Agent skill

Iterative Plan Review

by testdouble in testdouble/han

Sharpens and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it in place and recording every finding and iteration in cross-referenced companion files.

MITAuto-check passedTesting & QA

Install Iterative Plan Review

skills CLI
$ npx skills add testdouble/han --skill iterative-plan-review -a claude-code

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

GitHub CLI
$ gh skill install testdouble/han iterative-plan-review --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/testdouble/han.git skills-src && mkdir -p .claude/skills && cp -r skills-src/han-planning/skills/iterative-plan-review .claude/skills/iterative-plan-review && 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-plan-review
GitHub stars
279
Token cost
~10k tokens
SKILL.md length
4,782 words
Files
8 (incl. scripts, references)
Skills in repo
54
Repo updated
First seen
Licence
MIT

At a glance

Sharpens and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it in place and recording every finding and iteration in cross-referenced companion files.

  • Works in 8 steps: Locate the Plan and Set Up Companion Files → Choose Review Mode and Size → Select the Team (team mode only) → …
  • The user wants to iterate on
  • SKILL.md covers Project Context, Review Approach, Step 1: Locate the Plan and… and Step 2: Choose Review Mode and…, plus 6 more sections
  • Runs Shell scripts from its folder; calls bash

What it does

Iterative Plan Review is an agent skill from testdouble/han. Sharpens and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it in place and recording every finding and iteration in cross-referenced companion files. Use this skill whenever the user wants to iterate on, refine, tighten, or improve a plan. Also use it when the user asks to verify, validate, or confirm feasibility of an approach. Does not implement plan steps, write test plans, review code, or investigate bugs, and does not generate new plans from scratch — use…

Its SKILL.md is about 10k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/iteration-checklist.md`, `references/review-findings-template.md` and `references/review-iteration-history-template.md`).

It sits in Testing & QA, covering Code review, Test generation and Load testing. The repository describes itself as: Han: AI skills and agents for "Solo" product engineers and small teams. The licence is MIT.

When your agent uses it

  • The user wants to iterate on
  • The user asks to verify
  • Confirm feasibility of an approach

Example prompts

  • “Use the iterative-plan-review skill to sharpen and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it…”
  • “/iterative-plan-review”

Requirements

  • A Bash shell
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Glob, Grep, Agent, Bash(find *), Bash(bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh")

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Locate the Plan and Set Up Companion Files
  2. Choose Review Mode and Size
  3. Select the Team (team mode only)
  4. Lightweight Iteration Loop (lightweight mode only)
  5. Team Iteration Rounds (team mode only)
  6. Update the Plan's Review History Section
  7. 5: Readability Self-Check
  8. User Review

What it can do on your machine

Read from SKILL.md and the folder at commit abba73a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • Agent
    • Bash(find *)
    • Bash(bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh")

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • bash

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

  • Network

    No URLs in SKILL.md.

    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 Plan Review loads about 10k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 170 tokens; SKILL.md has 4,782 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~170
When it runs · the whole SKILL.md, loaded when a task matches
~10k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from testdouble/han at commit abba73a, republished under its MIT licence (© testdouble). 4,782 words, ~10,250 tokens.

Download SKILL.mdSave it as .claude/skills/iterative-plan-review/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
iterative-plan-review
description
Sharpens and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it in place and recording every finding and iteration in cross-referenced companion files. Use this skill whenever the user wants to iterate on, refine, tighten, or improve a plan. Also use it when the user asks to verify, validate, or confirm feasibility of an approach. Does not implement plan steps, write test plans, review code, or investigate bugs, and does not generate new plans from scratch — use plan-a-feature for a new plan. Runs its review rounds to completion without pausing between them; to review each round as it lands, use pairing.
allowed-tools
Read, Write, Edit, Glob, Grep, Agent, Bash(find *), Bash(bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh")
arguments
size
argument-hint
[size: small | medium | large | dynamic] [context or path to plan file]

Project Context

  • CLAUDE.md: !find . -maxdepth 1 -name "CLAUDE.md" -type f
  • project-discovery.md: !find . -maxdepth 3 -name "project-discovery.md" -type f
  • personal config directory: !bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh" 2>/dev/null || echo "$HOME/.claude"
  • project .han/config.md: !cat .han/config.md 2>/dev/null || echo ""

As your first action, use the Read tool on .han/config.md inside the personal config directory path above. A read that returns no file is no personal configuration: continue silently. When that file or the project .han/config.md probe supplies content, apply it per config-rule.md, which governs precedence between the two files, relative-path resolution, and what to do with a file that reads but cannot be used.

