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SKILL verified Apache-2.0 Self-run

Prune Harness

skill-space-dinosaurs-dinostack-prune-harness · by Space-Dinosaurs

Performs a periodic analysis pass over methodology files to surface deletion candidates - rules whose motivating assumptions have expired as Claude has become more capable.

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Install

$ agentstack add skill-space-dinosaurs-dinostack-prune-harness

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

/prune-harness

> Run the Activation preflight from METHODOLOGY.md before proceeding. If inactive, no-op and exit.

Performs a periodic analysis pass over methodology files to surface deletion candidates - rules whose motivating assumptions have expired as Claude has become more capable.

When to use: After each Claude model upgrade, or quarterly. This command is analysis only. It writes a proposal document and stops. No methodology files are changed. Actual deletions go through /update-agentic-engineering separately, one candidate at a time.

Do not use to: make changes, validate a specific rule's necessity, or replace judgment. The output is a proposal, not a verdict.

Safety model

The prune analyst Worker is instructed not to write to content/ and to restrict writes to the single output path in docs/planning/. This is enforced by Worker brief compliance, NOT by a harness-level technical barrier. The tool_scope field in the execution contract is documentation only - per the Worker preamble section of METHODOLOGY.md, it does not physically prevent writes. The analyst is instructed not to write to content/, and any violation would surface as a diff the conductor rejects before moving to Step 4. The authoritative gate is the Skeptic review on each subsequent deletion via /update-agentic-engineering. Do not describe this mechanism as "physically cannot delete" - it cannot and does not make that guarantee.

Step 0 - Preflight git sync

Run the Step 0 preflight from /update-agentic-engineering verbatim (fetch origin, check clean tree, check divergence, refuse dirty tree). Git state decisions require main-agent judgment; do not delegate this step.

Step 1 - Spawn the prune analyst

Spawn a single general-purpose Worker in background with the following execution contract (NLH format per METHODOLOGY.md):

"You are a Worker agent. Produce a pruning proposal for the agentic-engineering methodology corpus and return your complete output. The main agent will present the proposal to the user for approval."

  • outputs: a pruning proposal document written to docs/planning/harness-pruning-YYYY-MM-DD.md (substitute today's date)
  • budget: ~40 tool calls
  • tool_scope: Read, Glob, Grep, Write (Write restricted to the single output path - documentation only, Worker brief compliance)
  • completion_conditions: every file under content/rules/, content/references/, content/agents/, content/commands/ read; signal checklist applied section-by-section; proposal document written using the template below; no content/ file modified; if no candidates are found after applying all signals, the proposal still writes and states this explicitly with rationale (an empty proposal is a valid output)
  • output_paths: docs/planning/harness-pruning-YYYY-MM-DD.md

Pass the signal checklist verbatim in the spawn prompt (see Signal Checklist below). The analyst applies the checklist section-by-section and writes candidates incrementally to its output document rather than holding all findings in memory.

Signal checklist (verbatim - this is the binding contract)

The analyst applies each signal to every file in scope and flags candidates as they are found. No signal is skipped except Signal 4 when findings.md is absent at both resolver paths (see Signal 4).

Signal 1 - explicit model-version reference. Candidate if the named version is older than the current deployed model. NOTE: on the current corpus, no model-version references exist in rules/references/agents - this signal is primarily a forward-looking guard. If it fires, flag with HIGH confidence.

Signal 2 - "because the model forgets" framing. Candidate if that failure class has not appeared in findings.md (resolved via .agentic/findings.md preferred, legacy .claude/findings.md fallback) in the last 6 months, or the rule has no known firing instance. MEDIUM confidence.

Signal 3 - verbatim duplication across files. Flag the duplicate, not the canonical (usually the longer or more detailed version). EXCEPTION: cross-reference duplication is NOT a candidate. Intentional repetition - a preamble appearing in both a rule file AND the command that instructs agents to follow the rule, the execution contract appearing in both METHODOLOGY.md AND implement-ticket.md, the Skeptic sign-off format appearing in both skeptic-protocol.md AND METHODOLOGY.md - is load-bearing structural redundancy, not accidental bloat. The analyst must explicitly test: "would deleting this copy break a cross-reference another doc depends on?" If yes, not a candidate. MEDIUM confidence when the duplication is genuinely accidental.

Signal 4 - contradiction with findings.md entries. Resolve findings.md via .agentic/findings.md preferred, legacy .claude/findings.md fallback. If neither path exists (as is currently the case for this repo), SKIP this signal entirely and note "Signal 4 skipped: findings.md does not exist at either resolver path in this repo" in the proposal's signal summary. Do not produce false candidates.

Signal 5 - orphaned fallback text. Sections labeled "fallback", "legacy", or "if the agent cannot" where the older behavior no longer occurs. MEDIUM confidence.

