SKILLEMALL.ai

AC session-recovery

Recover lost agent session content and file changes from on-disk conversation logs. Streaming and OOM-safe on 700MB+ daily JSONL. Two commands: search.py for keyword search across recent sessions (with hit snippets, file-op listing, JSON for agent consumption); extract.py for pulling full write/edit content from a single session by ID prefix, with optional replay-rebuild for pure-edit sequences and safe restore-to-disk (refuses silent overwrites without --yes). Multi-agent aware via --agent main|all|a,b. Configurable data root via --root flag or SESSION_RECOVERY_ROOT env var (default ~/.openclaw/agents/). Trigger when user wants to find lost session content, recover files written by an agent, locate which session modified a file, search session history by keyword, or rebuild a file from an edit replay. Also triggers on 找回会话, 会话被覆盖, 历史会话搜索, 文件被删了, session 丢了, 找回某个文件, 重放编辑.

ClawHub Agent Skills author: Evan Song v1.0.1 MIT-0 6 files body ≈ 988 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 988 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 884: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -31 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

    External checks

    ClawHub: clean
    This skill is a disclosed local recovery tool for searching OpenClaw session logs and restoring agent-written files, with no network behavior found.
    LLM: benign (high) · VirusTotal: · 20 Jun 2026