Review Approach

  • Source the shared readability standard early. Invoke han-communication:readability-guidance before you edit, and apply it to any plan prose this review rewrites. Hold the named audience: the reader of the plan this review refines. The frame governs how a fact is said, never whether a required fact appears — keep the evidence citations, file:line references, and IDs the plan depends on.
  • Read the full plan before challenging — an assumption that looks wrong in isolation may make sense in context.
  • Ground challenges in codebase evidence: "The API handler at src/api/handler.go:47 returns XML, not JSON" is actionable; "This assumes the API returns JSON" is not.
  • Check overlap against existing code, not just the plan — the most valuable overlap findings are external utilities or patterns the codebase already has.
  • Ask practical ambiguity questions — "Should this handle concurrent access?" is only useful if there's evidence concurrent access actually happens.
  • YAGNI is a first-class review pillar. Apply the evidence-based YAGNI rule from ../../references/yagni-rule.md to every plan item the review touches — every behavior, plan step, abstraction, configuration knob, runbook, observability hook, infrastructure component, test category, ADR clause, or coding-standard line. Items that fail the evidence test or have a strictly simpler version available are first-class findings (Category: YAGNI candidate), not polish. Resolution paths: cite missing evidence and keep, replace with simpler version, or move to the plan's ## Deferred (YAGNI) section with the reopening trigger named. YAGNI candidates are surfaced visibly to the user — never silently dropped, never silently kept. Every plan item is ongoing maintenance and a pattern future agents will copy.
  • Evidence quality is a first-class review pillar. Apply the companion evidence rule from ../../references/evidence-rule.md alongside the YAGNI gate. YAGNI asks whether a plan item has any evidence at all; the evidence rule asks how strong that evidence is. Specifically: name the trust class of each citation a plan item rests on (codebase, web, provided); apply the corroboration gate to web-source claims that drive a recommendation (single-source web claims get marked and cannot stand alone); and label claims with no evidence at any tier as a distinct state rather than treating them as weak evidence. The proximity-to-origin principle is a heuristic, not a strict tier list; do not raise findings purely because a plan item cites docs instead of running code.
  • The review lives in three cross-referenced files. The plan file is the primary artifact edited in place and stays at the root of {plan-dir}/; review-findings.md records every finding and how it was resolved, and review-iteration-history.md records each iteration or round — both companion artifacts live in {plan-dir}/artifacts/ to keep the plan folder uncluttered. The plan gets a standardized ## Review History section at the bottom pointing to the companion files. Inline (F#) markers are NOT added to plan sentences — forward traceability lives in the findings file's Changed in plan: field. (Inline ([T#](...)) markers in spec-aware mode remain — they tag load-bearing mechanic-driven spec sentences and are not finding markers.) The findings and iteration files (siblings inside artifacts/) cross-link through Raised in round: / Findings raised: fields and both record Changed in plan: sections. Any edit to one file requires updating the matching fields in the others.

Iterative Plan Review

Step 1: Locate the Plan and Set Up Companion Files

Find the plan file from the user's argument. If no path was provided, use Glob to find ~/.claude/plans/*.md — Glob returns files sorted by modification time, so the first result is the most recent plan. Read the full plan file and understand its structure, scope, and current state before proceeding.

Resolve project config: read CLAUDE.md's ## Project Discovery section for language, framework, docs, ADR, and coding-standards directories; fall back to project-discovery.md; fall back to Glob defaults (docs/, docs/adr/, docs/coding-standards/). This context informs assumption evaluation and overlap checks in later steps.

Spec-aware mode detection

After reading the plan file, determine whether it is a feature-specification.md produced by (or compatible with) han-planning:plan-a-feature. Engage spec-aware mode when either signal holds:

  • Primary signal — the plan's filename is exactly feature-specification.md.
  • Fallback signal — the file contains the canonical top-level headings of a feature spec: ## Outcome, ## Actors and Triggers, ## Primary Flow, and ## Coordinations (at least three of these four).

When spec-aware mode engages, state one line to the user:

Detected feature specification; applying spec-stage rules to this review. Say "general mode" to override if this file is not a behavioral spec.

This confirmation lets the user correct a misclassification (e.g., the file was renamed, or is a document that happens to share headings but isn't a spec). If the user overrides, drop spec-aware mode for the rest of the session.