Signal 6 - rule complexity exceeding the behavior it constrains. LOW confidence - the analyst flags these with an explicit "low-confidence" marker and explicitly defers to human judgment. Not a standalone deletion candidate - only a "consider simplifying" suggestion. The analyst must NOT propose outright deletion on Signal 6 alone.

Signal 7 - reference doc that is a strict subset of another. MEDIUM confidence. Flag for consolidation, not outright deletion, unless the subset is empty (no unique content).

Explicit non-signals:

  • Short rules - length is not a deletion signal
  • "This could be inferred" - theoretical deducibility is not a signal; affirmative evidence of expiration is required
  • Intentional cross-reference duplication (see Signal 3 exception)

Confidence tiers

Every candidate in the proposal carries a confidence tier:

  • HIGH - Signal 1 (named model version is stale). Also used when multiple MEDIUM signals fire on the same candidate.
  • MEDIUM - Signals 2, 3, 5, 7 when the evidence is unambiguous.
  • LOW - Signal 6 (complexity) only. Always accompanied by an explicit "consider simplifying" suggestion rather than a deletion proposal.

Mixed-signal candidates take the highest confidence among the triggering signals.

Proposal document template

The analyst writes the proposal using this exact structure:

# Harness Pruning Proposal - YYYY-MM-DD

## Signal summary
- Total candidates: N (H high / M medium / L low confidence)
- Signals that fired: [list]
- Signals skipped: [list, with reason - e.g., "Signal 4: findings.md absent at both resolver paths"]
- Signals that produced no candidates: [list]

## Deletion candidates

### [Candidate title]
- Confidence: HIGH | MEDIUM | LOW
- File: content/path/to/file.md (lines N-M)
- Signal(s): [which signals fired]
- Rationale: [why this is a candidate, specific evidence]
- Risk if wrong: [what breaks if this is deleted incorrectly]
- Suggested action: [delete / consolidate into X / simplify]

(repeat per candidate)

## Notable checks that passed
[Optional: rules reviewed and explicitly kept, with brief rationale - only if especially relevant]

## Recommended action sequence
[Ordered list of candidates to action, one per /update-agentic-engineering invocation - see Step 4]

Step 2 - Present to user

After the analyst returns, the conductor:

  1. Reads the proposal file.
  2. Presents inline: candidate count by confidence tier, top 3 candidates with one-line description each, and the full proposal file path.
  3. Waits for explicit user approval of SPECIFIC candidates before moving to Step 4. The user may approve a subset, defer others, or reject all.

Do not proceed to Step 4 without a clear "approve candidate X" (or equivalent) from the user.

Step 3 - (deliberately not automated)

There is no Step 3 that runs automatically. The proposal is a human-reviewed artifact. Each approved candidate moves to Step 4 individually.

Step 4 - Action approved candidates

One /update-agentic-engineering invocation per approved candidate. Each deletion gets its own Worker + Skeptic cycle.

If the user approves N candidates, the conductor runs /update-agentic-engineering exactly N times, one per candidate, sequentially. Each call gets its own Worker spawn for the specific deletion, its own Skeptic review on the single-file diff, and its own commit. Batching deletions into a single Worker scope collapses the per-deletion review gate and is prohibited.

Why this matters: each deletion is an independent content decision. A Skeptic reviewing a single-file, single-deletion diff can check that nothing else references the deleted rule. A Skeptic reviewing 5 deletions at once has scope bleed and may miss cross-references.

docs/planning/ - Vercel note

docs/planning/ is inside the Vercel static deploy tree (per project MEMORY.md). Proposal files written there will be published to the deployed site. This is intentional - the pruning audit trail is a design artifact. If the deployed site's nav does not link docs/planning/, the files are accessible only by direct URL. Do not treat proposal files as sensitive.

If you want to avoid publishing a given proposal, move or delete the file from docs/planning/ before deploying - but this is optional and not required by default.

Risks and failure modes

  • Over-pruning (analyst flags an active, necessary rule): mitigated by per-candidate user approval and a fresh independent Skeptic on each /update-agentic-engineering deletion. The Skeptic's cross-reference check is the last line of defense.
  • Under-pruning (0 candidates): a valid output. The proposal must state that all signals were applied and explain which signals were checked. Silently returning an empty proposal without rationale is not acceptable.
  • False-positive on Signal 3 (intentional duplication flagged): the signal explicitly lists the cross-reference exception. If the analyst flags a known intentional duplicate, it is a proposal error - reject the candidate in Step 2.
  • Signal 6 subjectivity: clamped to "consider simplifying" suggestions only, never outright deletion proposals. Human judgment is required; the analyst is not authorized to propose deletion on Signal 6 alone.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.