Os evolution planner

Role

os-evolution-planner transforms an evolution goal into a structured task plan and a Copilot CLI delegation prompt that can be dispatched in one premium request. Before writing the plan it generates 2-3 approach options using the cheapest available model, so the best path is chosen before spending premium tokens on a full plan.

Inputs

Input	How provided	Default
Target plugin	argument or interview question	required
Target skill or agent	argument or interview question	"all" (full plugin audit)
Evolution goal	argument or interview question	required
Auto-detect gaps	flag	true
Dispatch immediately	flag	false (present for human review)

---

Phase 0 — Read Environment Profile

Before doing anything else, check context/memory/environment.md:

• If it exists, read the ## Delegation Strategy section to determine:
  • Brainstorm model (cheapest available)
  • Dispatch backend (Copilot CLI or Claude subagent)
• If it does not exist, default to Claude-only mode and note:

  "No environment profile found — defaulting to Claude-only. Run os-environment-probe to unlock low-cost Copilot or Agy brainstorming."

---

Phase 1 — Brainstorm Options (cheap model)

Do this before gap detection and before writing any plan.

Using the cheapest available model, generate 2-3 distinct approaches to the evolution goal. Each approach sketch is ~3-5 sentences: what it does, what it doesn't do, estimated effort, key tradeoff.

Model selection (use first that is available per environment profile):

Consult references/cheapest_models.md for current model names and costs.

Priority	Engine	How to invoke
1	Copilot CLI (gpt-5-mini)	run_agent.py --cli copilot
2	Agy CLI (gemini-3.5-flash)	run_agent.py --cli agy
3	Claude Haiku subagent	Spawn Agent(subagent_type="haiku", prompt=...)

Brainstorm prompt template:

Evolution goal: <goal>
Target: <target skill/agent>
Current gaps detected: <gap list>

Generate exactly 3 distinct approaches to this evolution goal. For each:
- Approach name (2-4 words)
- What it does (2-3 sentences)
- What it trades off (1 sentence)
- Estimated effort: [Small / Medium / Large]

Present options to user:

Here are 3 approaches to <goal>:

**Option A — <name>** [<effort>]
<description>
Tradeoff: <tradeoff>

**Option B — <name>** [<effort>]
<description>
Tradeoff: <tradeoff>

**Option C — <name>** [<effort>]
<description>
Tradeoff: <tradeoff>

My recommendation: **Option <X>** — <one-line reason>.
Which would you like to proceed with? (A / B / C / modify)

Wait for the user to select before proceeding to Phase 2.

---

Phase 2 — Gap Detection Lens

Once the approach is confirmed, read the target files and check for each gap below. Each confirmed gap becomes one workstream:

Check	Gap if...	Workstream type
## Gotchas section	absent from SKILL.md or agent file	Add Gotchas (3–5 field-derived patterns)
## HANDOFF_BLOCK in completion	absent from child skill completion section	Add HANDOFF_BLOCK code fence
evals.json	stub (< 6 cases) or REPLACE placeholders	Fill with real routing cases
Model identifiers	contain dashes (claude-sonnet-4-6)	Fix to dot notation
Domain patterns layer	references/domain-patterns/ absent	Create README + first pattern file
## Smoke Test	absent from SKILL.md	Add with 2–3 acceptance criteria
Session hook	hooks/session_end.py absent	Create session-end hook
Script security	--dangerously-skip-permissions unconditional	Add --tier flag

---

Phase 3 — Output Format

Task plan written to tasks/todo/<YYYY-MM-DD>-<slug>-plan.md:

# <task-number> — <title>

## Context
[What triggered this evolution, what was found]

## Approach Selected
Option <X> — <name>: <one-line description of chosen approach and why>
(Options considered: <A>, <B>, <C> — see brainstorm output for tradeoffs)

## Gaps Identified
[One bullet per gap found by the detection lens]

## Workstreams
| WS | Scope | Delegate to |
...

**WS ordering rule**: Structural fixes (model identifiers, path bugs, security flags) MUST
be listed as the first workstreams. Additive content (Gotchas, HANDOFF_BLOCK, domain
patterns, smoke tests) comes after. The delegated agent executes workstreams in listed order.

