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[Feature]: Let extensions contribute always-on instructions (not just on-demand commands + hooks)

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@TheovanKraay y travaille déjà.

Depuis le 1/9/2026.

  • #4389 par @TheovanKraay — ouverte

Évaluation

Difficulté
5/5
Temps estimé
Plus d'une semaine
Accessibilité débutants
38/100
Type d'issue
Fonctionnalité
Clarté
Plutôt claire
Activité
Active
Stack technique
python, yaml
Domaine
cli, documentation, tooling

Piste de recherche

Commencez par retracer le schéma du manifeste de l’extension et les points d’entrée specify extension add ainsi que de désinstallation/mise à jour, puis comparez les marqueurs de fusion existants dans speckit.json et la gestion de .specify/memory/constitution.md. Examinez les cibles d’agents proposées, notamment .github/copilot-instructions.md, AGENTS.md, CLAUDE.md, GEMINI.md et .cursor/rules/. Le travail est considéré comme terminé lorsque les instructions sont validées par rapport au schéma, routées et fusionnées en toute sécurité, supprimables, compatibles avec l’opt-out et documentées pour tous les agents pris en charge.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Description

enhancement feature-assess feature-needs-clarification triage-can-wait
Problem Statement

Spec Kit extensions can deliver knowledge only two ways today: commands (on-demand prompts/skills the agent must choose to invoke) and hooks (fire around /speckit.implement). Both are opt-in by the agent. So in autonomous / hands-off workflows - increasingly the common case - an extension's guidance often never reaches the model: the agent is given a task, writes code directly, and never invokes the commands, so the extension has no effect.

We hit this building the Azure Cosmos DB Spec Kit extension (pre-release, still in active development). In autonomous agent runs where it was installed exactly as intended, the agent invoked its commands in 0 of 30 runs and the before_implement/after_implement hooks fired 0 times - net effect ≈ zero. There is currently no way for an extension to contribute always-on guidance (a few "always apply this while you work" rules in the agent's persistent context) the way the project constitution does.

Proposed Solution

Add an optional provides: instructions: capability to the extension manifest, so an extension can ship a compact always-on rule block that specify extension add installs into the agent-native always-on file for the active integration:

provides:
  commands: [ ... ]     # unchanged
  hooks: [ ... ]        # unchanged
  instructions:         # NEW
    - file: instructions/best-practices.md

Install it as a delimited, per-extension block so it is merge-safe (multiple extensions coexist; user-authored content preserved), idempotent, removable on uninstall, and routed per agent (.github/copilot-instructions.md for Copilot; AGENTS.md / CLAUDE.md / GEMINI.md; .cursor/rules/…) - or appended to .specify/memory/constitution.md if a single canonical target is preferred.

This looks very feasible because Spec Kit already has the building blocks and would reuse all three rather than invent a subsystem: an always-on context concept (the constitution), per-agent routing, and merge-with-markers for extension content (hooks -> speckit.json).

Evidence it matters: delivering the same guidance as an always-on rule block instead of commands improved a best-practice conformance score by +0.16 mean (vs +0.10 as commands), with better determinism and improvement in every measured cell (2 models × 4 languages × 3 complexity levels) - because always-on context can't be bypassed.

Alternatives Considered
  • A hook that writes the always-on file at before_implement. Unreliable: agents load instruction files at session start, so writing mid-session isn't picked up; also couples always-on context to the implement step.
  • Docs-only ("paste these rules into your copilot-instructions.md"). Manual, easily skipped, not portable across agents, and defeats the point of an extension.
  • Status quo (everything as commands/hooks). Proven not to reach autonomous agents (the 0/30 invocation above).
Component

Specify CLI (initialization, commands)

AI Agent (if applicable)

All agents

Use Cases
  1. A domain extension (e.g. Azure Cosmos DB) wants its few mandatory best-practices followed even when an autonomous agent never runs a command.
  2. A security / hardening extension wants "always apply these secure-coding rules" in context for every generation.
  3. An IaC / framework / accessibility extension wants its house style enforced across a whole session without the user or agent invoking anything.
  4. A team installs several extensions and wants each one's key rules merged, attributed, and cleanly removable, alongside their own project constitution.
Acceptance Criteria
  • provides: instructions: is accepted in the extension manifest schema.
  • specify extension add installs each instructions file into the correct always-on location for the active integration.
  • Content is written as a delimited, attributed block that merges safely with user content and with other extensions.
  • Uninstall / update cleanly removes or replaces the extension's block.
  • Users can disable extension-provided instructions (globally or per extension).
  • Works across supported agents (or a documented no-op where an agent has no always-on file).
  • Documentation updated.
Additional Context
  • General gap, not Cosmos-specific. Any extension whose value is best-practice guidance (security, IaC, API-design, framework, accessibility, …) hits the same wall - commands only help if the agent opts to run them. Azure Cosmos DB is just where we measured it.
  • Design notes (input welcome): keep instructions compact — always-on text costs tokens on every request, so a soft size cap / lint is worth considering; user-authored instructions and the constitution should take precedence over extension blocks; ensure deterministic ordering; support opt-out.
  • Forward-looking: the extension that surfaced this is still pre-release; we want to align its delivery model with Spec Kit's direction before shipping broadly - this is not a report of a regression in a shipped extension.
  • We have the full delivery-mechanism A/B data (models × languages × complexity) and a reference compact rule block, and are happy to share or prototype the capability.
Langage dominant
Python
Étoiles
139k
Forks
12.5k
Merge moyen
2 j 5 h
PR mergées (30 j)
164

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