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 skills/junmystery/agent-guidance-python/claude-devfleetnpx skills add JunMystery/Agent-Guidance-Python --skill claude-devfleetgit clone --depth 1 https://github.com/JunMystery/Agent-Guidance-PythonWhat 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 | $0.00036 | $0.01451 |
| Opus 5 | $0.00018 | $0.00726 |
| Sonnet 5 | $0.00007 | $0.00290 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
claude-devfleet 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.
This is a copy
95% identical to claude-devfleet — 3 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude DevFleet Multi-Agent Orchestration
When to Use
Use this skill when you need to dispatch multiple Claude Code agents to work on coding tasks in parallel. Each agent runs in an isolated git worktree with full tooling.
Setup
The DevFleet server is a separate project, not bundled with ECC. Install and run it from its repository first: https://github.com/LEC-AI/claude-devfleet
Then connect the running instance via MCP:
claude mcp add devfleet --transport http http://localhost:18801/mcp
Before first use, verify the process listening on port 18801 is the DevFleet binary you installed (see SECURITY.md on localhost MCP servers).
How It Works
User → "Build a REST API with auth and tests"
↓
plan_project(prompt) → project_id + mission DAG
↓
Show plan to user → get approval
↓
dispatch_mission(M1) → Agent 1 spawns in worktree
↓
M1 completes → auto-merge → auto-dispatch M2 (depends_on M1)
↓
M2 completes → auto-merge
↓
get_report(M2) → files_changed, what_done, errors, next_steps
↓
Report back to user
Tools
| Tool | Purpose |
|---|---|
plan_project(prompt) |
AI breaks a description into a project with chained missions |
create_project(name, path?, description?) |
Create a project manually, returns project_id |
create_mission(project_id, title, prompt, depends_on?, auto_dispatch?) |
Add a mission. depends_on is a list of mission ID strings (e.g., ["abc-123"]). Set auto_dispatch=true to auto-start when deps are met. |
dispatch_mission(mission_id, model?, max_turns?) |
Start an agent on a mission |
cancel_mission(mission_id) |
Stop a running agent |
wait_for_mission(mission_id, timeout_seconds?) |
Block until a mission completes (see note below) |
get_mission_status(mission_id) |
Check mission progress without blocking |
get_report(mission_id) |
Read structured report (files changed, tested, errors, next steps) |
get_dashboard() |
System overview: running agents, stats, recent activity |
list_projects() |
Browse all projects |
list_missions(project_id, status?) |
List missions in a project |
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 · 112 lines · 36 tokens per session scan A 3c3cf89e041a
claude-devfleet is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,451 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to claude-devfleet, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
common-feedback-reporter
Pre-write audit for skill violations: checks planned code against loaded skill anti-patterns before any file write. Use when writing Flutter/Dart/TS code or editing SKILL.md files with active project skills. Load as composite; on auto-fixed violation, also load +common/common-learning-log.
common-exploit-verification
Enforce "No Exploit, No Report" policy with PoC construction standards, false-positive filtering, and evidence collection per vulnerability class across backend, frontend, and mobile. Use when validating security findings, constructing exploit proofs, filtering false positives, or writing pentest findings.
common-session-retrospective
Analyze conversation corrections to detect skill gaps and prepare targeted skill-library maintenance tasks. Use after any session with user corrections, rework, or retrospective requests. After finding correction loops, also load +common/common-learning-log to persist mistake entries to AGENTSLEARNING.md.
common-store-changelog
Generate user-facing release notes for the App Store and Google Play from git history (App Store <=4000 chars, Google Play <=500). Use when generating release notes, app store changelog, play store release, or "what's new" text for a mobile app.
common-code-review
Conduct high-quality, persona-driven code reviews. Use when reviewing PRs, critiquing code quality, or analyzing changes for team feedback.
common-workflow-writing
Rules for writing concise, token-efficient workflow and skill files. Prevents over-building that requires costly optimization passes. Use when creating or editing workflow files, SKILL.md files, or new skill definitions.