Agent skill

Agent Wiki Ingest

by AgentToolkit in AgentToolkit/altk-evolve

Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, optionally compare…

Apache-2.0Auto-check passedKnowledge Management

Install Agent Wiki Ingest

skills CLI
$ npx skills add AgentToolkit/altk-evolve --skill agent-wiki-ingest -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AgentToolkit/altk-evolve agent-wiki-ingest --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AgentToolkit/altk-evolve.git skills-src && mkdir -p .claude/skills && cp -r skills-src/explorations/agent-wiki/skills/agent-wiki-ingest .claude/skills/agent-wiki-ingest && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-wiki-ingest
GitHub stars
122
Token cost
~4k tokens
SKILL.md length
1,741 words
Files
1
Skills in repo
21
Repo updated
First seen
Licence
Apache-2.0

At a glance

Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, optionally compare…

  • Works in 9 steps: Convert → Bootstrap the wiki → 5 — Skip already-processed traces… → …
  • You have a batch of traces to turn into a wiki in one pass
  • SKILL.md covers Overview, Input, Step 0 — Convert and Step 1 — Bootstrap the wiki, plus 9 more sections
  • Calls uv and node

What it does

Agent Wiki Ingest is an agent skill from AgentToolkit/altk-evolve. Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, optionally compare outcomes, consolidate into clusters, and catalog. Use when you have a batch of traces to turn into a wiki in one pass.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering LLM wikis and End-to-end testing. The repository describes itself as: Self improving agents through iterations. The licence is Apache-2.0.

When your agent uses it

  • You have a batch of traces to turn into a wiki in one pass
  • Tasks that involve LLM wikis
  • Tasks that involve End-to-end testing

Example prompts

  • “/agent-wiki-ingest”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Convert
  2. Bootstrap the wiki
  3. 5 — Skip already-processed traces (pre-flight)
  4. Summarize (parallel subagents)
  5. Extract guidelines (sequential subagents)
  6. Synthesize skills (sequential subagents)
  7. 5 — Compare outcomes (conditional)
  8. Consolidate (single subagent — MANDATORY)
  9. Catalog (you run this directly)

What it can do on your machine

Read from SKILL.md and the folder at commit 8deea81. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv
    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Wiki Ingest loads about 4k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 1,741 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from AgentToolkit/altk-evolve at commit 8deea81, republished under its Apache-2.0 licence (© AgentToolkit). 1,741 words, ~3,987 tokens.

Download SKILL.mdSave it as .claude/skills/agent-wiki-ingest/SKILL.md (or your agent's skills folder).
name
agent-wiki-ingest
description
Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, optionally compare outcomes, consolidate into clusters, and catalog. Use when you have a batch of traces to turn into a wiki in one pass.

Agent Wiki — Ingest (end-to-end orchestrator)

Overview

This is the one-pass entry point for turning a batch of raw trajectories into a fully-built wiki. It orchestrates the rest of the agent-wiki family in the right order so no pass is skipped — in particular the cross-trajectory consolidation pass, which is easy to forget when each skill is invoked by hand.

You — the driving agent — run this by spawning one subagent per (trace × pass), not by doing the work inline. That keeps your own context small (you never load every trace's full JSON) and lets independent passes run in parallel. Each subagent acts as the corresponding single-purpose skill (agent-wiki-summarize, -extract-guidelines, -synthesize-skill, -compare-outcomes, -consolidate-guidelines); this skill only sequences them and passes the per-trace adapter notes.