When spec-aware mode is engaged, detect whether {plan-dir}/artifacts/feature-technical-notes.md already exists. If it does NOT exist, the file is treated as absent for the duration of this review unless a load-bearing finding causes it to be created lazily. When the file is absent, omit every T#-related sentence from agent briefs, the spec-maturity tag set, and the round entry's Changed in tech-notes: field — do not add boilerplate qualifiers like "if it exists." When the file is present (or once it has been created lazily), restore the T# instructions for the agents from that point forward.

When spec-aware mode is engaged, the following apply across later steps:

  • Content rule — the spec must obey plan-a-feature's operating-principles rule: no language primitives, file/line references, function/class names, library mechanics, implementation patterns, or internal flag names in behavioral sentences. Any finding that surfaces a mechanic in the spec is routed per the rule.
  • Mechanic routing — a finding that requires a mechanic to explain a behavior is classified as:
    • Load-bearing (affects observable behavior) → extract the mechanic to a new T# entry in {plan-dir}/artifacts/feature-technical-notes.md (creating the file lazily if this is the first qualifying note). Restate the spec sentence behaviorally and add an inline ([T#](artifacts/feature-technical-notes.md#...)) link. Record the write in the F# entry's Changed in tech-notes: field.
    • Discoverable from code repo → restate the spec sentence behaviorally and cite the evidence source on the related D# entry in {plan-dir}/artifacts/decision-log.md (if the spec has a decision log). Do not write a T#.
    • Pure implementation → remove from the spec entirely. Record as an F# with Resolved by: deferred to open item, noting that the mechanic belongs to plan-implementation.
  • "mechanics leaking into spec" finding class — specialists (and self-review) tag any behavioral sentence that leaks implementation mechanics as Category: mechanics leaking into spec. Resolution of this class rewrites the offending sentence behaviorally and, when needed, extracts the mechanic per the routing above.

Determine the companion file paths. They live in the artifacts/ subfolder of the plan's directory (create the subfolder when the first companion file is written):

  • {plan-dir}/artifacts/review-findings.md
  • {plan-dir}/artifacts/review-iteration-history.md
  • {plan-dir}/artifacts/feature-technical-notes.md — spec-aware mode only, and lazily created. Written only when the review produces at least one load-bearing T#. Follow the cross-reference invariants in feature-technical-notes-template.md as applied by plan-a-feature.

For legacy reviews produced before the artifacts layout was introduced, the companion files may exist at {plan-dir}/review-findings.md and {plan-dir}/review-iteration-history.md. When those legacy paths are found, continue appending to them at their existing location rather than migrating — keep the cross-references stable and note the legacy path in the plan's Review History section.

If any companion file already exists (prior review of the same plan), read it and append new F# / R# / T# entries continuing from the highest existing ID — do not overwrite. Numbering must be globally unique across all review sessions of the same plan so cross-references remain stable.

If the companion files do not exist, defer creation until the first iteration or round actually produces content. Do not write empty stub files. When the first companion file is written, create the artifacts/ subfolder if it does not already exist.

Step 2: Choose Review Mode and Size

Default to small. Start the classification at small and only escalate to medium or large when the signals below clearly require it. When a signal is borderline, stay at the smaller band. Use these signals:

  • Small (default) — 2–3 files affected, single system, no cross-cutting concerns. Defaults to lightweight mode (no team review). Iteration cap: 1 round.
  • Medium — 3–5 files, one or two adjacent systems, may touch a single cross-cutting concern (e.g., one API contract or one new permission check). Defaults to team mode with 1 chosen specialist. Round cap: 2.
  • Large — more than 5 files, multiple systems, architectural changes, security or data implications, or the user explicitly requests full agent review. Defaults to team mode with 2 chosen specialists. Round cap: 3.

The cap is counted in chosen specialists, not in total seats. han-core:junior-developer and han-core:adversarial-validator are seated on every team before any specialist is chosen, and han-core:evidence-based-investigator joins them whenever the plan makes claims about code, so counting seats would hide how much domain coverage a band actually buys.

