agentsclimarketplace
Sign in

Spec intake

Skill mwarger/forge/skills/spec-intake

Normalize all starting evidence into a subject spec run. Use this when beginning a Forge spec from code, docs, transcripts, screenshots, URLs, or a sparse user request. Creates the subject slug, frontmatter, evidence ledgers, request archetype, evidence-density classification, and canonical readiness skeleton.From its SKILL.md

Install
npx -y skills add mwarger/forge --skill spec-intake

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.5 KB, as published. Nobody here has run it

Use this skill first.

Goal

Turn raw inputs into a normalized evidence run with:

  • a subject slug
  • a canonical spec path
  • a root specs index path
  • a request archetype
  • an evidence-density classification
  • a critical-decision coverage skeleton
  • seeded frontmatter
  • initial evidence records
  • intake summary

Steps

  1. Derive the subject slug from the thing being studied or developed.
  2. Ensure specs/README.md exists.
  3. If specs/README.md is missing, create it with:
    • a short title
    • a one-line description of the specs tree
    • an optional notes section
    • a managed block delimited by:
      • <!-- forge:spec-index:start -->
      • <!-- forge:spec-index:end -->
  4. If specs/README.md exists but the managed block is missing, add the managed block without deleting human prose outside it.
  5. Create specs/<subject>.md.
  6. Create specs/_artifacts/<subject>/.
  7. Classify every input as:
    • repo
    • doc
    • transcript
    • ui
    • user_statement
    • answer
    • observation
  8. For each input, create an evidence_unit record with:
    • id
    • source_type
    • source_ref
    • directness
    • authority
    • freshness
    • independence_group
    • extraction_method
  9. Record source_origin_keys from evidence provenance, not raw file count.
  10. Classify request_archetype:
  • feature
  • analogy_feature
  • parity_clone
  • integration
  • bugfix
  • migration
  • refactor
  • reverse_spec
  1. Classify starting_evidence_density:
  • sparse
  • mixed
  • dense
  1. Seed critical_decision_coverage with these buckets:
  • core_outcome
  • scope_boundary
  • implementation_constraints
  • dependencies_and_integrations
  • acceptance_signal
  1. Seed the spec with:
  • Overview
  • Intake Summary
  • Evidence Model
  1. Seed sidecars:
  • manifest.json
  • run-state.json
  • branch-registry.json
  • input-log.md
  • evidence-ledger.jsonl
  • claim-ledger.jsonl
  1. If UBIQUITOUS-LANGUAGE.md does not exist at the project root, create it with an initial set of domain terms extracted from the evidence sources. If it already exists, read it and use existing terms for consistency. Append any new domain terms, entity names, or concepts discovered during intake.
  2. Read AGENTS.md at the project root (if it exists). Note project conventions, patterns, and anti-patterns it encodes — these are prior implementation learnings that should inform the new spec rather than be rediscovered. Record relevant conventions as observation evidence units with source_type: prior_learnings.

Output rules

  • Intake Summary stays short.
  • Detailed intake history stays in sidecars.
  • A one-line feature request is still enough to start a run.
  • If the source is large, spawn sub-agents to catalog source areas before the main loop starts.
  • The root specs/README.md is navigational.
  • The subject spec is the implementation handoff artifact.
  • sparse analogy or feature runs should default to clarification if the critical decision buckets are not explicit in evidence.

Keep looking

Skills are one crate of 627,801. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.