agentsclimarketplace

Plan

Skill bricerising/enterprise-software-playbook/skills/plan

Break a request into a scoped implementation plan with ordered tasks, risk flags, and verification steps. Use before starting non-trivial, cross-cutting, or ambiguous work to align on approach and prevent rework. NOT for writing spec artifacts or contracts (use spec); NOT for auto-routing across multiple skills (use workflow).From its SKILL.md

Install
npx -y skills add bricerising/enterprise-software-playbook --skill plan

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

2 things to look at

  • 7 stars7 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.
  • runs commandsInstructs the agent to run 3 commands, including `archobs show all --format json` and 2 more.

SKILL.md

8.1 KB, ~1.7k tokens by cl100k_base, as published. Nobody here has run it

Plan

Overview

Create a short plan that a developer (or agent) can execute end-to-end: ordered tasks, acceptance checks, and a concrete verification strategy.

Chooser

  • Use plan when: starting non-trivial work that needs task ordering, risk flags, or cross-cutting coordination — especially when scope is ambiguous or multiple approaches exist.
  • Do NOT use plan when: the change is tiny (rename, typo fix, single-file edit) — just do it.
  • Do NOT use plan for: writing spec artifacts or contracts (use spec); auto-routing across multiple skills (use workflow).

Inputs / Outputs

Inputs: User request; archobs JSON from archobs show all --format json (required for non-tiny); forecast data (if external dependencies involved via step 4b). Outputs: Objective function, decision table (if 2+ approaches), ordered task list with acceptance criteria, measurement ladder. Consumed by downstream skills: spec, architecture, design, testing, finish.

Workflow

  1. Write the objective function:

    • goal (one sentence)
    • constraints (budget/time/compliance/latency/team capacity/etc.)
    • anti-goals (what you are explicitly not optimizing now)
  2. Externalize a one-page system sketch:

    • boundary (in/out) + time horizon (near-term vs later)
    • actors + incentives
    • key flows (work/data/risk/attention)
    • top 3 constraints/bottlenecks
  3. Scope the change:

    • impacted components/services
    • impacted boundaries/contracts (HTTP/gRPC/events/WS/data model)
    • what is explicitly out of scope
  4. Load archobs data (required): Run archobs show all --format json to get risk scores, cluster health, and drift in one shot. Files with risk > 0.5 and clusters with leakage > 0.20 should appear earlier in the task order. Drift data (ari_prev < 0.50) flags areas to avoid broad moves in. If the artifacts do not exist, run archobs report --repo <path> --out .archobs --suggestions-provider rules and wait for it to complete before continuing. 4b. Risk Amplification (conditional — run when any high-risk files/clusters from archobs touch external dependencies or technology choices):

    intel forecast    # lifecycle phases for relevant dependencies
    

    Cross-reference matrix: See Lifecycle Decision Mapping — Plan: Risk Amplification for archobs signal x forecast signal → priority adjustment table.

  5. Identify the primary risk(s) (pick 1–3): correctness, migration, partial failure, security/privacy, performance, operability.

  6. Add a compact decision table (2–3 options including a no-change baseline):

    • what each option optimizes
    • what each option knowingly worsens
    • kill criteria / reversal trigger
  7. Stress-test the decision (if 2+ viable approaches exist; skip for single viable approach):

    • Assumptions: What are facts vs assumptions? Which assumption is least certain — how will we validate it? Cross-reference with risk amplification from step 4b (if applicable). (attach to decision table)
    • Second-Order Effects: What happens next week / next quarter / next year? What new load, toil, coupling, or failure mode does this create? If this fails in 6-12 months, what likely caused failure? Cross-reference with risk amplification from step 4b (if applicable). (attach to decision table)
    • Opportunity Cost: What are we saying "no" to? Are we favoring this due to sunk cost, familiarity, or novelty? (attach to decision table)
    • If probe output already exists from an earlier Define-stage skill in this flow (including workflow orchestration), refine it instead of re-running.

GATE: If step 6 identified 2+ viable approaches, a decision table MUST exist with trade-offs and kill criteria before proceeding. Do not skip to the task list — a plan without a decision is just a to-do list.