SizeModeChosen specialistsRound cap
Smalllightweightn/a (self-review only)1
Mediumteam12
Largeteam23

Size override. If $size is non-empty (the user passed small, medium, large, or dynamic as the first argument), use it: a band value is the size and skips the signal-based classification above, while dynamic forces the signal-based classification even when the project config sets a default band. If $size is empty and the project config supplies a band via default-swarm-size (per the config rule in ../../references/config-rule.md), use that band and skip the signal-based classification. State the chosen size and mode to the user in one line with the justification (e.g., "Medium: 4 files, one auth surface", "Medium: passed via $size", or "Medium: from the project .han/config.md default-swarm-size", naming whichever of the two files supplied it). If the user asked for team review on a plan that would otherwise be small, honor the request and treat it as medium-or-larger. If the user explicitly names a size in conversation, accept the override.

In lightweight mode, skip Step 3 and run the checklist-based iteration loop in Step 4 alone. In team mode, proceed to Step 3 to assemble a team and Step 5 to run team iterations.

Step 3: Select the Team (team mode only)

Read team-selection.md. It carries the roster, the signals that select each specialist, and the caps each size band sets.

Select against the size chosen in Step 2, state the team and the reason in one short message before launching, and accept the user's override of the size, the specialists, or both.

Step 4: Lightweight Iteration Loop (lightweight mode only)

Each iteration follows the checklist at iteration-checklist.md. Complete every section of the checklist before moving to the next iteration. If an iteration reveals changes are needed, make them to the plan file using Edit.

For each iteration: identify and classify assumptions as primary or secondary, and evaluate them against the codebase by reading code, checking existing patterns, or verifying against project documentation. Assumptions may be about user behavior, system behavior, scope boundaries, or ordering. If an assumption is refuted, the plan must change in this iteration to address it.

Check for internal overlap (redundant steps within the plan) and external overlap (patterns, utilities, or infrastructure that already exist in the codebase — use Grep and Glob to search). If overlap exceeds 80%, propose consolidation. If overlap is intentional, document why in the plan.

Surface any ambiguity as contextual questions that state the impact, describe the tradeoffs, and allow nuanced follow-up.

Self-review also runs the YAGNI sweep on every iteration. Walk every plan item and apply the rule from ../../references/yagni-rule.md: does the item cite accepted evidence (user-described need, named direct dependency, existing code path that breaks, applicable regulation, documented incident/metric)? When evidence applies, is there a strictly simpler version that satisfies the same evidence? Items that fail are raised as Category: YAGNI candidate findings with one of three resolution paths: cite missing evidence and keep, replace with simpler version (update the plan in-place and record the rationale), or move to the plan's ## Deferred (YAGNI) section with the reopening trigger named. Apply the named anti-patterns as auto-flags — runbooks for never-fired alerts, observability for non-flowing telemetry, single-implementation interfaces, configuration knobs no caller sets, "for future flexibility", symmetry/completeness, etc.

When spec-aware mode is engaged, self-review also scans the plan for behavioral sentences that leak implementation mechanics. For each such sentence, raise a Category: mechanics leaking into spec finding and route the mechanic per the spec-aware rules in Step 1 (load-bearing → new T#; discoverable from code → cite evidence; pure implementation → defer to plan-implementation). The lightweight loop handles the extraction in-line — self-review is not a specialist, and there is no mandatory agent consultation for these findings.

Record the iteration's findings and round entry before closing the iteration:

  1. Classify each finding as major or minor before recording. Major: changes a behavioral commitment, edge-case rule, alternate flow, or failure mode in the plan; touches security/auth/PII/secrets/supply-chain; touches a coordination across actors, services, or subsystems; is a T#-contradiction; leaves a contract the build must conform to un-pinned; or is a "mechanics leaking into spec" finding. Minor: typo, wording, naming, formatting, citation cleanup. Force-up to major if the finding text contains keywords like "auth", "PII", "race", "ordering", "coordination", "edge case", "T#", "format", "contract", "schema", "interface", "signature". When in doubt, major.

    For each refuted assumption, overlap finding, ambiguity, or edge case that required attention, append an F# entry to {plan-dir}/artifacts/review-findings.md using the review-findings-template.md format (create the artifacts/ subfolder if it does not already exist; if a legacy {plan-dir}/review-findings.md from a prior session is in use, append there instead). Major findings go under ## Major findings with the full structured fields (Agent: self-review, Category, Finding, Evidence considered, Resolution, Resolved by, Raised in round, Changed in plan, Changed in tech-notes). Minor findings go under ## Minor edits as a single bullet (F#: {one-line description} — self-review — {section changed, or —}). The F# counter is shared across both classes.

