Pp Keenable

Keenable — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the keenable-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:

1. Install via the Printing Press installer. It defaults binaries to $HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:

   npx -y @mvanhorn/printing-press-library install keenable --cli-only

2. Verify: keenable-pp-cli --version
3. Ensure the reported install directory is on $PATH for the agent/runtime that will invoke this skill.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.6 or newer). This installs into $GOPATH/bin (default $HOME/go/bin), so add that directory to $PATH instead:

go install github.com/mvanhorn/printing-press-library/library/ai/keenable/cmd/keenable-pp-cli@latest

If --version reports "command not found" after install, the runtime cannot see the binary directory on $PATH. Do not proceed with skill commands until verification succeeds.

Search current web knowledge and fetch clean Markdown through the documented Keenable API, then preserve the evidence locally so agents can replay, compare, and cite prior research instead of starting from a blank response.

When to Use This CLI

Use Keenable when an agent needs current web evidence, clean page content, or a reproducible research trail. Prefer the local evidence commands when the task involves comparing runs, citing sources, or assembling a multi-page brief.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI for writing or mutating remote Keenable resources; the API exposes read-only search and fetch operations.
• Do not treat local snapshots as fresh remote data; refresh explicitly when current evidence matters.
• Do not use the keyless tier for sustained high-volume workloads when an organization-scoped key is available.

Unique Capabilities

These capabilities aren't available in any other tool for this API.

Evidence that compounds

• research snapshot — Save an exact search and fetch run as an immutable local evidence snapshot.

  Choose this when an agent needs to reproduce or audit the evidence behind a research answer.

  keenable-pp-cli research snapshot --query "AI agent evaluation methods" --max-results 8 --agent

• research replay — Rerun a saved research recipe and report how current evidence changed.

  Choose this for weekly monitoring and regression checks where ranking drift matters.

  keenable-pp-cli research replay --snapshot latest --agent

• research local-search — Search saved titles, snippets, URLs, and Markdown without spending an upstream request.

  Choose this when an agent needs to recall prior evidence while preserving a clear local-versus-live boundary.

  keenable-pp-cli research local-search "retrieval evaluation" --agent

• research diff — Compare saved runs for URL changes, rank movement, metadata edits, and content-hash drift.

  Choose this to detect changing sources before an agent relies on stale research.

  keenable-pp-cli research diff --after latest --agent

Agent-ready evidence

• research citations — Export source-linked Markdown or JSON citations from a saved research run.

  Choose this when another agent or reviewer needs portable, inspectable evidence rather than raw API output.

  keenable-pp-cli research citations --snapshot latest --format markdown

• research fetch-many — Fetch a bounded URL list with concurrency limits and explicit partial failures.

  Choose this when a pipeline has several known sources and must not silently drop a failed fetch.

  keenable-pp-cli research fetch-many --url https://docs.keenable.ai/api-reference --url https://docs.keenable.ai/mcp-server --agent

• research coverage — Measure domain diversity, rank share, timestamp coverage, and missing metadata in a saved run.

  Choose this when an agent must explain whether its evidence is broad, balanced, and date-grounded.

  keenable-pp-cli research coverage --snapshot latest --agent

Command Reference

fetch — Manage fetch

• keenable-pp-cli fetch fetch — Extract clean content (markdown) from a web page URL.
• keenable-pp-cli fetch public — Keyless twin of GET /v1/fetch. Same query parameters and same response, no API key.

web_search — Manage web search

• keenable-pp-cli web-search search-post — Perform a web search using a JSON request body. Supports date filters and site restriction.
• keenable-pp-cli web-search search-post-public — Keyless twin of POST /v1/search. Same request body and same response, no API key.

Finding the right command

When you know what you want to do but not which command does it, ask the CLI directly:

keenable-pp-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match — fall back to --help or use a narrower query.

Recipes

Save a reproducible run

keenable-pp-cli research snapshot --query "AI agent evaluation methods" --max-results 8 --agent

Persist the exact query and evidence for later replay.

Fetch several sources safely

keenable-pp-cli research fetch-many --url https://docs.keenable.ai/api-reference --url https://docs.keenable.ai/mcp-server --agent

Keep successes and failures explicit in one structured result.

Recall saved evidence

keenable-pp-cli research local-search "retrieval evaluation" --agent --select results

Search the local corpus without claiming fresh upstream data.

Compare research drift

keenable-pp-cli research diff --after latest --agent

Inspect changed sources and content between two saved runs.

Auth Setup

Set KEEENABLE_API_KEY for authenticated organization-scoped usage. Keenable also exposes keyless public endpoints that require an X-Keenable-Title application header and share an IP-based pool.

