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Iterative Retrieval

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bradygaster
iterative-retrieval

Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Use when spawning sub-agents to complete scoped work.

Overview

Publisherbradygaster
Repositorysquad
Skill nameiterative-retrieval
Stars
3.3K
Forks
506
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 bradygaster on GitHub. Read the source before you install it.

Installation

Install the Iterative Retrieval 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/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/iterative-retrieval
  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/bradygaster/squad.git /tmp/squad
mkdir -p .claude/skills
cp -r /tmp/squad/packages/squad-cli/templates/skills/iterative-retrieval .claude/skills/iterative-retrieval
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Iterative Retrieval 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 Iterative Retrieval 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 Iterative Retrieval 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.

Iterative Retrieval Skill

Squad agents frequently spawn sub-agents to complete scoped work. Without structure, these handoffs become vague, cycles multiply, and outputs land without being checked. The Iterative Retrieval Pattern caps cycles at 3, mandates WHY context in every spawn, and requires the coordinator to validate agent output before closing an issue.


Spawn Prompt Template

Every agent spawn must include the following four sections. Copy and fill in the template:

## Task
{What you need done — concrete and bounded}

## WHY this matters
{The motivation and context. What system or user goal does this serve? What breaks if skipped?}

## Success criteria
{How you will know the output is correct. Be explicit — list acceptance criteria, not vibes.}
Example:
- [ ] File X exists and contains Y
- [ ] No regressions in existing tests
- [ ] PR is open targeting main with description matching the issue

## Escalation path
{What the agent should do if uncertain or stuck. "Stop and ask me" is valid.}
Example:
- If requirements are ambiguous → stop, comment on the issue, set label status:needs-decision
- If blocked by a dependency → label status:blocked, explain in a comment
- If 3 cycles exhausted without resolution → write a summary to inbox and surface to coordinator

3-Cycle Protocol

CycleDescriptionExit condition
1Initial attemptDone → coordinator validates. Incomplete → surface delta.
2Targeted retry with specific correctionsDone → coordinator validates. Incomplete → one more.
3Final attempt with all context from cycles 1–2Done or escalate — no cycle 4.

Rules

  1. After each cycle, the coordinator evaluates the output against the success criteria before accepting it or spawning the next cycle.
  2. Objective context forward: each subsequent spawn includes a summary of what was tried and what is still missing — not just a repeat of the original task.
  3. Cycle 3 exhausted → escalate: write a summary to .squad/decisions/inbox/, label the issue status:needs-decision, and notify the user.

Coordinator Validation Checklist

Before accepting agent output and closing an issue, the coordinator must check:

  • All success criteria from the spawn prompt are met
  • PR exists and description matches the issue (if code work)
  • No obvious regressions (grep for TODO/FIXME introduced, build passes)
  • Agent did not silently skip parts of the task
  • If the agent reported uncertainty — was it resolved or escalated?

If any item fails → do not accept. Spawn cycle N+1 (up to cycle 3) with specific deltas.


When to Escalate vs Retry

Retry (cycle N+1) when:

  • Output is structurally correct but missing specific items
  • Agent misunderstood scope (provide more context and re-run)
  • Partial success — clearly identified remaining delta

Escalate when:

  • Requirements are fundamentally unclear (decision needed)
  • 3 cycles complete without convergence
  • Agent returned conflicting results across cycles
  • Task requires elevated permissions or external action
  • The work depends on another issue that isn't done yet

Issue Dedup Check (Mandatory)

Before any agent creates a GitHub issue, it must search for existing open issues to avoid duplicates.

bash
# Check for existing open issues before creating a new one
gh issue list --search "<keywords from your issue title>" --state open
  • If an open issue already covers the same problem → comment on it instead of creating a new one.
  • If no duplicate → proceed to create the issue.
  • Use 2–3 representative keywords from the planned issue title as the search query.

Mandatory Output Requirement (Research-Then-Execute)

Every research or analysis task completed under this protocol MUST end with at least one concrete action before the cycle is closed. Acceptable follow-up actions:

  • GitHub issue created documenting the findings and next steps
  • PR opened implementing a recommendation
  • Decision recorded in .squad/decisions/inbox/
  • Documented recommendation with a named assignee and due date

Pure analysis reports without actionable follow-up will be rejected during triage. If no action is warranted, the agent must explicitly state why and get coordinator sign-off.


Anti-Patterns

  • Spawning without WHY — agents can't prioritise trade-offs without motivation context.
  • Accepting output without validating — one failed check avoids merging broken work.
  • Cycle 4+ — if 3 cycles haven't converged, the problem is in the requirements, not the agent.
  • Vague success criteria — "looks good" is not a criterion. Use checkboxes.
  • Forwarding WHAT without delta — cycle 2+ prompts must include what cycle 1 got wrong.
  • Creating issues without dedup check — always search before creating.
  • Research without action — delivering analysis with no issue, PR, decision, or assignee is incomplete work.

Examples

Good spawn prompt

## Task
Add an "Iterative Retrieval Protocol" section to `.squad/agents/coordinator/charter.md` explaining
the 3-cycle rule, WHY format, and validation checklist.

## WHY this matters
The coordinator spawns sub-agents on every round. Without a documented protocol, agents run unbounded
cycles and outputs go unvalidated — leading to stale issues and silent failures.

## Success criteria
- [ ] Section "Iterative Retrieval Protocol" exists in charter.md
- [ ] Section documents max-3-cycles rule
- [ ] Section documents WHY format requirement
- [ ] Section contains validation checklist (at least 4 items)
- [ ] No other sections of charter.md are modified

## Escalation path
If the charter.md format is unclear, check another agent charter as a reference.
If uncertain about content, stop and surface to coordinator.

Bad spawn prompt (don't do this)

Update the coordinator charter with the iterative retrieval stuff.

Frequently asked questions

What does the Iterative Retrieval AI skill do?

Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Use when spawning sub-agents to complete scoped work.

Why use Iterative Retrieval on TypingMind?

Because you install it once and use it with any model. Iterative Retrieval 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 Iterative Retrieval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/iterative-retrieval. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Iterative Retrieval?

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 Iterative Retrieval?

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

Is the Iterative Retrieval AI skill free?

Yes. It is published on GitHub by bradygaster 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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