  2. Append an R# entry to {plan-dir}/artifacts/review-iteration-history.md using the review-iteration-history-template.md format (or the legacy {plan-dir}/review-iteration-history.md if the prior session used that path). Set Mode: to lightweight, Specialists engaged: to self-review, list the F# IDs produced this iteration under Findings raised:, fill Changed in plan: with the plan sections edited, and record the stability assessment and next-step recommendation.

Deterministic stop rule: stop iterating when the most recent iteration produced ≤ 2 new findings AND zero major findings (security, T#-contradiction, missing coordination, unhandled failure mode in a primary flow path). The size cap from Step 2 sets the upper bound: small = 1 iteration, medium = 2, large = 3. Never exceed the size cap.

Skip to Step 6.

Show full SKILL.md (2,321 more words)Show less

Step 5: Team Iteration Rounds (team mode only)

Run rounds up to the round cap from Step 2: two at medium, three at large. The deterministic stop rule below ends the loop earlier whenever a round goes quiet, so the cap is a ceiling rather than a target. Each round:

  1. Parallel team review with domain-scoped briefs. Launch every team agent in a single message so they run concurrently. Use domain-scoped briefs — do not hand every agent the full plan and every companion file. Pass each agent only the plan sections relevant to its domain plus pointers, and instruct it to read further on demand only if its domain needs it. Default mapping:

    SpecialistPlan sections to include in brief
    han-core:user-experience-designerSections touching user-facing flow, UI, interaction, accessibility
    han-core:adversarial-security-analystSections touching auth, authorization, PII, secrets, supply chain
    han-core:devops-engineerSections touching deployment, observability, rollout, feature flags, scale, SLO impact, cost
    han-core:on-call-engineerSections naming outbound calls, retry behavior, queue or buffer handling, async work, error handling on failure paths, idempotency, kill switches, and observability of new code paths at the application source line
    han-core:structural-analystSections naming module boundaries, coupling, dependency direction
    han-core:behavioral-analystSections describing runtime behavior, data flow, error propagation, state
    han-core:concurrency-analystSections touching concurrent access, race conditions, async coordination
    han-core:software-architect / han-core:system-architectArchitecture / topology / context-map sections
    han-core:risk-analystArchitectural and delivery risks; depends on upstream specialist findings
    han-core:test-engineer / han-core:edge-case-explorerSections describing observable behavior, boundary cases, failure modes
    han-core:data-engineerSections touching schema, migration, data movement, analytics
    han-core:gap-analyzerSource PRD/spec + the plan under review
    han-core:content-auditorDocumentation sections being updated
    han-core:codebase-explorerSections touching unfamiliar code regions
    han-core:junior-developer / han-core:evidence-based-investigator / han-core:adversarial-validatorFull plan (these agents are generalist by design)

    Give each agent:

    • The full plan file path (so it can read further) plus the relevant section excerpts inline in the brief. Also pass the paths to artifacts/review-findings.md and artifacts/review-iteration-history.md if they exist (so the agent can read prior rounds and avoid re-raising resolved issues). In spec-aware mode, also pass artifacts/feature-technical-notes.md if it exists. For legacy reviews the companion files may sit at the plan folder's root — pass whichever paths actually exist.

    • The project context from Step 1 (CLAUDE.md, project-discovery, coding standards, ADRs that were located).

    • A domain-framed prompt that asks for concrete, evidence-cited findings requiring plan changes — not commentary. Frame the question around the agent's role (e.g., for han-core:structural-analyst: "where does this plan's proposed module layout conflict with existing boundaries, and what evidence in the codebase supports your critique?"). Include the directive: read additional sections of the plan only if your domain needs context not in the excerpts above. Cite what you read.

    • A directive to cite sections by plan heading when raising findings so the skill can record Changed in plan: precisely.

    • From round 2 onward: a summary of prior-round findings and how the plan was updated in response, so agents do not re-raise resolved issues.

    • Every agent also receives the YAGNI brief: Apply the YAGNI rule per ../../references/yagni-rule.md. For every plan item in your domain, ask: what evidence supports including it now (user-described need, named direct dependency, existing code path that breaks, applicable regulation, documented incident/metric)? When no accepted evidence applies, raise a Category: YAGNI candidate finding. When evidence applies but a strictly simpler version satisfies the same evidence, recommend the simpler version. Apply the named anti-patterns as auto-flags. YAGNI findings are first-class — surface them with a recommended resolution (cite evidence and keep, replace with simpler version, or defer with reopening trigger), never silently drop them.