Run keenable-pp-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color.

• Pipeable — JSON on stdout, errors on stderr

• Filterable — --select keeps a subset of fields. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

  keenable-pp-cli fetch fetch --url https://example.com/resource --agent --select author,content,description

• Previewable — --dry-run shows the request without sending

• Non-interactive — never prompts, every input is a flag

• Read-only — do not use this CLI for create, update, delete, publish, comment, upvote, invite, order, send, or other mutating requests

Paths and state

Agents should treat the CLI's path resolver as part of the runtime contract:

• Use --home <dir> for one invocation, or set KEENABLE_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: KEENABLE_CONFIG_DIR, KEENABLE_DATA_DIR, KEENABLE_STATE_DIR, KEENABLE_CACHE_DIR.

• Resolution order is per-kind env var, --home, KEENABLE_HOME, XDG (XDG_CONFIG_HOME, XDG_DATA_HOME, XDG_STATE_HOME, XDG_CACHE_HOME), then platform defaults.

• config contains settings like config.toml and profiles. data contains credentials.toml, data.db, cookies, and auth sidecars. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.

• Stored secrets live in credentials.toml under the data dir. Existing legacy config.toml secrets are read for compatibility and leave config.toml on the first auth write.

• Run keenable-pp-cli doctor --fail-on warn to surface path and credential-location warnings. agent-context exposes a schema v4 paths block for agents that need the resolved dirs.

• For MCP, pass relocation through the MCP host config. The MCP binary does not inherit CLI flags:

  {
    "mcpServers": {
      "keenable": {
        "command": "keenable-pp-mcp",
        "env": {
          "KEENABLE_HOME": "/srv/keenable"
        }
      }
    }
  }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use KEENABLE_HOME or per-kind vars as durable fleet levers, and use --home only for a single invocation. Relocation is not reversible by unsetting env vars; move files manually before clearing KEENABLE_HOME, or doctor will not find credentials left under the former root.

Automatic learning

This CLI ships a self-capturing learning loop. The CLI does its own bookkeeping: every invocation is journaled locally, a failed flag followed by a corrected retry auto-derives a flag_alias candidate, and a teach on a query family without a playbook auto-synthesizes a playbook_candidate from the session's journal. Your job is judgment only: recall first, act on surfaced candidates, teach the final answer, playbook amend when you observe a correction. You never record failures by hand.

Step 1: recall before any discovery

Before list/search/drill commands on a new user question, run:

# Agent input must already be structuralized and identifier-free. Do not
# read from stdin: agent shells are noninteractive and a user question can
# contain names, emails, phone numbers, or account identifiers.
STRUCTURAL_QUERY='search current API documentation'
keenable-pp-cli recall "$STRUCTURAL_QUERY" --agent

The response envelope:

{
  "query": "...",
  "normalized": "<normalized form>",
  "query_entities": ["..."],
  "found": true | false,
  "match_score": 0.0,
  "results": [
    { "resource_id": "...", "resource_type": "...", "venue": "...",
      "confidence": 2, "entity_match": "exact|partial|unknown",
      "source": "taught|preseed|pattern", "warnings": ["..."] }
  ],
  "mismatches": [ /* only when --debug-mismatches */ ],
  "warnings": [ /* top-level */ ],
  "candidates": [
    { "id": 12, "class": "flag_alias | playbook_candidate",
      "summary": "...", "sightings": 3, "last_seen": "...",
      "rationale": "...",
      "next_action": ["<trial command>", "keenable-pp-cli learnings confirm 12"] }
  ],
  "playbook": {
    "query_family": "...",
    "playbook": {
      "steps": [ { "cmd": "<command with {slot} substitution>", "purpose": "..." } ],
      "entity_slots": ["$ENTITY"],
      "expected_tool_calls": 3
    },
    "slots_resolved": { "$ENTITY": { "token": "<live token>", "canonical": "<canonical>" } },
    "notes": "<workarounds + gotchas for this query family>"
  },
  "notes": "<duplicate surface for non-playbook callers>"
}

Empty-store short-circuit: if the store has no learnings, playbooks, or candidates yet (recall finds nothing and learnings list and learnings candidates are both empty), skip recall for the rest of this session instead of taxing every query; resume recall-first once something has been taught.