The pipeline:

0.  Convert    raw bob / claude traces → normalized analysis JSON   (skip if already normalized)
1.  Bootstrap  create wiki scaffold + seed catalog                  (skip if wiki exists)
1.5 Skip       drop traces whose summaries/<sid>.md already exists   [pre-flight — idempotency]
2.  Summarize  1 subagent / new-trace → summaries/<sid>.md          [PARALLEL]
3.  Extract    1 subagent / new-trace → guidelines/*.md (+tags)     [SEQUENTIAL]
4.  Synthesize 1 subagent / new-trace → skills/<slug>/ --archive-covered  [SEQUENTIAL]
4.5 Compare    success/failure contrasts → contrastive guidelines   [CONDITIONAL]
5.  Consolidate 1 subagent over the whole corpus → cluster pages    [SINGLE — MANDATORY]
6.  Catalog    final bookkeeping → indexes, used-by, priority       [you run this directly]

Idempotent by default. Re-running on the same source dir reprocesses nothing: Step 1.5 filters out every trace that already has a summary page, so Steps 2–4 only touch genuinely new traces. The consolidate + catalog tail always runs (it's cheap and self-idempotent). To force a redo of an already- ingested trace, keep it in the list and pass --rewrite to its render-* calls.

Why this order. synthesize-skill runs before consolidate-guidelines so skills claim recipe-level territory first (and archive the atomics they cover via --archive-covered); consolidation then clusters only the surviving atomics. This matches the consolidate skill's own rule — "don't propose clusters that overlap a skill's territory."

Why parallel vs sequential. Summarize writes one independent file per trace (summaries/<sid>.md) → safe to parallelize. Extract and synthesize both mutate shared state (guidelines/_id_index.json, skills/_id_index.json, _config.yaml, and the _archived/ moves) → run them one trace at a time to avoid lost-update races.

Input

One of:

  • a list of trace file paths
  • a directory of traces (the skill globs it)
  • already-normalized analysis JSON files

…plus a target --wiki-root (e.g. wiki-twobatch-skills).

Detecting trace shape (Step 0 dispatch)

Read the top-level JSON keys of each input to classify it:

ShapeSignatureConversion
bob session JSONtop-level sessionId + messagesbob-trace-converter
claude stream-jsonJSONL lines with {"type":"system"/"assistant"/"result"}normalize_stream_json_transcripts.py
normalized analysis JSONtop-level model + messages + metadata.idpass through (no conversion)

Step 0 — Convert

Write converted output under a stable corpus dir: trajectories/normalized/<label>/items/.

bob session JSON:

bash
NODE_OPTIONS='' node ~/.claude/skills/bob-trace-converter/scripts/convert_bob_trace.mjs \
  <trace.json> --out-dir trajectories/normalized/<label>/items --format both

The NODE_OPTIONS='' prefix is required — some shells inject a --require preload that breaks a bare node invocation. Strip it for this call.

The converter writes three files per trace; the ingest pipeline consumes the *-openai-chat-completions.analysis.json one.

claude stream-json:

bash
uv run python explorations/agent-wiki/experiments/harness/normalize_stream_json_transcripts.py \
  --in <transcripts-dir> --out trajectories/normalized \
  --label <label> --user-prompt "<the task prompt>"

Already normalized: skip — use the path as-is.

Collect the resulting list of analysis-JSON paths; this is the trace set the rest of the pipeline iterates.

Step 1 — Bootstrap the wiki

If <wiki-root>/_index.jsonl does not exist:

bash
mkdir -p <wiki-root>/{summaries,guidelines,tasks,skills}
uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py \
  --wiki-root <wiki-root> catalog

The first catalog seeds AGENTS.md and _config.yaml from the bundled defaults and writes empty indexes. Skip this whole step if the wiki already exists — you're appending to it.

Piping JSON to the helper — avoid echo

Every render-* subcommand reads JSON on stdin. The echo '<json>' | … form in the per-pass skills breaks when the payload has multi-line content/narrative fields (literal newlines become invalid control characters in the shell-quoted string). Tell every subagent to write its payload to a temp file and cat it instead:

bash
cat /tmp/ingest-payload.json | uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py --wiki-root <wiki-root> render-guidelines

Step 1.5 — Skip already-processed traces (pre-flight)

This is what makes re-running the skill on the same source dir cheap. The helper's render-* subcommands skip-if-exists, but only after a subagent has already read the trace and synthesized its output — so the LLM cost is already spent. Filter before spawning any subagent.

For each normalized trace, read its session_id — it lives at metadata.id (bob-converted analysis JSON) or top-level session_id (claude-normalized). If <wiki-root>/summaries/<sid>.md already exists, the trace was ingested on a prior run → drop it from the work-list. The surviving new-trace list is what Steps 2–4 iterate.