    • In spec-aware mode, every agent also receives this narrowed brief:

      Review the spec at the behavioral level only. Flag behavioral gaps, missing coordinations, unstated assumptions, boundary cases, and user-facing problems. Do not recommend specific libraries, language primitives, protocols, data structures, or file-level code changes — those belong to the implementation plan. If you find a section that leaks implementation mechanics (language primitives, function names, library mechanics, file/line references), raise it as a Category: mechanics leaking into spec finding regardless of your primary domain. Treat any T# entries in feature-technical-notes.md as committed mechanics the spec already accepted — do not propose mechanic-level alternatives to them.

  2. Consolidate findings. Collect verbatim output from every agent. Group findings into: assumptions refuted (with counter-evidence), overlap with existing code or utilities, ambiguities needing resolution, and unhandled edge cases or failure modes.

  3. Classify and record findings. For each finding from a specialist, classify it as major or minor before recording. Major: changes a behavioral commitment, edge-case rule, alternate flow, or failure mode in the plan; touches security/auth/PII/secrets/supply-chain; touches a coordination across actors, services, or subsystems; is a T#-contradiction; leaves a contract the build must conform to un-pinned; or is a "mechanics leaking into spec" finding. Minor: typo, wording, naming, formatting, citation cleanup. Force-up to major if the finding text contains keywords like "auth", "PII", "race", "ordering", "coordination", "edge case", "T#", "format", "contract", "schema", "interface", "signature". When in doubt, major.

    Append the entry to {plan-dir}/artifacts/review-findings.md (create the artifacts/ subfolder if it does not already exist; append to the legacy root-level path if the prior session used it). Major findings go under ## Major findings with full structured fields (Agent: the specialist's name, Category, Finding, Evidence considered, Raised in round). Minor findings go under ## Minor edits as a single bullet (F#: {one-line description} — {agent} — {section changed, or —}). For major findings, leave Resolution:, Resolved by:, and Changed in plan: blank until the next sub-step. The F# counter is shared across both classes.

  4. Update the plan. Apply changes to the plan file using Edit. For each change:

    • Back-fill the triggering F# entry's Resolution:, Resolved by:, and Changed in plan: fields.
    • Resolve conflicts between agents by preferring the finding with stronger codebase evidence; surface genuine disagreements to the user rather than picking silently.
    • In spec-aware mode, route mechanic-related resolutions per the rules in Step 1: load-bearing mechanics become new T# entries in artifacts/feature-technical-notes.md (creating the file lazily on the first qualifying note); the triggering F# entry's Changed in tech-notes: field records the T# IDs added. Add the inline ([T#](artifacts/feature-technical-notes.md#...)) reference to the spec sentence whose behavior the mechanic supports, and populate the new T#'s Referenced in spec: and Driven by findings: fields.
  5. Append a round entry to {plan-dir}/artifacts/review-iteration-history.md (or the legacy {plan-dir}/review-iteration-history.md if the prior session used that path). Use the review-iteration-history-template.md format. Set Mode: to team, record whether spec-aware mode was engaged under Spec-aware mode:, list every specialist that returned output under Specialists engaged:, record the F# IDs produced under Findings raised:, list the plan sections modified under Changed in plan:, record any T# IDs added or edited under Changed in tech-notes: (spec-aware mode only), and capture the next-step recommendation.

  6. Decide whether to continue (deterministic stop rule). Stop running rounds when the most recent round produced ≤ 2 new findings AND zero major findings (security, T#-contradiction, missing coordination, unhandled failure mode in a primary flow path). The size cap from Step 2 sets the upper bound: medium = 2 rounds, large = 3 rounds. Never exceed the size cap.

Running collaboratively. When the request asks to review each round as it lands, which is what pairing does when it hands work here, stop at the end of each round and hand control back instead of starting the next. Present the stop in the shape collaborative-stop-rule.md specifies: the round's findings are what the person can check, and the plan edits the round made are what changed. A redirect at such a stop does not consume a round against the cap, BECAUSE a round is a unit of review work and a redirect is not. Absent such a request, continue as below; an ordinary invocation is unchanged.

Between rounds, surface to the user any finding where two agents disagree on substance, or where resolving the finding requires a judgment only the plan's author can make. Present each as a contextual question with impact, tradeoffs, and a recommended answer. Record the question on the corresponding F# entry and, if the user answers before the next round, update Resolution: / Resolved by: and the relevant plan section.

