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Recon Tool Integration

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samugit83
recon-tool-integration

Adding a new tool to the recon pipeline: the enrichment-module contract and its isolated wrapper (the actual fan-out and test call path), graph completeness, and the preset catalog that silently strips unknown settings. Miss the isolated wrapper and the tool never runs in parallel; miss the catalog and AI presets drop its config. Trigger: adding a tool to the recon pipeline; a new recon/*.py or recon/main_recon_modules/*.py enrichment module; editing the execution groups in recon/main.py or the IMAGES array in recon/entrypoint.sh.

Overview

Publishersamugit83
Repositoryredamon
Skill namerecon-tool-integration
Stars
2.5K
Forks
504
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by samugit83 on GitHub. Read the source before you install it.

Installation

Install the Recon Tool Integration 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.
  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/samugit83/redamon.git /tmp/redamon
mkdir -p .claude/skills
cp -r /tmp/redamon/skills/recon-tool-integration .claude/skills/recon-tool-integration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recon Tool Integration 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 Recon Tool Integration 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 Recon Tool Integration 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.

When to Use

  • Adding a brand-new scanning/enrichment tool to the scan pipeline.

For adding AI decision-making to an existing tool, use recon-ai-enrichment. For the graph write, use graph-db-writes. For the settings, use project-settings-cascade. This skill is the module + pipeline wiring.


Critical Rules

  • NEVER ship the enrichment module without its _isolated wrapper. run_<tool>_enrichment_isolated() is the actual call path for the GROUP 3b parallel fan-out AND for every unit test; the plain run_<tool>_enrichment() alone is never fan-out-safe. Reference: recon/main_recon_modules/censys_enrich.py:369.
  • NEVER vary the top-level result key. The module writes combined_result["<tool>"] and the wrapper returns snapshot.get("<tool>", {}) with the same identifier used everywhere else in the pipeline (censys_enrich.py:365).
  • NEVER collect a field and not write it to the graph. Every field in the output dict must land on a node/relationship or it is silent data loss - see graph-db-writes.
  • NEVER add a tool setting without updating BOTH preset layers. Add it to the Zod recon-preset-schema.ts and to RECON_PARAMETER_CATALOG in webapp/src/app/api/presets/generate/route.ts. Miss the Zod schema and AI-generated presets silently strip the setting; miss the catalog and the preset LLM never knows the tool exists.
  • ALWAYS prefix every print() log [symbol][ToolName] ([*] progress, [+] success, [-] skipped, [!] error). Recon stdout is tailed into the SSE recon drawer; a bare print() is invisibly formatted.
  • ALWAYS place a fan-out tool behind the deep-copy _isolated wrapper and in the correct execution group in recon/main.py; never parallelize across a dependency boundary (a tool needing live URLs cannot run before GROUP 4).
  • A new tool setting FAILS THE BUILD until it is in the registry. Every parameter is described once in recon_settings/registry.yaml, and a test walking Prisma.ProjectScalarFieldEnum fails until every column has an entry. Draft it with python3 tooling/scripts/extract_recon_registry.py, EDIT IT, then python3 recon_settings/build.py.
  • Tuning is OPEN and controlled at the point of use, not by being refused. A rate is capped to the engagement ceiling at scan start; a wordlist path outside the project's own directories is dropped. A container image is a closed values: list and an out-of-set value is REFUSED at the write, because accepting one and replacing it at scan start made get_recon_settings echo an image the scan would never run.
  • Two things stay closed, and only two. The engagement SCOPE (mcp: create_only, set once by create_project) and the engagement RECORD (mcp: never, deny_reason: engagement-record - the client, the contacts, the dates, the document). The engagement's LIMITS are ordinary mcp: settable fields in the engagement_limits group: they are reachable from the form and from MCP alike, and what makes them safe is that each is enforced at scan start whatever the setting says. See README.MCP.SERVER.md.
  • A settable field with no form input fails parity.test.ts, and so does a form input with no classification. Neither door may reach something the other cannot. form_section is joined from the tool's own entry, so a field lands beside its tool automatically.
  • A new rps field with traffic: active and no roe_capped: true fails the build. That gap is how three rate limits shipped reachable over MCP and outside the ceiling.

Enrichment-module contract (copy target)

python
def run_censys_enrichment(combined_result: dict, settings: dict) -> dict:
    ...                                              # mutate in place
    combined_result["censys"] = censys_data          # top-level key == tool id, everywhere
    return combined_result

def run_censys_enrichment_isolated(combined_result: dict, settings: dict) -> dict:
    """Thread-safe: does not mutate combined_result. The fan-out + test call path."""
    import copy
    snapshot = copy.deepcopy(combined_result)
    run_censys_enrichment(snapshot, settings)
    return snapshot.get("censys", {})                 # returns only this tool's payload

From recon/main_recon_modules/censys_enrich.py.

Commands

bash
docker compose build recon                            # if you added a Docker-based tool (recon/entrypoint.sh IMAGES)
docker compose exec webapp npx prisma db push         # for new settings (NEVER prisma migrate)
./redamon.sh test unit                                # recon + root-recon sections

Resources

Frequently asked questions

What does the Recon Tool Integration AI skill do?

Adding a new tool to the recon pipeline: the enrichment-module contract and its isolated wrapper (the actual fan-out and test call path), graph completeness, and the preset catalog that silently strips unknown settings. Miss the isolated wrapper and the tool never runs in parallel; miss the catalog and AI presets drop its config. Trigger: adding a tool to the recon pipeline; a new recon/*.py or recon/main_recon_modules/*.py enrichment module; editing the execution groups in recon/main.py or the IMAGES array in recon/entrypoint.sh.

Why use Recon Tool Integration on TypingMind?

Because you install it once and use it with any model. Recon Tool Integration 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 Recon Tool Integration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/samugit83/redamon/tree/master/skills/recon-tool-integration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Recon Tool Integration?

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 Recon Tool Integration?

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

Is the Recon Tool Integration AI skill free?

Yes. It is published on GitHub by samugit83 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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