Skip to main content

harness-planner

Select the next task for an autonomous continuation run and write the plan artifact. Use when acting as the Planner persona, or when deciding what the repository should work on next from vision, category balance, and the dependency graph.

Quellinformationen

Repository
arvindrk/deep-research-agents
Letzte Quellaktivität
8. August 2026 um 11:53
Erkannte Sprache von SKILL.md
Englisch
Sterne
6
Forks
1

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
harness-planner
description
Select the next task for an autonomous continuation run and write the plan artifact. Use when acting as the Planner persona, or when deciding what the repository should work on next from vision, category balance, and the dependency graph.
# Harness planner Selection procedure for the Planner persona. The prompt at `agent/harness/planner-prompt.md` is the contract; this is the method. ## 1. Establish state Run `bash agent/init.sh`. It resolves the dependency closure and tells you what is genuinely unblocked, so you do not recompute it. Read `agent/harness/horizon.json`. The previous run left a lookahead. Treat its head as the default choice and only depart from it with a reason you write down. ## 2. Build the candidate set A candidate must satisfy all of: - `status` is not `completed` - every id in `depends_on` is `completed` - it is not in the in-flight exclusion list appended to the prompt - it advances at least one facet or outcome in `agent/vision.md` If the set is empty, the correct output is a new feature you define fully, not the least-bad existing one. ## 3. Score In order: 1. **Vision alignment.** Which facet, which outcome, and how directly. A task that advances a facet indirectly loses to one that advances it head-on. 2. **Category balance.** Compare the mix across the current slice and the last several `PROGRESS.md` entries against the weights in `agent/categories.json`. Correct the largest deviation. 3. **Priority.** The tiebreak, not the driver. Two patterns worth naming: - **Lock after ship.** When the previous run shipped behaviour, the strongest next task is usually the eval that locks it while the shape is fresh. - **Unblock the widest.** When several tasks tie, prefer the one the most other features depend on. ## 4. Size One task, one pull request, inside the caps in `agent/harness-config.json`. If it does not fit, the plan is to split it and the chosen task is the first slice. Say so in `alternatives_considered`. ## 5. Write constraints that can be broken `execution_constraints` is what the Validator judges against, so each one must be falsifiable by looking at a diff. Weak: "keep the change focused", "do not break anything". Strong: "only `src/lib/**` and `src/eval/**` may change", "no new dependencies", "`npm run verify` must pass", "do not modify the hybrid search weights". Same for `principles`: quote real text from `AGENTS.md`, `.agents/rules/`, or `agent/vision.md`. The Executor restates them and the Validator checks compliance, so an invented principle corrupts both. ## 6. Persist Write the plan JSON and its narrative companion to the paths in `agent/harness/artifacts.json`, then update `agent/harness/horizon.json` with the new slice and a rationale per step. Write nothing else. No source, no tests, no `feature_list.json`.
Auf GitHub ansehen