Compute the new-trace list and log what was skipped (never let a silent no-op masquerade as success):

bash
for f in <trace-glob>; do
  sid=$(uv run python -c "import json,sys; d=json.load(open(sys.argv[1])); print(d.get('session_id') or d.get('metadata',{}).get('id',''))" "$f")
  if [ -n "$sid" ] && [ -f "<wiki-root>/summaries/$sid.md" ]; then
    echo "skip (already ingested): $sid  $f"
  else
    echo "NEW: $sid  $f"
  fi
done

The NEW: lines are the work-list for Steps 2/3/4. If every trace is skipped, that's fine — jump straight to Steps 5–6 (the tail always runs).

Override. To force reprocessing of an already-ingested trace, keep it in the work-list and pass --rewrite to its render-* calls (the helper overwrites instead of skipping).

Step 2 — Summarize (parallel subagents)

Spawn one subagent per new-trace (from Step 1.5's work-list), all in parallel. Each acts as agent-wiki-summarize (point it at that skill's SKILL.md). In each subagent prompt include:

  • the analysis-JSON path and the --wiki-root
  • the trace's agent (bob, claude-code, …) — it must set agent: accordingly, not hardcode claude-code
  • the bob field-mapping adapter notes (only if the trace came from bob): session_id ← metadata.id; model ← top-level model; tool calls live in messages[i].content[j] blocks with type: "tool_use"; transcript_path ← metadata.source_file; recalled_guidelines is empty for a freshly-built wiki
  • do NOT run catalog — the orchestrator runs it once at the end

Each subagent pipes its summary JSON to:

bash
echo '<json>' | uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py --wiki-root <wiki-root> render-summary

Step 3 — Extract guidelines (sequential subagents)

Spawn one subagent per new-trace, one at a time (wait for each before starting the next — they share guidelines/_id_index.json and _config.yaml). Each acts as agent-wiki-extract-guidelines. In each prompt:

  • the analysis-JSON path, --wiki-root, agent, and bob adapter notes
  • the list of existing guideline slugs (from prior traces this run) so it suppresses near-duplicates
  • instruct it to attach a tags: array to every entity (these now propagate to both the .md frontmatter and _config.yaml — see commit that fixed render-guidelines)
  • instruct it to set "agent": "<source>" on every entity. The extract-guidelines entity schema does not list agent as a field, so the subagent must add it explicitly; otherwise the page defaults to agent: claude-code even for bob traces.
  • skip arc: for single-summary sessions
  • do NOT run catalog

Pipe via:

bash
echo '<json>' | uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py --wiki-root <wiki-root> render-guidelines

Step 4 — Synthesize skills (sequential subagents)

Spawn one subagent per new-trace, one at a time (shared skills/_id_index.json plus _archived/ moves). Each acts as agent-wiki-synthesize-skill. In each prompt:

  • the analysis-JSON path, --wiki-root, agent, bob adapter notes
  • the list of existing skill slugs so it doesn't re-author one
  • tell it to decide promote-vs-skip per that skill's "When To Use" rubric (trivial single-command recipes → skip and emit nothing)
  • when promoting, pipe with --archive-covered so the atomics the skill subsumes are soft-archived:
    bash
    cat /tmp/skill-payload.json | uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py --wiki-root <wiki-root> render-skill --archive-covered
    --archive-covered is safe to run blind: the matcher only archives an atomic from another trajectory when the skill's tags are a true superset (≥2 non-generic shared tags). The weak lexical heuristics (a slug word or format token appearing in the atomic's title) fire only for atomics from the same trajectory this skill was synthesized from, so a skill can no longer reach across into an unrelated trace's atomic on a coincidental word like "python" or "csv".
  • do NOT run catalog
Show full SKILL.md (696 more words)Show less

Step 4.5 — Compare outcomes (conditional)

Run this step when the corpus has multiple trajectories that can be judged as successes and failures for the same or similar task — benchmark corpora, repeated attempts, A/B experiment arms, or user-labeled sessions. Skip it when there is no success/failure contrast: a corpus of only apparent successes still produces summaries, atomics, skills, and clusters, but it cannot safely derive contrastive rules.

