spike

Imported from softwaresalt/csv-managed (.autoharness/staging/.github/skills/spike/SKILL.md). Install upstream with npx skills add softwaresalt/csv-managed --skill spike. Copyright stays with the author.

Spike

Execute a focused, time-boxed investigation to answer a specific technical question or evaluate feasibility. Produces a findings artifact that captures the investigation goal, approach, evidence, and recommendation. The artifact can be promoted to the planning pipeline, linked to a feature or chore as a prerequisite, or captured as a compound learning for institutional knowledge.

A spike replaces what was previously called "research" in the backlog structure. Unlike a deliberation (which produces a decision through interactive operator dialogue), a spike produces findings through hands-on investigation — reading code, prototyping, benchmarking, or evaluating external tools.

When to Use

• The operator or stage agent identifies unknowns that require investigation before committing to an implementation approach
• A feature or chore has technical risk that should be explored in isolation
• A proof-of-concept is needed to validate feasibility
• The team needs to evaluate external libraries, tools, or APIs
• Performance characteristics need measurement before design decisions
• Migration paths need assessment and comparison

When NOT to Use

• The question can be answered through deliberation alone (use deliberate)
• The work is implementation, not investigation (use build-feature)
• The investigation is debugging a specific failure (that's a bug fix, not a spike)

Inputs

• goal: (Required) The specific question the spike must answer, stated as a question or hypothesis.
• time_box: (Optional) Maximum duration for the investigation. Defaults to 4h. Accepted values: 1h, 2h, 4h, 8h.
• scope_constraints: (Optional) Boundaries on what the spike may and may not touch (e.g., "read-only — no production changes", "prototype in a branch only").
• linked_parent_work_item: (Optional) Path or ID of a feature or chore this spike informs. When provided, the findings artifact references that top-level release unit.
• promote_to: (Optional) Where to route the outcome. Accepts a single value or a comma-separated list for multiple paths: plan, queue, learnings, none, ask. Defaults to ask.
  • plan — promote to impl-plan for feature or chore planning
  • queue — create a queue entry with link to the spike artifact
  • learnings — capture as a compound learning entry
  • none — leave the artifact in docs/decisions/ without linking
  • ask — ask the operator at the end
  • Combinations are valid (e.g., plan,learnings)

Output

A findings artifact at docs/decisions/{YYYY-MM-DD}-{slug}-spike.md (long-lived knowledge) with an optional work item in .backlogit/queue/ for workflow tracking.

When the backlogit capability pack is installed and promote_to includes queue (or ask resolves to queue), the spike is also created as a backlog work item at conclusion with artifact_type: spike, labels: ["spike"], and a status of done. The work item filename follows the configured suffix convention: {id}-{suffix}-{slug}.md. If promote_to does not include queue, no backlog work item is created regardless of the installed capability pack.

Required Protocol

Phase 1: Scope

Step 1.1: Confirm the Investigation Goal

Restate the goal as a precise, answerable question. If the goal is vague, narrow it until you can describe what a successful answer looks like.

Document:

• Goal question: The specific question to answer
• Success criteria: What constitutes a sufficient answer (e.g., "we can demonstrate a working prototype", "we have latency numbers under load", "we have a recommendation with trade-off analysis")
• Out of scope: What this spike explicitly will NOT investigate

Step 1.2: Establish Constraints

• Confirm the time-box with the operator (default 4h)
• Confirm scope constraints (read-only, branch-only, sandbox, etc.)
• Confirm linked feature or chore if applicable

Step 1.3: Check Prior Work

Search for prior spikes, deliberations, and compound learnings on the same topic to avoid repeating past investigations.

When the agent-engram capability pack is installed, prefer indexed search for related modules, symbols, and prior context before falling back to broader file scans.

When the backlogit capability pack is installed and search is supported, query the backlog for related spike items, deliberation outcomes, or tasks that may have already explored this question.

• Search docs/decisions/ for prior spike artifacts and deliberation outcomes
• Search docs/compound/ for relevant institutional learnings
• Search .backlogit/queue/ for related active work items
• If prior work exists, summarize what was already learned and what remains unknown

Phase 2: Investigate

Step 2.1: Plan the Approach

Before diving in, outline the investigation approach in 3-5 steps. This keeps the spike focused and prevents scope creep within the time-box.