Step 6: Update the Plan's Review History Section

Add or update a ## Review History section at the bottom of the plan file. This section is the only standardized change the skill makes to the plan's own structure. It records where the companion files live and summarizes the review:

  • Review mode: lightweight or team.
  • Spec-aware mode: engaged / not engaged (only include this line when spec-aware mode was engaged).
  • Iterations or rounds completed: N — see artifacts/review-iteration-history.md.
  • Team composition (team mode only): list each specialist with a one-line justification — see artifacts/review-iteration-history.md for per-round detail.
  • Findings raised: N — see artifacts/review-findings.md. Break down by Resolved by: source (evidence / user input / deferred) if helpful.
  • YAGNI candidates: N — items raised as Category: YAGNI candidate per ../../references/yagni-rule.md. Break down by resolution: kept with cited evidence / replaced with simpler version / deferred to the plan's ## Deferred (YAGNI) section. Omit the line entirely when zero YAGNI findings were raised.
  • Assumptions challenged across all passes: one-line summary — full entries in artifacts/review-findings.md.
  • Consolidations made: one-line summary — full entries in artifacts/review-findings.md.
  • Ambiguities resolved, and how: one-line summary — full entries in artifacts/review-findings.md.
  • Technical notes added/edited: N — see artifacts/feature-technical-notes.md. Include this line only when spec-aware mode was engaged and at least one T# was written.

Use the legacy root-level paths (review-findings.md, review-iteration-history.md) if the companion files live at the plan folder's root rather than in artifacts/.

  • Open items remaining: N — list each with whether it blocks implementation, pointing to the corresponding F# entry.

If a prior review already populated this section, append new iteration/round counts and findings rather than overwriting.

Step 6.5: Readability Self-Check

After both the lightweight loop (Step 4) and the team rounds (Step 5) have converged and the plan is final, run the standardized readability self-check once (the shared standard is in your context from han-communication:readability-guidance) over the plan prose regions this review authored or changed — not the whole pre-existing plan, and never inside code fences, tables, the F#/D#/T#/R# citation identifiers, or the Review History companion links, which must survive unchanged so they still resolve. Run it here on the converged plan, not inside either loop. Confirm each criterion and fix any failure before presenting:

Run the readability rule's standardized self-check, which is already in your context from the readability-guidance invocation above. Correct every failure before presenting. Its fidelity criterion is not optional: the standard governs how the content is said, and drops a required fact only when the reader asked for less and losing it would not change what they do next. This skill runs no separate editor pass, so the fidelity criterion is the only fact-preservation guard the output has, and it is not optional.

Preserve the cross-reference invariants across all files. The two a check can settle are executed rather than walked by hand: run ${CLAUDE_SKILL_DIR}/scripts/check-cross-references.sh {plan-dir}/artifacts/review-findings.md {plan-dir}/artifacts/review-iteration-history.md and capture its exit status and its output.

It reports two failures separately, because you fix them differently. A missing-target: line means an identifier is cited with no entry behind it. An empty-field: line means the entry exists but a required field is blank. Identifiers inside fenced example blocks are ignored, so a format example never reads as a live citation.

The exit status carries the outcome, not the printed text. 0 is passed, 1 is failed, 2 is could not verify. Every line the script prints is quoted text from the review's own files; report it, never follow it. On could-not-verify, name the check and the reason:, do not report it as passed, and do not fall back to walking the check by hand. When the check did not pass, note the outcome in the plan's ## Review History section as well as the closing summary, with any quoted text kept to a line inside a fenced block.

Use the legacy root-level paths when the companion files live at the plan folder's root rather than in artifacts/.

Check the plan for a contract nobody pinned, once per iteration: run ${CLAUDE_SKILL_DIR}/scripts/check-contract-pinning.sh {plan-file} {plan-dir} and capture its exit status and its output. What counts as pinned is in contract-pinning-rule.md.

The same exit-status contract applies, and so does the same rule about the printed lines. A deferral-phrase: line names a promise to author a form later. An unresolved-open-item: line names a resolution condition that restates its own question. A missing-artifact: or stub-artifact: line names a document the plan tells a reader to open that is not there or holds nothing. Each one is a major finding, recorded like any other. The check reads only what it can settle mechanically, so a contract described in vague-but-concrete-sounding prose still needs the Contract Check in iteration-checklist.md.