  1. Choose the minimum up-front artifacts:
    • if boundary semantics/contracts change → use spec
    • if cross-service/system pressure exists → use architecture
    • if in-process structure pressure exists → use design
    • if repeated boundary logic is likely → use platform
  2. Produce an ordered task list:
    • tasks should be small, reversible, and verifiable
    • include "stop points" where you can re-check assumptions and kill criteria
    • include one quick blast-radius check ("if X degrades, what breaks next/silently?")
  3. Define measurement + verification:
  • measurement ladder: decision, 3 leading indicators, 3 lagging outcomes, instrumentation source, review ritual (owner + cadence + trigger)
  • exact commands (tests/lint/typecheck/build) if known
  • if unknown, list what you will run and ask once for preferred commands

Minimum viable execution

When context or time is constrained, these are the load-bearing steps:

  1. Write objective function (step 1) — goal + constraints + anti-goals.
  2. Load archobs data (step 4) — risk-based task ordering depends on this.
  3. Decision table (step 6) — required if 2+ approaches exist.
  4. Ordered task list with acceptance criteria (step 9) — every task must be verifiable.

Steps that can be cut under pressure: system sketch (step 2), stress-test probes (step 7), measurement ladder (step 10).

Clarifying Questions

  • What is the goal in one sentence? What are we explicitly not optimizing?
  • What components, services, or boundaries are affected?
  • Are there time, budget, or compliance constraints?
  • Is there existing archobs data or a recent report available?
  • What verification commands are available (tests, lint, typecheck, build)?

Guardrails

  • Keep the plan short (usually 5–12 tasks). Avoid “spec theater” for tiny changes.
  • Every task needs an observable acceptance check (test, command output, file diff, or demo step).
  • Call out unknowns early; don’t pretend certainty.
  • No metric without a named decision it informs.
  • For non-trivial work, include explicit opportunity costs; avoid decision-by-default.
  • If you propose retries, also propose idempotency/dedupe and time budgets (resilience).

Common failure modes

  • Produces task lists without acceptance criteria — tasks cannot be verified as done.
  • Skips the decision table when 2+ approaches exist, defaulting to the first idea without evaluating alternatives.
  • Does not load archobs data, so task ordering ignores empirical risk scores and cluster health.
  • Conflates constraints with anti-goals — constraints are things you must satisfy; anti-goals are things you're explicitly choosing not to optimize.
  • Plans that are just "the steps I was going to take anyway" — no risk identification, no stop points, no blast-radius check.

Output Template

Return:

  • Goal: 1–2 sentences.
  • Objective function: goal + constraints + anti-goals.
  • System sketch: boundary/time horizon, actors/incentives, key flows, bottlenecks.
  • Scope: in/out.
  • Decision table: options, optimizations, known downsides, kill criteria, assumptions (facts vs assumptions), and opportunity costs.
  • Risks/assumptions: 3–6 bullets.
  • Plan: ordered checklist with acceptance per task.
  • Measurement ladder: leading/lagging indicators, instrumentation, owner/cadence/trigger.
  • Verification: commands to run + what “good” looks like.
  • Open questions: only if blocking.

References

  • Empirical risk data for prioritization: archobs
  • Structured-thinking probes + templates: ../references/ (checklists for inline probes, templates for escalation)

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most plan spec skills give in ~1.7k tokens

Counted across 1,360 of the 2,617 authors here whose files we hold, read 2026-09-06

  • Ask one question at a timein 73 of 1360
  • Write the spec using the templatein 22 of 1360
  • Ask clarifying questions if neededin 19 of 1360, across 18 files
  • Wait for user confirmation before proceedingin 19 of 1360
  • Save plans to the plans directoryin 17 of 1360, across 13 files
  • Check for product marketing context firstin 16 of 1360, across 5 files
  • Read the plan file completelyin 16 of 1360
  • Order tasks by dependencyin 16 of 1360
  • Gather context from the conversationin 15 of 1360, across 9 files
  • Explore the codebase instead of askingin 15 of 1360, across 13 files
  • Wait for explicit user approvalin 14 of 1360, across 13 files
  • Quiz the user on the breakdownin 13 of 1360, across 7 files

Said here and by no other author read

  • Write the objective function with goal constraints and anti-goals
  • Externalize a one-page system sketch
  • Scope the change by listing impacted components and out of scope items
  • Run archobs show all format json to load archobs data
  • Add a compact decision table with options and trade-offs
  • Produce an ordered task list with acceptance checks

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. 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.