Step 2.2: Search the Codebase

Mandatory codebase investigation:

• Search the compound learnings library (docs/compound/) FIRST — treat prior learnings as mandatory pre-investigation context
• Analyze relevant code paths, modules, and patterns
• Identify existing implementations that relate to the question

When the agent-engram capability pack is installed, follow .github/instructions/agent-engram.instructions.md: prefer indexed search for related modules, symbols, and prior context before falling back to broader file scans.

Step 2.3: Hands-On Investigation

Execute the investigation approach:

• Read relevant source code and documentation
• Build prototypes or proofs-of-concept if the goal requires it
• Run benchmarks or measurements if the goal involves performance
• Evaluate external tools or libraries if the goal involves technology selection
• Document evidence as you go — don't rely on memory

Time-box enforcement: Track elapsed effort. If the investigation is approaching the time-box limit:

1. Stop exploring new avenues
2. Consolidate what you have learned so far
3. Proceed to Phase 3 with current findings
4. Note unresolved questions as "needs further investigation"

Step 2.4: External Research (When Needed)

If the codebase investigation is insufficient, conduct targeted external research. Focus on:

• Official documentation for relevant libraries or tools
• Known patterns and anti-patterns for the approach under evaluation
• Community experience with similar migrations or integrations

Phase 3: Synthesize

Step 3.1: Consolidate Findings

Organize investigation evidence into a coherent narrative:

• What was investigated
• What was discovered (with evidence)
• What was tried and failed (with reasons)
• What remains uncertain

Step 3.2: Form Recommendation

Based on findings, produce one of:

• Proceed: The investigation supports moving forward. Describe the recommended approach and any caveats.
• Pivot: The investigation suggests a different approach than originally expected. Describe the alternative and why.
• Defer: More investigation is needed, but the time-box has expired. Describe what additional work is required and the remaining unknowns.
• Abandon: The investigation reveals the approach is not viable. Describe the evidence and suggest alternatives if any exist.

Step 3.3: Assess Confidence

Rate confidence in the recommendation:

• High: Evidence is strong, multiple signals confirm the recommendation
• Medium: Evidence supports the recommendation but some unknowns remain
• Low: Limited evidence, significant unknowns persist — treat recommendation as tentative

Phase 4: Conclude and Link

Step 4.1: Determine Promotion Path

Present the findings summary and recommendation to the operator. Ask where to route the outcome (unless promote_to was pre-set):

• Plan: Findings are actionable — promote to impl-plan for feature or chore planning
• Queue: Findings inform future work — create a queue entry with link to the spike artifact
• Learnings: Findings are institutional knowledge — capture as a compound learning entry
• None: Leave the artifact in docs/decisions/ for manual reference

The operator may choose multiple paths (e.g., both plan and learnings).

Step 4.2: Promote to Implementation Plan (When Applicable)

When promote_to includes plan (or ask resolved to plan):

1. Invoke the impl-plan skill with the spike findings artifact as context:
   • Pass the findings artifact path as the plan source
   • Set the implementation scope to the recommendation from Step 3.2
   • Include the spike's success criteria and constraints as planning constraints
2. The impl-plan skill writes a plan document to docs/plans/{YYYY-MM-DD}-{slug}-plan.md
3. Update the spike findings artifact's promoted_to frontmatter field to include plan and add a plan_artifact field pointing to the generated plan path
4. If linked_parent_work_item was provided, the impl-plan output should reference that feature or chore so the planning chain is traceable

When plan is combined with other paths (e.g., plan,learnings or plan,queue), complete this step first, then continue with Steps 4.3 and 4.4 for the remaining paths.

If the stage agent is active (operator preference over direct impl-plan invocation), call stage instead and pass the spike artifact as the planning seed. The stage agent will invoke impl-plan internally and return the plan artifact path.