## Delegation Plan
1. Delegation prompt at tasks/todo/copilot_prompt_<slug>.md
2. Dispatch via run_agent.py with claude-sonnet-4.6
3. Review output (diff, symlink audit)
4. Commit and PR

## Status
- [ ] WS-A ...

Delegation prompt written to tasks/todo/copilot_prompt_<slug>.md:

• One section per workstream with exact file paths and content specifications — listed in order: structural fixes first, then additive content
• Global instruction: "Use the Write tool to write files directly — do not output delimiters"
• Completion checklist section at the end with COMPLETION_REPORT format

---

If the --dispatch flag is set (or the user confirms dispatch), run the heartbeat then dispatch:

Step 4 — Dispatch via copilot-cli-agent skill

Invoke the copilot-cli-agent skill with the following parameters:

1. Heartbeat check (always first):

   • prompt_file: /dev/null
   • context: /dev/null
   • output: temp/heartbeat_<slug>.md
   • instruction: "HEARTBEAT CHECK: Respond HEARTBEAT_OK only."
   • model: gpt-5-mini

   Verify heartbeat before premium dispatch:

   grep -q "HEARTBEAT_OK" temp/heartbeat_<slug>.md || (echo "HEARTBEAT FAIL — aborting dispatch" && exit 1)
   

2. Main Dispatch:

   • prompt_file: tasks/todo/copilot_prompt_<slug>.md
   • context: /dev/null
   • output: temp/copilot_output_<slug>.md
   • instruction: "Generate all files exactly as specified. Use the Write tool to write files directly."
   • model: claude-sonnet-4.6
   • mode: non-interactive

   After dispatch, verify output before claiming complete:

   wc -l temp/copilot_output_<slug>.md  # expect 100+ lines for multi-workstream output
   test -s temp/copilot_output_<slug>.md || echo "ERROR: empty output — copilot-cli-agent dispatch failed"
   

After dispatch completes (or after plan is written if dispatch is off), log to experiment log:

python3 plugins/agent-agentic-os/scripts/experiment_log.py append \
  --source-type planner \
  --report tasks/todo/<slug>-plan.md \
  --session-id "<slug>" \
  --target "<target-skill-or-agent>" \
  --triggered-by os-evolution-planner

This records the workstream count and gaps identified as a qualitative entry in context/experiment-log/ — traceable alongside any subsequent verifier or tester runs.

If dispatch flag is NOT set, present the plan and prompt paths and ask:

"Plan written to tasks/todo/<slug>-plan.md and delegation prompt to tasks/todo/copilot_prompt_<slug>.md. Dispatch to Copilot CLI now? (yes / review first)"

---

Integration with os-architect

os-architect calls this skill when:

• Path B (Update): a capability exists but has gaps — pass the target + list of gaps
• Path C (Create): a new skill/agent is being built — pass the target name + goal description

os-architect provides the intent classification and gap audit as context. This skill runs Phase 0 (environment check), Phase 1 (option brainstorm), presents options for user selection, then proceeds to gap detection and plan writing for the confirmed approach.

---

Gotchas

• Always brainstorm before planning: Even if the "right" approach seems obvious, running Phase 1 costs near-zero tokens and frequently surfaces a simpler or more composable alternative the first instinct missed.
• Brainstorm prompt must include current gaps: Without gap context, the cheap model produces generic approaches. Pass the full gap list from Phase 2 detection into the brainstorm prompt.
• Gap detection reads source files, not installed .agents/ files: Always read from plugins/<plugin>/skills/<skill>/SKILL.md — the installed .agents/ copy may be stale.
• Workstream order matters: Model identifier fixes (WS-A type) must come before Gotchas sections (WS-B type) — otherwise the Gotchas section may embed incorrect model identifiers.
• Delegation prompt must be self-contained: The Copilot CLI agent has no memory of this session. Every workstream spec must include enough context to be executed cold — file paths, exact content, insertion points.
• Dispatch flag off by default: Do not auto-dispatch without user confirmation. A bad prompt dispatched immediately produces a bad output with no checkpoint.

Smoke Test

1. Given target = os-eval-runner skill, Phase 1 produces 3 named approaches with effort estimates before any plan file is written.
2. After user selects an option, the plan file includes an "Approach Selected" section naming the chosen option and noting alternatives considered.
3. Given --dispatch flag set and heartbeat passes, the skill calls run_agent.py with claude-sonnet-4.6 and verifies output line count before reporting complete.