Coordination logo

Coordination

Organization
transilienceai
coordination

Pentest coordination — orchestrates executor and validator agents with context-controlled spawning. Entry point for all engagements.

Overview

Publishertransilienceai
Repositorycommunitytools
Skill namecoordination
Stars
565
Forks
81
Bundled files
22
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 22 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by transilienceai on GitHub. Read the source before you install it.

Installation

Install the Coordination AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/transilienceai/communitytools/tree/main/skills/coordination
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/coordination .claude/skills/coordination
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Coordination in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Coordination on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Coordination is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Coordination

Runs as a spawned subagent (one per target). Within its own context, the coordinator holds engagement state inline — it does not delegate its thinking to further sub-subagents. Thinks before every action.

The parent orchestrator (main session) must not execute this workflow inline. If you find yourself doing P1-P5 in the main session, you skipped the spawn step in skills/hackthebox/SKILL.md (or the relevant platform skill) and the bookkeeping discipline is silently disabled.

Principle (Rule 0)

Source code first. Read all accessible source — application code, config, scripts, share contents — before any executor batch. Every answer is in the data you already have. Guessing without reading is the most common failure mode.

Workflow

P0: Ingest scope
 ↓
P1: Recon + read source code → write attack-chain.md → run preflight-checklist
 ↓
┌→ P2: Think — read chain + experiments.md, write 3 hypotheses (≥1 [wildcard]), pick 1-2 to test
│  P2b: Research (conditional) — see reference/creative-research.md
│  P3: Execute — spawn 1-2 executors with CHAIN_CONTEXT [+ RESEARCH_BRIEF]
│  P4: Integrate — materialize each candidate; **validate it now** (interleaved, strict per-finding,
│      fresh blind agents) → CONFIRMED | REJECTED | CURE→re-validate | DROPPED; update chain, revise theory
│      Coverage flips only on VALID (coverage-by-VALID); REJECTED/DROPPED classes stay pending → keep searching
│      No progress 1 batch → consider P2b · goal_attempts ≥ 3 on any conceptual goal → P4b
└─ loop (max 30 experiments; mandatory skeptic at experiments 5, 15, 25)

P4b: Reset — re-read all recon + source + chain. Creative Research (mandatory). Fresh theory.
P5: Engagement-thoroughness validation + Report (validated/ = VALID/REPAIRED only; drop-entirely, no gaps section)

Steps

  1. Recon + Source Code — read all accessible code (see formats/reconnaissance.md). Run pre-flight checklist (reference/preflight-checklist.md).
  2. Think — write 3 hypotheses to attack-chain.md, ≥1 tagged [wildcard]. Pick 1-2 to spawn.
  3. Test — 1-2 executors per batch, integrate before next.
  4. Validate (interleaved) — validate each candidate the instant INTEGRATE materializes it, on fresh blind agents (strict per-finding cure/drop loop → CONFIRMED | REJECTED | CURE | DROPPED); at loop end run the engagement-thoroughness validator (see reference/validator-role.md).
  5. Report — the VALID/REPAIRED findings in {OUTPUT_DIR}/artifacts/validated/ (all of them, by construction) → Transilience PDF via formats/transilience-report-style/SKILL.md.

attack-chain.md

{OUTPUT_DIR}/attack-chain.md. Updated every batch. Sections: services, surface, theory (3 hypotheses + chosen), tested, next. Bullets, max 50 lines, prune old items to one-liners.

Bookkeeping

experiments.md ledger, tools/ logs, EXPERIMENT_ID injection, conceptual-goal counting — see reference/bookkeeping.md.

Creative Research (P2b)

Triggers: P4b reset (mandatory), goal_attempts ≥ 3 on any goal, novel error class, source code unreadable, every executor returned negative, no hypothesis at P2, no progress for 1 batch. See reference/creative-research.md. Most batches skip P2b.

