planning-with-files skill

by OthmanAdi·OthmanAdi/planning-with-files·27k stars·MIT

Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Kiro skill instructions and steering state read selected project planning context. Recovery reads project planning files and their timestamps only, not agent transcript stores. This adapter registers no Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

A100/100content scan

Is the planning-with-files skill safe?

Clean: nothing in its files matched our rules. We read 13 files in the folder on 2026-09-28.

No findings.

Install the planning-with-files skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/OthmanAdi/planning-with-files.git /tmp/planning-with-files
mkdir -p ~/.claude/skills
cp -r /tmp/planning-with-files/.kiro/skills/planning-with-files ~/.claude/skills/planning-with-files

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Planning with Files (Kiro)

Work like Manus: use persistent markdown as your working memory on disk while the model context behaves like volatile RAM. Deep background: references/manus-principles.md.

Kiro complements this with:

Agent Skills (this file) — progressive disclosure when the task matches the description.
Steering — after bootstrap, .kiro/steering/planning-context.md uses inclusion: auto and #[[file:.kiro/plan/…]] live references (Steering docs).

Hooks are not bundled: project-level hooks affect every chat in the workspace. Prefer this skill + steering + the reminder block below.

STEP 0 — Bootstrap (once per workspace)

From the workspace root:

sh .kiro/skills/planning-with-files/assets/scripts/bootstrap.sh

Windows (PowerShell):

pwsh -ExecutionPolicy RemoteSigned -File .kiro/skills/planning-with-files/assets/scripts/bootstrap.ps1

Creates:

.kiro/plan/task_plan.md, findings.md, progress.md
.kiro/steering/planning-context.md (auto + #[[file:.kiro/plan/…]])

Idempotent: existing files are not overwritten.

Import as a workspace skill (optional): Kiro → Agent Steering & Skills → Import a skill → choose this planning-with-files folder (Skills docs).

STEP 1 — Persistent reminder (after skill activation)

Append the following block to the end of your reply, and repeat it at the end of subsequent replies while this planning session is active:

[Planning Active] Before each turn, read .kiro/plan/task_plan.md and .kiro/plan/progress.md to restore context.

STEP 2 — Read plan every turn (while active)

Read .kiro/plan/task_plan.md — goal, phases, status
Read .kiro/plan/progress.md — recent actions
Use .kiro/plan/findings.md for research and decisions

If .kiro/plan/ is missing, run STEP 0.

STEP 3: Project-file catchup (after a long gap or suspected drift)

Summaries and planning-file mtimes (compare with git diff --stat if needed). This helper does not read Kiro or other agent transcript stores:

$(command -v python3 || command -v python) \
  .kiro/skills/planning-with-files/assets/scripts/session-catchup.py "$(pwd)"

Windows:

python .kiro/skills/planning-with-files/assets/scripts/session-catchup.py (Get-Location)

Then reconcile planning files with the actual codebase.

Optional — Phase checklist

From workspace root (defaults to .kiro/plan/task_plan.md):

sh .kiro/skills/planning-with-files/assets/scripts/check-complete.sh

pwsh -File .kiro/skills/planning-with-files/assets/scripts/check-complete.ps1

The Core Pattern

Context Window = RAM (volatile, limited)
Filesystem = Disk (persistent, unlimited)

→ Anything important gets written to disk.

File Purposes

Critical Rules

1. Create Plan First

Never start a complex task without task_plan.md. Non-negotiable.

2. The 2-Action Rule

"After every 2 view/browser/search operations, IMMEDIATELY save key findings to text files."

This prevents visual/multimodal information from being lost.

3. Read Before Decide

Before major decisions, read the plan file. This keeps goals in your attention window.

4. Update After Act

After completing any phase:

Mark phase status: in_progress → complete
Log any errors encountered
Note files created/modified

5. Log ALL Errors

Every error goes in the plan file. This builds knowledge and prevents repetition.

6. Never Repeat Failures

if action_failed:
    next_action != same_action

Track what you tried. Mutate the approach.

7. Continue After Completion

When all phases are done but the user requests additional work:

Add new phases to task_plan.md (e.g., Phase 6, Phase 7)
Log a new session entry in progress.md
Continue the planning workflow as normal

The 3-Strike Error Protocol

ATTEMPT 1: Diagnose & Fix
  → Read error carefully
  → Identify root cause
  → Apply targeted fix

ATTEMPT 2: Alternative Approach
  → Same error? Try different method
  → Different tool? Different library?
  → NEVER repeat exact same failing action

ATTEMPT 3: Broader Rethink
  → Question assumptions
  → Search for solutions
  → Consider updating the plan

AFTER 3 FAILURES: Escalate to User
  → Explain what you tried
  → Share the specific error
  → Ask for guidance

Read vs Write Decision Matrix

Scripts

Kiro keeps its plan in .kiro/plan/. The canonical set-active-plan listing helper reads named plans under .planning/ and does not list or switch this Kiro plan.

Helper scripts (under assets/scripts/):

assets/scripts/bootstrap.sh — Idempotent workspace bootstrap. Creates .kiro/plan/ and .kiro/steering/planning-context.md.
assets/scripts/session-catchup.py: Reports Kiro planning-file timestamps and summaries. It does not read agent transcript stores.
assets/scripts/check-complete.sh -- Verify all phases in the active plan are complete.

Advanced Topics

More skills from OthmanAdi/planning-with-files

Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; agent instructions read selected project planning context when invoked. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. This adapter registers no lifecycle or Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Gemini lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. The session-end hook reports status only; it does not request continuation or run commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
Aplanning-with-filesPersistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

All agent skills → · MCP servers