developer

A development workflow for coding agents covering research, planning, implementation, and verification. It requires agents to inspect dependencies and history, plan affected files, make focused edits, and test the result.

In plain words
What is it for?
Use it for non-trivial development work, especially when investigating a bug, changing several files, relying on external libraries, or checking for regressions.
Why use it?
It reduces accidental changes and makes the cause, implementation, and correctness of a change easier to verify.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/dineshdb/pie/developer
Any agent
npx skills add dineshdb/pie --skill developer
Clone the repo
git clone --depth 1 https://github.com/dineshdb/pie

Made for: Claude Code, Codex.

Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 358 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5 $0.00014 $0.00358
Opus 5 $0.00007 $0.00179
Sonnet 5 $0.00003 $0.00072
Haiku 4.5 $0.00001 $0.00036

Measured yesterday against content hash ad505be47fad, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

developer 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.

.pie/skills/developer/SKILL.md · 42 lines

What it actually says

Development Lifecycle Orchestration

Follow this rigorous loop for any non-trivial step.

Phase 1: Research & Discovery

Gather context before proposing changes.

  • Map Dependencies: Use filesystem to find relevant files and imports.
  • Trace Logic: Use grep to understand data flow and symbol usage.
  • Reference Docs: Use context7 for external libraries.
  • Check History: Use git to see how code evolved.

Phase 2: Strategy & Planning

Define the "What" and "How" before implementation.

  • Identify every file to be modified.
  • Outline the logic changes.
  • Define Verification: Plan how success will be proven.

Phase 3: Implementation (The Edit Loop)

Execute minimal, verified changes.

  • Use filesystem for surgical edits.
  • Follow existing project patterns and style.
  • Prefer composition and simplicity over abstractions.

Phase 4: Verification & Hardening

Apply the verification workflow.

  • Repro First: Confirm the bug/absence of feature.
  • Verify Fix: Prove the change works.
  • Regression Test: Ensure no collateral damage.

Core Principles

  1. Read before Write: Context is everything.
  2. Surgical Precision: Minimal changes for maximum effect.
  3. Evidence over Assertion: Prove it works with output.
  4. Root Cause Fixes: Fix the source, not the symptom.
  5. Simplify Continuously: Leave the codebase cleaner than you found it.
Changes

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.

  1. yesterday First seen · 42 lines · 14 tokens per session scan A ad505be47fad

Subscribe to this mod's changes

developer is a skill published in the GitHub repository dineshdb/pie (2 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 358 once invoked, about $0.0001 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 2026-08-31.

Related

Other skills, from other repositories

release

Create a new versioned release with changelog. Bumps version in code, updates CHANGELOG.md, commits, tags, and pushes using the repository release flow. GitHub Actions creates the release and uses only the current changelog section as release notes. Use when the user says "release", "cut a release", "bump version"…

genai-io/san · 75 tokens

qa

Regression test a San feature by name. Looks for a feature doc in docs/reference/ or docs/packages/2-feature/, runs automated Go tests and interactive tmux tests, then produces a pass/fail report. Use this skill when the user says "qa", "regression test", "test feature X", "verify feature", or references a feature…

genai-io/san · 85 tokens

openspec-explore

Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.

KunAgent/Kun · 39 tokens

pi-sync

Daily upstream-sync job for the pi Go port — fetch upstream pi, triage every change since the recorded pin, port what's in scope, verify idiomatic + parity via independent reviews, update the ledger, and push. Use for "sync with upstream", "porting job", or as the scheduled daily run.

sky-valley/pi · 66 tokens

proactive-agent

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞.

MerkyorLynn/Lynn · 46 tokens

novel-workshop

小说创作工作台。用户说写小说、创作小说、写故事、写穿越文、写言情、写科幻、创作故事、开始创作、继续写、写下一章、多视角、POV、装订成册时使用。AI-assisted novel writing workbench for outline, characters, chapter drafting, multi-POV narrative, editing, and book assembly.

MerkyorLynn/Lynn · 90 tokens