Spawning

See reference/spawning-recipes.md for copy-paste-ready spawn patterns per role. Context contracts in reference/context-injection.md. Role boundaries in reference/role-matrix.md.

Roles

RoleFileContextWhen
Executor (explore)reference/executor-role.mdFull chain + skillsRecon / breadth
Executor (exploit)reference/executor-role.mdFull chain + skills + scenariosConfirmed theory
Skepticreference/skeptic-role.mdexperiments.md + recon (no chain)Mandatory at experiments 5, 15, 25
Validator (finding)reference/validator-role.mdEvidence only (blind)Interleaved — each candidate the instant it's materialized; fresh each cure round
Executor (cure)reference/executor-role.mdOnly named failed_checks + missing_evidence (no theory)On a DEMOTED verdict, before re-validation
Validator (engagement)reference/validator-role.mdOUTPUT_DIR only (blind)Once at loop end

Rules

  1. Autonomous. Coordinator MUST NOT call AskUserQuestion. If a credential is missing, run python3 tools/env-reader.py; if it returns NOT_SET, terminate with status=BLOCKED and emit a clear blocker. Asking is the parent orchestrator's job.
  2. Think before acting. Write 3 hypotheses (≥1 wildcard) to attack-chain.md before every batch. Record rejected ones — they are the search tree.
  3. Max 1-2 executors per batch. Recon can use more.
  4. Pass chain context + specific PATT_URL to executors. Not the full PATT map.
  5. 30-experiment cap.
  6. goal_attempts ≥ 3 on a conceptual goal → P4b reset. Count by goal, not literal technique string. Five PKINIT cert variants chasing "use this cert to authenticate" = five strikes against one goal. See reference/bookkeeping.md for the goal column.
  7. Mandatory skeptic at experiments 5, 15, 25 (see reference/skeptic-role.md).
  8. All output to OUTPUT_DIR.
  9. Sequential flag progression in multi-flag engagements. User-foothold first; root path usually flows from there.
  10. No partial completion as a success state. A multi-flag engagement is incomplete until every flag submits. status=FAILED_partial is a temporary marker, never a final outcome.
  11. Phase 3 (skill-update + Slack + queue) is parent-orchestrator only. Coordinator emits PHASE3_SUMMARY and exits.
  12. Source for library internals. Before writing Python against any library API (Impacket, ldap3, pyasn1), read the source. Prefer CLI tools (secretsdump.py, ticketer.py, getST.py).
  13. Background command discipline. State the specific result a tunnel/relay/listener will produce before spawning.
  14. Report gate. Validated findings exist → Transilience PDF report required. Read formats/transilience-report-style/pentest-report.md.
  15. Validation completeness. After validators run, every validated finding has evidence/validation/validation-summary.md. Flag any without proof.

Token Discipline

  • Internal output (chain, logs, reports): bullets, not prose.
  • Executor prompts: 1-2 relevant skill files + the specific PATT_URL.
  • attack-chain.md max 50 lines; bookkeeping max 10% of mission tokens.
  • User-facing output (reports, summaries): detailed.

References

reference/principles.md · reference/preflight-checklist.md · reference/role-matrix.md · reference/bookkeeping.md · reference/spawning-recipes.md · reference/context-injection.md · reference/creative-research.md · reference/executor-role.md · reference/skeptic-role.md · reference/validator-role.md · reference/VALIDATION.md · reference/ATTACK_INDEX.md · reference/OUTPUT_STRUCTURE.md · reference/GIT_CONVENTIONS.md · formats/INDEX.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Coordination AI skill do?

Pentest coordination — orchestrates executor and validator agents with context-controlled spawning. Entry point for all engagements.

Why use Coordination on TypingMind?

Because you install it once and use it with any model. Coordination is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Coordination in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/transilienceai/communitytools/tree/main/skills/coordination. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Coordination?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Coordination?

As many as you like. As long as a model supports skills, you can use Coordination with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Coordination AI skill free?

Yes. It is published on GitHub by transilienceai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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