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/melagiri/code-insights/start-featuregit clone --depth 1 https://github.com/melagiri/code-insightsWhat 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 | $0.00000 | $0.03267 |
| Opus 5 | $0.00000 | $0.01633 |
| Sonnet 5 | $0.00000 | $0.00653 |
| Haiku 4.5 | $0.00000 | $0.00327 |
Grade A, and why
start-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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. First, check if the dev server is already running: `curl -s -o /dev/null -w '%{http_code}' http://localhost:7890/api/health` (or just `http://localhost:7890`) How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/start-feature — Auto-Setup Feature Development Team
Feature: $ARGUMENTS
You are setting up a hybrid agent team for feature development. The PM agent leads the team and owns the ceremony. Your job is minimal setup, then hand control to PM.
Step 1: Create Git Worktree
Slugify the feature description into a branch name:
- Take
$ARGUMENTS, lowercase it, replace spaces/special chars with hyphens, truncate to 50 chars - Prefix with
feature/(e.g., "add demo mode onboarding" ->feature/add-demo-mode-onboarding)
# Example (adapt the slug from $ARGUMENTS):
BRANCH_NAME="feature/<slugified-arguments>"
git fetch origin
git worktree add ../code-insights-${BRANCH_NAME#feature/} -b ${BRANCH_NAME} origin/main
If the worktree or branch already exists, inform the user and ask how to proceed.
Run pnpm install in the worktree:
cd ../code-insights-${BRANCH_NAME#feature/} && pnpm install
Step 2: Create Named Team
Create the team using TeamCreate. Use the slugified branch name (without feature/ prefix) as the team name:
TeamCreate {
team_name: "feat-<slugified-arguments>",
description: "Feature team: $ARGUMENTS"
}
Step 3: Spawn PM Agent
The PM agent handles scoping, GitHub Issues, and task graph creation. It does NOT spawn other agents — only the orchestrator can spawn agents.
Task {
name: "pm-agent",
subagent_type: "product-manager",
team_name: "feat-<slugified-arguments>",
prompt: "You are the PM for this feature team.
FEATURE REQUEST: $ARGUMENTS
WORKTREE: ../code-insights-<slugified-arguments>/
BRANCH: feature/<slugified-arguments>
TEAM: feat-<slugified-arguments>
Your responsibilities are scoping, GitHub Issues, task graph, and handoff preparation.
IMPORTANT: You CANNOT spawn agents. Only the orchestrator can. When you need an agent spawned, message the orchestrator with the request.
Follow this protocol:
1. SCOPE: Read docs in docs/ and CLAUDE.md to understand what this feature involves. Check for existing design docs in docs/plans/. Check docs/architecture/ for architecture context. If the scope is unclear, message the orchestrator for clarification.
2. ISSUES: Search for an existing GitHub Issue that matches this feature. If one exists, use it. If not, create one with proper description, acceptance criteria, and T-shirt size. Record the issue number.
3. TASK GRAPH: Create the ceremony tasks using TaskCreate, then set dependencies with TaskUpdate:
- Task: 'TA: Review architecture alignment' (no dependencies) -- SKIP if internal-only (new components, UI, styling)
- Task: 'LLM Expert: Design prompt architecture' (no dependencies) -- SKIP if feature doesn't involve LLM calls
- Task: 'PM: Prepare handoff context in GitHub Issue' (no dependencies, you do this yourself)
- Task: 'Dev: Read handoff and design docs, prepare questions' (blockedBy: TA + LLM Expert if applicable + PM handoff)
- Task: 'Dev + TA: Reach consensus on implementation approach' (blockedBy: above) -- SKIP if internal-only
- Task: 'Dev: Implement feature in worktree' (blockedBy: above)
- Task: 'Dev: Verify feature and create PR' (blockedBy: above) -- includes pre-PR verification protocol
- Task: 'Review: Triple-layer code review (loops until 0 FIX NOW)' (blockedBy: above)
- Task: 'Post final review summary to GitHub PR' (blockedBy: above)
4. DO YOUR OWN TASK: Work on your handoff task -- prepare context in the GitHub Issue (description, acceptance criteria, relevant doc paths, implementation guidance).
IMPORTANT — Classify the feature scope for Dev's verification protocol:
- Does it touch UI/visual rendering? → Tag: VISUAL
- Does it produce output artifacts (images, PDFs, exports)? → Tag: OUTPUT_ARTIFACT
- Does it add new npm dependencies? → Tag: NEW_DEPS
- Does it change API endpoints? → Tag: API_CHANGE
Include these tags in the GitHub Issue so Dev knows which verification tiers apply.
5. REQUEST AGENT SPAWNS: When your handoff task is done, message the orchestrator:
- If TA is needed: 'SPAWN_REQUEST: ta-agent — [brief context]'
- When dev prerequisites are met: 'SPAWN_REQUEST: dev-agent — [brief context including issue number and key details]'
The orchestrator will spawn the agents and assign tasks.
SKIP TA if the feature is internal-only (new components, UI fixes, styling, LLM provider additions). In that case, mark the TA task as completed with note 'Skipped -- internal-only change'.
If the feature involves LLM calls (prompts, model selection, token budgets, structured output):
- 'SPAWN_REQUEST: llm-expert-agent — [brief context about the LLM aspect]'
The LLM Expert will design the prompt architecture, token budget, and model recommendation before dev implements.
SKIP LLM Expert if the feature doesn't touch LLM code. Mark the task as completed with note 'Skipped -- no LLM impact'.
6. MONITOR: Check TaskList periodically.
- When Dev creates a PR, message the orchestrator: 'REVIEW_REQUEST: PR #XX is ready for /start-review'
- The orchestrator will run /start-review which loops until 0 FIX NOW items
- If review finds FIX NOW items, the orchestrator sends them to Dev. Dev fixes and pushes. Review re-runs automatically.
- You do NOT need to request additional review rounds — the loop is built into /start-review.
7. REPORT: When review converges (0 FIX NOW items) and final summary is posted, message the orchestrator: 'PR #XX for $ARGUMENTS is ready for founder review and merge.'
IMPORTANT RULES:
- NEVER merge PRs -- founder-only
- NEVER try to spawn agents -- message the orchestrator instead
- All dev work happens in the worktree
- If you need user clarification, message the orchestrator who will ask the user
- You can message existing teammates directly via SendMessage for routine coordination
- Task dependencies enforce ceremony order -- don't skip steps
- PRE-PR GATE (MANDATORY): Before any PR is created, Dev must complete the full verification protocol (see Dev agent instructions). GitHub Actions costs money — failed CI runs are wasted spend.",
mode: "bypassPermissions"
}
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 · 296 lines · 0 tokens per session scan A cfa29162bd5b
start-feature is a command published in the GitHub repository melagiri/code-insights (76 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,267 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
fastapi_stripe
A Stripe Checkout implementation takes four steps.
lians-audit
Run a Lians compliance/audit operation - snapshot, chain verify, backtest, or erasure.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.
lians-remember
Store a fact in Lians memory with its business event-time and metadata.
wrap
Close the session cleanly. Update entity state. Capture decisions. Detect momentum shifts.
decide
Capture a strategic decision so it never gets lost or reopened accidentally.