Spawn one subagent acting as agent-wiki-compare-outcomes over the whole corpus (point it at that skill's SKILL.md). It derives contrastive guidelines — rules backed by a failed path, a successful path, and concrete trajectory evidence — rather than mining from one trajectory alone. It does not depend on benchmark-specific outcome labels; it can LLM-judge success or failure from the normalized transcript. In its prompt:

  • the --wiki-root and the trace agent: value (so promoted contrastive guidelines are stamped with the right source, not defaulted to claude-code)
  • build the evidence pack:
    bash
    uv run python explorations/agent-wiki/skills/agent-wiki-compare-outcomes/scripts/compare_outcomes.py \
      --input <normalized-dir-or-json> \
      --out-json /tmp/agent-wiki-outcome-comparison.json \
      --out-md <wiki-root>/tasks/outcome-comparison.md \
      --judge-outcomes always
    Prefer --judge-outcomes always when stored labels come from a benchmark evaluator or other dataset-specific schema; use --judge-outcomes missing only for trusted, dataset-neutral labels.
  • instruct it to promote only strong candidates (one failed + one successful run in the same group, a task-action tool/API or workflow difference, source trajectory IDs for both sides) per that skill's "Inspect" and "Promote Carefully" rules — keep weak ones as hypotheses, not rules.
  • pipe promoted entities through the helper (avoid echo; use a temp file):
    bash
    cat /tmp/contrastive-guidelines.json | uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py --wiki-root <wiki-root> render-guidelines
  • do NOT run catalog — the orchestrator runs it once at the end

Run this after synthesize and before consolidate, so the new contrastive guidelines can participate in clusters.

Step 5 — Consolidate (single subagent — MANDATORY)

This pass is not optional. It is the step most easily forgotten when the family is invoked by hand, and it is the whole reason this orchestrator exists. Always run it, even on a small corpus — the subagent's own judgment returns zero clusters when nothing qualifies, which is the correct outcome for a tiny or heterogeneous corpus.

Run it even when Step 1.5 skipped every trace. A re-run that ingests no new traces still benefits from a consolidation pass over the existing corpus — it can form clusters that an earlier run missed. Steps 5 and 6 are the always-on tail; only Steps 2–4 are gated on the new-trace list.

Spawn one subagent acting as agent-wiki-consolidate-guidelines over the whole surviving-atomic corpus. In its prompt:

  • the --wiki-root
  • instruct it to run dump-guidelines first, then propose clusters
  • remind it: a cluster needs ≥2 atomic members sharing a real rule (not just a topic); don't propose clusters overlapping a skill's territory (the skill is already the canonical aggregator)
  • do NOT run catalog — the orchestrator runs it next

Each cluster is piped via:

bash
echo '<json>' | uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py --wiki-root <wiki-root> render-cluster

Step 6 — Catalog (you run this directly)

One final bookkeeping pass:

bash
uv run python explorations/agent-wiki/skills/scripts/build_agent_wiki.py --wiki-root <wiki-root> catalog

This regenerates _index.jsonl, the section indexes, the priority table, the "By tag" and used-by sections, and propagates cluster: / superseded_by: backrefs onto clustered atomics.

Report

After Step 6, report the end-state counts: summaries, surviving atomics, clusters, skills, archived atomics — and call out any trace that produced no guidelines or no skill (trivial recipes), plus any cluster proposals that were considered and rejected.