Step 4.3: Create Backlog Item (When Applicable)

When the backlogit capability pack is installed and create_task is available:

1. Create a spike item with artifact_type: spike
2. Set title: the goal question (shortened to 5-10 words)
3. Set description: link to the findings artifact path
4. Set labels: ["spike"] plus ["deliberation-outcome"] if the spike was triggered from a deliberation
5. Set status: done (since the investigation is complete)
6. If linked_parent_work_item was provided, set parent_id to link the spike to its feature or chore

When backlog-md is the installed backlog tool:

• If promoting to queue, create a spike item using backlogit_create_item with title from the goal question (shortened to 5-10 words), description linking to the findings artifact path, status: "done", and labels: ["spike"].

When no backlog tool is available:

• If promoting to queue, append a spike entry to .backlogit/queue/.stash.md with the format:

  - [{YYYY-MM-DD}] **Spike**: {goal summary}
    Conclusion: {proceed|pivot|defer|abandon} (confidence: {high|medium|low})
    Artifact: `docs/decisions/{YYYY-MM-DD}-{slug}-spike.md`
    Linked parent work item: {feature/chore reference or "standalone"}
  

Step 4.4: Capture as Compound Learning (When Applicable)

If the promotion path includes learnings or the spike produced insights that would benefit future work, invoke the compound skill with the findings as source material. The compound entry preserves the spike's key findings in the institutional knowledge base where the learnings-researcher can surface them in future tasks.

Phase 5: Write Findings Artifact

Write the findings artifact to docs/decisions/{YYYY-MM-DD}-{slug}-spike.md:

---
title: "{Goal question — short form}"
type: spike
date: {YYYY-MM-DD}
time_box: "{time_box value}"
conclusion: "{proceed|pivot|defer|abandon}"
confidence: "{high|medium|low}"
linked_parent_work_item: "{feature or chore path/ID, or null}"
promoted_to: ["{plan|queue|learnings|none}"]
tags:
  - "{domain tag}"
  - "{technology tag}"
---

## Goal

{Precise question or hypothesis}

## Success Criteria

{What constitutes a sufficient answer}

## Scope Constraints

{Boundaries on the investigation}

## Investigation Approach

{The 3-5 step approach taken}

## Findings

### What Was Discovered

{Organized evidence and observations}

### What Was Tried and Failed

{Approaches that didn't work and why}

### Remaining Unknowns

{What is still uncertain}

## Recommendation

**Conclusion**: {proceed | pivot | defer | abandon}
**Confidence**: {high | medium | low}

{Detailed recommendation with reasoning}

## Next Steps

{Actions following from the recommendation}

## References

{Links to code paths, documentation, prototypes, benchmarks examined}

Quality Criteria

• Goal question is specific and answerable, not open-ended
• Findings are grounded in evidence (code references, measurements, documentation) — not speculation
• Failed approaches are documented with reasons — not silently omitted
• Recommendation includes one of the four conclusion types with a confidence rating
• Time-box was respected — the spike did not expand beyond its declared limit
• If prior spikes or learnings existed on the topic, they were consulted and referenced
• The artifact has valid YAML frontmatter with all required fields
• If a backlog item was created, it has the correct artifact_type: spike and labels

Relationship to Other Workflows

• Deliberate: Deliberation produces decisions through dialogue; spikes produce findings through investigation. A deliberation may trigger a spike when unknowns are identified during Phase 2 (Research).
• Impl-plan: Spike findings with a proceed or pivot conclusion are valid source documents for impl-plan, just like deliberation outcomes.
• Compound: Spike findings can be captured as compound learnings for the institutional knowledge base.
• Build-feature: Spikes do NOT produce implementation code. If a spike creates a prototype, that prototype lives in a branch and is referenced in findings — it is not the deliverable.

Resumption Protocol

If the skill is interrupted (context overflow, session timeout, or operator halt), write a checkpoint to docs/memory/ capturing: current phase, evidence gathered, hypotheses formed, and next step. On re-invocation, check for an existing checkpoint. If found, resume from the recorded phase rather than restarting from scratch.

Model Routing

This skill operates at Tier 3 (Frontier) — open-ended investigation requires deep analysis.

Generated by autoharness | Template: spike/SKILL.md.tmpl