
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: productivity
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions: not checked yet.
What's inside
- Skill name
make-plan- Description
- Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.
- License
- MIT
- Instructions
- 64 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Make Plan You are an ORCHESTRATOR. Create an LLM-friendly plan in phases that can be executed consecutively in new chat contexts. ## Delegation Model Use subagents for *fact gathering and extraction* (docs, examples, signatures, grep results). Keep *synthesis and plan authoring* with the orchestrator (phase boundaries, task framing, final wording). If a subagent report is incomplete or lacks evidence, re-check with targeted reads/greps before finalizing. ### Subagent Reporting Contract (MANDATORY) Each subagent response must include: 1. Sources consulted (files/URLs) and what was read 2. Concrete findings (exact API names/signatures; exact file paths/locations) 3. Copy-ready snippet locations (example files/sections to copy) 4. "Confidence" note + known gaps (what might still be missing) Reject and redeploy the subagent if it reports conclusions without sources. ## Plan Structure ### Phase 0: Documentation Discovery (ALWAYS FIRST) Before planning implementation, deploy "Documentation Discovery" subagents to: 1. Search for and read relevant documentation, examples, and existing patterns 2. Identify the actual APIs, methods, and signatures available (not assumed) 3. Create a brief "Allowed APIs" list citing specific documentation sources 4. Note any anti-patterns to avoid (methods that DON'T exist, deprecated parameters) The orchestrator consolidates findings into a single Phase 0 output. ### Each Implementation Phase Must Include 1. **What to implement** — Frame tasks to COPY from docs, not transform existing code - Good: "Copy the V2 session pattern from docs/examples.ts:45-60" - Bad: "Migrate the existing code to V2" 2. **Documentation references** — Cite specific files/lines for patterns to follow 3. **Verification checklist** — How to prove this phase worked (tests, grep checks) 4. **Anti-pattern guards** — What NOT to do (invented APIs, undocumented params) ### Final Phase: Verification 1. Verify all implementations match documentation 2. Check for anti-patterns (grep for known bad patterns) 3. Run tests to confirm functionality ## Key Principles - Documentation Availability ≠ Usage: Explicitly require reading docs - Task Framing Matters: Direct agents to docs, not just outcomes - Verify > Assume: Require proof, not assumptions about APIs - Session Boundaries: Each phase should be self-contained with its own doc references ## Anti-Patterns to Prevent - Inventing API methods that "should" exist - Adding parameters not in documentation - Skipping verification steps - Assuming structure without checking examples ## See Also - `oh-my-issues` — the issue-side sibling. When the plan you're being asked to make is rooted in a bug or feature backlog rather than a fresh idea, route through `oh-my-issues` first to cluster issues by root cause into plan masters and `plans/0X-*.md` design docs. `make-plan` then operates on the design doc for one plan slice.
From longhang2004/Tenvyr, MIT.
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jul 28, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/longhang2004/Tenvyr
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
Same skill elsewhere
Copies of this SKILL.md published by others; search shows only one of them.
More from longhang2004
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get make-plan running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
make-plan skill. Agent Mart. https://agentmart.dev/skills/longhang2004-make-plan (last updated 2026-10-24)