Best practices

  1. Consolidation is mandatory. Step 5 always runs. The cluster subagent self-skips individual clusters; the pass never skips.
  2. One subagent per (trace × pass). Don't batch multiple traces into one subagent — it bloats context and muddies provenance.
  3. Parallel only for summarize. Extract, synthesize, and consolidate all touch shared index/config state — keep them sequential.
  4. Subagents never catalog. Only the orchestrator does, once, at the end. A mid-run catalog wastes work and can race with in-flight writes.
  5. Pass agent: through. Bob traces are bob, not claude-code. The summarize and extract subagents must stamp the right source.
  6. Tags on every guideline. They drive the "By tag" index and future cluster formation; an untagged atomic is invisible to tag-based recall.
  7. Idempotent by default. Step 1.5 skips any trace that already has a summaries/<sid>.md, so re-running on the same source dir reprocesses nothing. Use --rewrite on the render-* calls to force a redo of a specific trace.

© AgentToolkit, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in explorations/agent-wiki/skills/agent-wiki-ingest of AgentToolkit/altk-evolve.

Open the folder on GitHubat commit 8deea81

Compare with similar skills

Agent Wiki Ingest next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Wiki Ingest compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Wiki Ingest this skillAgentToolkit/altk-evolve122—~4kAutomated safety check: PassApache-2.0
Karpathy LLM WikiAstro-Han/karpathy-llm-wiki2.4k—~3.6kAutomated safety check: PassMIT
LLM Wiki Knowledge GraphEgonex-AI/Understand-Anything86k1 repos~1.5kAutomated safety check: PassMIT
Hermes History IngestAr9av/obsidian-wiki3.5k1 repos~2.2kAutomated safety check: NotesMIT
LLM Wikilewislulu/llm-wiki-skill655—~3.7kAutomated safety check: PassNone
Wiki Builderrohitg00/pro-workflow2.9k—~1kAutomated safety check: PassNone

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  • Agent Wiki Guideline Extractor

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Questions about Agent Wiki Ingest

What does Agent Wiki Ingest do?

Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, optionally compare…. Agent Wiki Ingest is an agent skill from AgentToolkit/altk-evolve. Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, optionally compare outcomes, consolidate into clusters, and catalog.

When should I use Agent Wiki Ingest?

Agent Wiki Ingest fits situations like: you have a batch of traces to turn into a wiki in one pass; tasks that involve LLM wikis; tasks that involve End-to-end testing.

How do I install Agent Wiki Ingest in Claude Code?

Run `npx skills add AgentToolkit/altk-evolve --skill agent-wiki-ingest -a claude-code`. Or copy the skill folder (explorations/agent-wiki/skills/agent-wiki-ingest in AgentToolkit/altk-evolve) into .claude/skills/agent-wiki-ingest in your project. Claude Code loads it when a task matches its description.

How do I install Agent Wiki Ingest in Codex?

Run `npx skills add AgentToolkit/altk-evolve --skill agent-wiki-ingest -a codex`. Or copy the skill folder (explorations/agent-wiki/skills/agent-wiki-ingest in AgentToolkit/altk-evolve) into .agents/skills/agent-wiki-ingest in your project. Codex loads it when a task matches its description.

Can I use Agent Wiki Ingest in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AgentToolkit/altk-evolve --skill agent-wiki-ingest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-wiki-ingest, .gemini/skills/agent-wiki-ingest, .github/skills/agent-wiki-ingest and .opencode/skills/agent-wiki-ingest in your project.

What does Agent Wiki Ingest need to run?

Going by SKILL.md and its folder, Agent Wiki Ingest needs the command-line tools its instructions call (uv and node). Our summary lists: Python 3.

Does Agent Wiki Ingest access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Agent Wiki Ingest safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Wiki Ingest use?

Agent Wiki Ingest is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Wiki Ingest use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Wiki Ingest?

Skills that share tags, products or a category with Agent Wiki Ingest: Karpathy LLM Wiki (Astro-Han/karpathy-llm-wiki, 2.4k stars), LLM Wiki Knowledge Graph (Egonex-AI/Understand-Anything, 86k stars), Hermes History Ingest (Ar9av/obsidian-wiki, 3.5k stars) and LLM Wiki (lewislulu/llm-wiki-skill, 655 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Wiki Ingest?

AgentToolkit (a GitHub organization) maintains it in AgentToolkit/altk-evolve, which has 122 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on October 8, 2026.

Source: AgentToolkit/altk-evolve on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.