walk

A search and navigation command for a knowledge graph, which is a collection of linked notes. It finds relevant notes by meaning and can also inspect graph relationships such as neighbors, orphans, and hubs.

In plain words
What is it for?
Use it to find claims related to a question, inspect notes within a chosen number of links, find isolated claims, or identify notes that connect many others.
Why use it?
It reduces the need to search files one by one or remember where related information is stored. The graph views help reveal disconnected notes and links between topics.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/letrplb/second-brain/walk
Any agent
npx skills add letrplB/second-brain --skill walk
Clone the repo
git clone --depth 1 https://github.com/letrplB/second-brain

Made for: Claude Code, Codex.

Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,141 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.03141
Opus 5 $0.00011 $0.01571
Sonnet 5 $0.00004 $0.00628
Haiku 4.5 $0.00002 $0.00314

Measured 2d ago against content hash 12e505785e79, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

walk scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (tools/topology.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/walk/SKILL.md · 220 lines

How it starts

The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/walk

Intent. Walk the graph and return what's relevant. Semantic search is the default; topology operations are opt-in via flags.

Modes

Semantic mode (default)

User asks: /walk how does annealing relate to crystallisation in dmsn synthesis?

  1. Route to qmd MCP. Use lex (BM25) + vec (semantic) sub-queries. Pass intent so qmd's snippets are useful.
  2. Optionally augment with hyde (hypothetical document) if the query is abstract.
  3. Score and merge results. Filter by minScore: 0.5 to drop low-confidence hits.
  4. Return a ranked list:
    [[claim-title-1]] (claim, confidence: probable)
      description...
      why-it-matched: ...
    [[claim-title-2]] ...
    

Topology mode

Specific operations on the graph structure. Each is a flag; tools/topology.py provides the computation.

Flag What it returns
--orphans claims with no inbound or outbound [[wikilinks]]
--bridges <topic> claims with high betweenness centrality bridging the named topic to others
--neighbors <claim> [--depth N] claims within N hops of the named claim
--centrality ranked list of high-centrality (hub) claims
--moc-coverage list of claims that aren't in any _<topic>.md MOC
--disconnected-clusters [--min-size N] pairs of MOCs that share no member claims and no 1-hop cross-edges. Meta-MOCs (frontmatter meta: true) are excluded so they don't pollute the lonely-pair signal.

Mixed mode

A semantic query with a topology constraint, e.g. /walk crystallisation --neighbors-of dmsn-synthesis --depth 2. Run topology to get a candidate set, then semantic-rank within it.

Behaviour

  1. Parse the argument: query string, flags.
  2. Read vault/vault.yaml. Check qmd.enabled, surrender.enabled, surrender.walk_integration. Re-read every invocation; never cache any flag.
  3. If topology flag → invoke tools/topology.py with the appropriate sub-command. Parse result.
  4. If semantic and qmd.enabled: true → invoke qmd searches. Parse + filter. Continue at step 6.
  5. If semantic and qmd.enabled: false → use the grep-fallback path (see "Grep fallback" section below). Continue at step 6.
  6. If topology + semantic → constrain semantic to topology-result subset.
  7. Format the output as a ranked list with descriptions and a short why-it-matched annotation.
  8. If surrender.enabled && surrender.walk_integration: read engagement frontmatter from each result claim, compute decay, render the engagement column and the weakest-grip panel. Else: skip surrender rendering entirely; output is byte-identical to the gated-off behaviour.
  9. Print, with a one-line preamble noting which retrieval path was used (qmd / grep-fallback) and the surrender state (enabled / off).

Read the full file on GitHub · 220 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 220 lines · 22 tokens per session scan A 12e505785e79

Subscribe to this mod's changes

walk is a skill published in the GitHub repository letrplB/second-brain (1 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 3,141 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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