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Agent skill

Iterative Retrieval

by kubefleet-dev in kubefleet-dev/kubefleet

Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation.

MITAuto-check passedAgent Workflows

Install Iterative Retrieval

skills CLI
$ npx skills add kubefleet-dev/kubefleet --skill iterative-retrieval -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install kubefleet-dev/kubefleet iterative-retrieval --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/kubefleet-dev/kubefleet.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/iterative-retrieval .claude/skills/iterative-retrieval && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
iterative-retrieval
GitHub stars
162
Used in
1 other repo
Token cost
~1.6k tokens
SKILL.md length
564 words
Files
1
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation.

  • Works in 3 steps: After each cycle, the coordinator… → Objective context forward: each… → Cycle 3 exhausted → escalate: write a…
  • Spawning sub-agents to complete scoped work
  • SKILL.md covers Spawn Prompt Template, 3-Cycle Protocol, Coordinator Validation Checklist and When to Escalate vs Retry, plus 4 more sections
  • Calls gh

What it does

Iterative Retrieval is an agent skill from kubefleet-dev/kubefleet. Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Use when spawning sub-agents to complete scoped work.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Subagents. The repository describes itself as: KubeFleet is an open-source Kubernetes multi-cluster application management solution. The licence is MIT.

When your agent uses it

  • Spawning sub-agents to complete scoped work
  • Tasks that involve Subagents

Example prompts

  • “/iterative-retrieval”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. After each cycle, the coordinator evaluates the output against the success criteria
  2. Objective context forward: each subsequent spawn includes a summary of what was tried
  3. Cycle 3 exhausted → escalate: write a summary to .squad/decisions/inbox/, label the

What it can do on your machine

Read from SKILL.md and the folder at commit ea05fcb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Iterative Retrieval loads about 1.6k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 564 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from kubefleet-dev/kubefleet at commit ea05fcb, republished under its MIT licence (© kubefleet-dev). 564 words, ~1,572 tokens.

Download SKILL.mdSave it as .claude/skills/iterative-retrieval/SKILL.md (or your agent's skills folder).
name
iterative-retrieval
description
Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Use when spawning sub-agents to complete scoped work.
domain
agent-coordination
confidence
high
license
MIT

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
Show full SKILL.md (239 more words)Show less

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.

© kubefleet-dev, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/iterative-retrieval of kubefleet-dev/kubefleet.

Open the folder on GitHubat commit ea05fcb

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in kubefleet-dev/kubefleet, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Iterative Retrieval next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Iterative Retrieval compared with similar skills
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Iterative Retrieval this skillkubefleet-dev/kubefleet1621 repos~1.6kAutomated safety check: PassMIT
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Subagent Driven DevelopmentAsvarox/allkaraoke26138 repos~1.2kAutomated safety check: PassNone
Dispatching Parallel Agentsultralisp/ultralisp25841 repos~1.5kAutomated safety check: PassNone
Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence
Task Observerrebelytics/one-skill-to-rule-them-all3.2k1 repos~12kAutomated safety check: PassCC-BY-4.0

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Categories

Questions about Iterative Retrieval

What does Iterative Retrieval do?

Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Iterative Retrieval is an agent skill from kubefleet-dev/kubefleet. Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation.

When should I use Iterative Retrieval?

Iterative Retrieval fits situations like: spawning sub-agents to complete scoped work; tasks that involve Subagents.

How do I install Iterative Retrieval in Claude Code?

Run `npx skills add kubefleet-dev/kubefleet --skill iterative-retrieval -a claude-code`. Or copy the skill folder (.github/skills/iterative-retrieval in kubefleet-dev/kubefleet) into .claude/skills/iterative-retrieval in your project. Claude Code loads it when a task matches its description.

How do I install Iterative Retrieval in Codex?

Run `npx skills add kubefleet-dev/kubefleet --skill iterative-retrieval -a codex`. Or copy the skill folder (.github/skills/iterative-retrieval in kubefleet-dev/kubefleet) into .agents/skills/iterative-retrieval in your project. Codex loads it when a task matches its description.

Can I use Iterative Retrieval in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kubefleet-dev/kubefleet --skill iterative-retrieval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iterative-retrieval, .gemini/skills/iterative-retrieval, .github/skills/iterative-retrieval and .opencode/skills/iterative-retrieval in your project.

What does Iterative Retrieval need to run?

Going by SKILL.md and its folder, Iterative Retrieval needs the command-line tools its instructions call (gh).

Does Iterative Retrieval access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Iterative Retrieval safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Iterative Retrieval use?

Iterative Retrieval is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Iterative Retrieval use?

About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Iterative Retrieval?

Skills that share tags, products or a category with Iterative Retrieval: Claude Code Agent Development (anthropics/claude-plugins-official, 38k stars), Subagent Driven Development (Asvarox/allkaraoke, 261 stars), Dispatching Parallel Agents (ultralisp/ultralisp, 258 stars) and Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Iterative Retrieval?

kubefleet-dev (a GitHub organization) maintains it in kubefleet-dev/kubefleet, which has 162 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on October 8, 2026.

Source: kubefleet-dev/kubefleet on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.