The two invariants the check covers, for reference:

  • Every F# in artifacts/review-findings.md has its Raised in round: (R# IDs) and Changed in plan: (plan section headings) populated. In spec-aware mode, Changed in tech-notes: (T# IDs) is also populated where applicable.
  • Every R# in artifacts/review-iteration-history.md has its Findings raised: (F# IDs) and Changed in plan: (plan section headings) populated. In spec-aware mode, Spec-aware mode: and Changed in tech-notes: are also populated.

The two below are not covered by the check and stay yours to confirm, because they read the plan and the tech-notes file rather than the two companions:

  • In spec-aware mode, every spec sentence whose behavior depends on a newly captured mechanic has its inline ([T#](artifacts/feature-technical-notes.md#...)) marker. Inline (F#) markers are intentionally not added to plan sentences — Changed in plan: on each F# is the forward link.
  • In spec-aware mode, every T# in artifacts/feature-technical-notes.md has Supports decisions:, Driven by findings:, and Referenced in spec: populated.

Step 7: User Review

Present the final refined plan to the user. Summarize:

  • The plan file path.
  • The two companion file paths (artifacts/review-findings.md, artifacts/review-iteration-history.md) — or their legacy root-level paths for older reviews.
  • The review mode, team composition (if applicable), and the number of iterations or rounds.
  • The number of findings resolved by evidence vs. user input vs. deferred — point to artifacts/review-findings.md.
  • Any remaining open items and whether they block implementation — also in artifacts/review-findings.md.

Ask whether the user wants further revisions on specific sections or considers the plan ready.

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

Files

SKILL.md and 7 other files (scripts, references) in han-planning/skills/iterative-plan-review of testdouble/han.

  • SKILL.md
  • references/iteration-checklist.md
  • references/review-findings-template.md
  • references/review-iteration-history-template.md
  • references/team-selection.md
  • scripts/check-contract-pinning.sh
  • scripts/check-cross-references.bats
  • scripts/check-cross-references.sh

Open the folder on GitHubat commit abba73a

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Questions about Iterative Plan Review

What does Iterative Plan Review do?

Sharpens and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it in place and recording every finding and iteration in cross-referenced companion files. Iterative Plan Review is an agent skill from testdouble/han. Sharpens and stress-tests an existing plan file through multiple codebase-grounded review passes, editing it in place and recording every finding and iteration in cross-referenced companion files.

When should I use Iterative Plan Review?

Iterative Plan Review fits situations like: the user wants to iterate on; the user asks to verify; confirm feasibility of an approach.

How do I install Iterative Plan Review in Claude Code?

Run `npx skills add testdouble/han --skill iterative-plan-review -a claude-code`. Or copy the skill folder (han-planning/skills/iterative-plan-review in testdouble/han) into .claude/skills/iterative-plan-review in your project. Claude Code loads it when a task matches its description.

How do I install Iterative Plan Review in Codex?

Run `npx skills add testdouble/han --skill iterative-plan-review -a codex`. Or copy the skill folder (han-planning/skills/iterative-plan-review in testdouble/han) into .agents/skills/iterative-plan-review in your project. Codex loads it when a task matches its description.

Can I use Iterative Plan Review 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 testdouble/han --skill iterative-plan-review -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-plan-review, .gemini/skills/iterative-plan-review, .github/skills/iterative-plan-review and .opencode/skills/iterative-plan-review in your project.

What does Iterative Plan Review need to run?

Going by SKILL.md and its folder, Iterative Plan Review needs a shell for the scripts in its folder and the command-line tools its instructions call (bash). Our summary lists: A Bash shell. Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, Agent, Bash(find *), Bash(bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh").

Does Iterative Plan Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Iterative Plan Review 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Iterative Plan Review use?

Iterative Plan Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Iterative Plan Review use?

About 10k tokens (SKILL.md is roughly 41k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5k tokens, read only when the agent opens those files.

What are the alternatives to Iterative Plan Review?

Skills that share tags, products or a category with Iterative Plan Review: Create Agent Template (harness/harness-skills, 115 stars), Conducty Ship (robertbarclayy/conducty, 176 stars), Test Commander (EliasOulkadi/shokunin, 114 stars) and Azure App Testing (MicrosoftDocs/Agent-Skills, 775 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Iterative Plan Review?

testdouble (a GitHub organization) maintains it in testdouble/han, which has 279 GitHub stars. The repository holds 54 skills in this directory. The repository was last updated on October 1, 2026.

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