Step 2: decision tree

Read candidates, playbook, notes, results[0], and warnings in that order:

if Candidates present (warnings include "candidates_present"):
    -> candidates are try-then-confirm, never facts. Follow each candidate's
       two-step next_action verbatim: run the trial command first, then run
       `learnings confirm <id>` only after the trial verified the behavior.
       Reject a wrong candidate with `learnings reject <id>`.
    -> NEVER re-teach something recall surfaced as a candidate; confirm or
       reject that candidate instead of teaching a duplicate.
    -> candidates ride alongside playbooks and resource hits, not instead of
       them; continue with the branches below after acting on them.

if Playbook present:
    -> READ Playbook.notes verbatim FIRST (workarounds + gotchas the CLI surface doesn't expose)
    -> replay Playbook.steps in order, substituting Playbook.slots_resolved entries
       for the entity slot tokens. If a step's slot is unresolved, fall back to
       discovery for that step only.
    -> the Playbook's expected_tool_calls is a budget; if you find yourself running
       materially more, record the divergence via `keenable-pp-cli playbook amend`
       at end-of-session.

elif Notes present (no Playbook):
    -> read Notes verbatim before any discovery step; they carry known gotchas
       for this query family even when no structured choreography exists yet.

elif Found AND Results[0].EntityMatch == "exact" AND Results[0].Confidence >= 2:
    -> skip discovery; fetch live data for Results[*].ResourceID in parallel

elif Found AND Results[0].EntityMatch == "partial":
    -> candidate hint, NOT a hit; read the resource title to validate before trusting

elif (any row in Mismatches[] when --debug-mismatches was passed):
    -> treat as cold start; the stored learning is for a different entity
       (different canonical resolved from query_entities)

else:  // Found == false, no playbook, no notes
    -> cold start; run discovery normally; teach the answer afterward (Step 4).
       If the family has no playbook yet, that teach auto-synthesizes a
       playbook candidate from this session's journal - you do not need to
       record one by hand.

Playbook and Notes are orthogonal to the per-resource path. A recall response can carry both a Playbook AND a Results[] hit - use both: the Playbook tells you which choreography to run; the resource hits short-circuit specific steps. Default to skipping mismatches; pass --debug-mismatches only when investigating cold-start surprises.

Candidate judgment details: learnings confirm <id> prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. learnings reject <id> tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; keenable-pp-cli learnings candidates lists the full open set.

Graceful degradation: if learnings confirm is an unknown command, you are driving an older binary - ignore the candidates guidance and follow the rest of the protocol.

Step 3: always read warnings

• low_confidence: row exists at confidence<2. Treat as a hint, not a skip-discovery hit.
• resource_not_in_store: the local store doesn't have the resource the learning points at. The match validator couldn't classify entities — direct-fetch and re-evaluate.
• cross_alias_match (per-result): the row was taught under a different alias and matched the live query's canonical via entity_lookups (e.g., a "USA" teach satisfying a "United States" recall). Trust the resource_id.
• similar_shape_different_entity:<canonical> (top-level): a structurally matching row exists but its canonical entity differs from the live query's. Treated as cold start; the warning carries the conflicting canonical as a hint, but the row is NOT promoted into Results.
• ambiguous_alias (top-level): a single query entity resolved to multiple canonicals (e.g., "Cards" → Arizona Cardinals + St. Louis Cardinals). Surface the ambiguity from context before committing to a resource.
• candidates_present (top-level): the envelope carries a candidates section. Handle it via the candidates branch in Step 2 before anything else.
• Top-level no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.

Step 4: teach & after finalizing your response - always

Teaching is unconditional. After resolving a query the store could not answer, background-teach the final resource mapping - no call-count threshold, no judging whether it was "worth" learning. The teach is the anchor of the loop: it triggers playbook synthesis for a family without a playbook, and same-referent phrasings fold into one family so near-duplicate teaches do not fragment the store. Fire it after assembling your user-facing response but BEFORE emitting it, with a shell & so the call returns immediately:

# Pass only an identifier-free structural query held by the agent. Never copy
# a raw user question into the learning store.
STRUCTURAL_QUERY='search current API documentation'
keenable-pp-cli teach --query "$STRUCTURAL_QUERY" --resource-type <type> --resource <id1> --resource <id2>
# (append shell `&` to background it)

Silent on success. Errors only land in teach.log under the resolved state dir. Teach the most specific resource - if the user asked a broad question and you walked through parent records to find the specific answer, teach the leaf id, not the parent. The CLI uses seeded entity_lookups for cross-alias resolution at recall time, so a teach under one alias (e.g., "Niners") satisfies future queries under another alias (e.g., "49ers", "San Francisco") automatically.

PII rule: teach the structural question with identifiers stripped - never include names, emails, phone numbers, account ids, or other personal identifiers in taught queries or notes. The CLI scans teach queries for obvious email/phone shapes and warns, but does not block; strip before teaching rather than relying on the warning.